WO2022205242A1

WO2022205242A1 - Vehicle control method and device

Info

Publication number: WO2022205242A1
Application number: PCT/CN2021/084771
Authority: WO
Inventors: 朱盈璇; 童传伟; 张路刚
Original assignee: 华为技术有限公司
Priority date: 2021-03-31
Filing date: 2021-03-31
Publication date: 2022-10-06
Also published as: CN112955359B; CN115675468A; CN112955359A

Abstract

A vehicle control method, comprising: obtaining running information of a vehicle and road traffic information of an area where the vehicle is located, and predicting an operating condition of the vehicle passing a traffic light intersection and a first vehicle speed of the vehicle passing the traffic light intersection; if the operating condition of the vehicle passing the traffic light intersection is a braking operating condition, determining, according to the first vehicle speed and a second vehicle speed at which the vehicle is currently running, braking energy recovered by the vehicle; and controlling, according to the braking energy recovered by the vehicle, the vehicle to brake. Also disclosed are a vehicle control device, a communication device, a readable storage medium, an autonomous vehicle, and an autonomous driving assistance system.

Description

A vehicle control method and device

technical field

The present application relates to the technical field of smart cars, and in particular, to a vehicle control method and device.

Background technique

With the continuous development of technologies such as autonomous driving, vehicle-road collaboration, and vehicle-cloud collaboration, vehicles can obtain perception information around the vehicle through on-board sensors, roadside devices, and cloud servers. In the urban traffic environment, traffic light intersections are one of the working conditions faced by vehicles. The perception of traffic lights by on-board sensors of vehicles is easily affected by factors such as illumination, occlusion, and distance, and the description information of traffic lights cannot be obtained.

With the development of technologies such as vehicle-road collaboration and vehicle-cloud collaboration, vehicles can obtain more comprehensive and accurate perception information through roadside devices or cloud servers. The distance of the traffic light intersection, the driving of the vehicle is planned in advance, so that the vehicle can pass the traffic light intersection safely and smoothly.

At present, the speed control methods at traffic light intersections based on vehicle-road coordination are mostly aimed at fuel vehicles, but no solution has been given for the control scenarios of vehicles powered by power sources.

SUMMARY OF THE INVENTION

Embodiments of the present application provide a vehicle control method and device, which are used to control the vehicle, recover as much braking energy as possible under braking conditions, improve the energy utilization rate of the vehicle, and extend the cruising range of the vehicle.

In a first aspect, a vehicle control method is provided, comprising: acquiring driving information of a vehicle and road traffic information of an area where the vehicle is located, predicting the working condition of the vehicle passing through a traffic light intersection and a first speed of the vehicle passing through the traffic light intersection v _f ; if the working condition of the vehicle passing through the traffic light intersection is the braking condition, according to the first vehicle speed v _f and the second vehicle speed v(t) that the vehicle is currently traveling, determine the braking of the vehicle recovery energy; controlling the vehicle to perform braking according to the braking energy recovered by the vehicle.

Wherein, the vehicle may be a pure electric vehicle, or may be a gasoline-electric hybrid vehicle, or may be another vehicle with an energy storage device, which is not limited herein.

Through the above method, when planning and controlling the vehicle speed, the factor of braking energy recovery is considered under braking conditions, and as much braking energy is recovered as possible, the energy utilization rate of the vehicle is improved, and the cruising range of the vehicle is prolonged.

In a possible design, when obtaining the driving information of the vehicle and the road traffic information of the area where the vehicle is located, and predicting the working condition of the vehicle passing through the traffic light intersection and the first speed v _f of the vehicle passing through the traffic light intersection, it is possible to According to the driving information of the vehicle and the road traffic information of the area where the vehicle is located, a vehicle control model is constructed; based on the vehicle control model, the working conditions of the vehicle passing through the traffic light intersection and the first time when the vehicle passes the traffic light intersection are predicted. vehicle speed v _f . By building a vehicle control model, a more accurate planning control of the vehicle's speed can be performed.

In a possible design, the vehicle control model includes one or more of the following: a vehicle dynamics model, a physical constraint model, a boundary constraint model, and an optimization objective of the vehicle control model. Considering different models and optimization objectives in vehicle control can improve passenger comfort and safety, as well as improve the overall traffic efficiency of the road.

The physical constraint model includes a speed constraint model of the vehicle, and/or an acceleration constraint model of the vehicle.

The boundary constraint model is used to constrain the vehicle not to collide with a vehicle in front of the vehicle.

The optimization objectives of the vehicle model include one or more of the following: an efficiency evaluation index of the vehicle passing through a traffic light intersection, a safety evaluation index, a comfort evaluation index, and a vehicle energy recovery index.

In a possible design, when acquiring the driving information of the vehicle and the road traffic information of the area where the vehicle is located, the driving information collected by the collection module of the vehicle may be acquired, and the roadside device or the cloud server may acquire all the driving information. The road traffic information in the area where the vehicle is located. In this way, more accurate, real-time and reliable road condition information can be obtained, the perception range of the vehicle can be improved, and the perception capability of the vehicle can be enhanced.

In a possible design, when the working condition is a braking condition, the optimization objective of the vehicle model includes the vehicle energy recovery index. By considering the vehicle energy recovery index, the energy utilization rate of the vehicle can be improved, and the cruising range of the vehicle can be extended.

In a possible design, when the working condition is the braking condition, the braking acceleration a(t) of the vehicle satisfies the following conditions: a _min ≤a _z ≤a(t)≤a _max , where a _min is the The minimum acceleration of the vehicle, a _max is the maximum acceleration of the vehicle, and a _z is the acceleration related to the braking strength Z of the vehicle. When the braking acceleration of the vehicle is greater than _az and the vehicle is under non-emergency braking, under the braking condition, the emergency braking of the vehicle is avoided, and the braking energy can be recovered as much as possible.

In one possible design, the optimization objective of the vehicle control model is related to one or more of the following information: the speed of the vehicle, the position of the vehicle, the acceleration of the vehicle, or the speed of the vehicle through a traffic light intersection. time.

For example, the efficiency evaluation index of a vehicle passing through a traffic light intersection may be related to the speed of the vehicle. The safety evaluation index of a vehicle passing through a traffic light intersection can be related to the speed of the vehicle and the position of the vehicle. The comfort evaluation index of a vehicle passing through a traffic light intersection can be related to the acceleration of the vehicle. The vehicle energy recovery index may be related to the time when the vehicle passes through the traffic light intersection and the speed of the vehicle. It can be seen that the influence of different factors on the optimization objective is considered in the vehicle control, and the moment when the vehicle passes through the traffic light intersection.

In one possible design, the optimization objective of the vehicle model satisfies the following formula:

J is the optimization target of the vehicle model, t ₀ is the initial moment of vehicle control, t _f is the moment when the vehicle passes the traffic light intersection, v is the speed of the vehicle, x is the position of the vehicle, a is the the acceleration of the vehicle.

In a possible design, the G(v(t _f ),x(t _f ),t _f ) is related to one or more of the following information: the time when the vehicle passes through a traffic light intersection, the speed of the vehicle.

In a possible design, when the working condition is the braking condition, the G(v(t _f ),x(t _f ),t _f ) satisfies the following formula: G(v(t _f ),x( t _f ),t _f )=ω _time G _time -ω _SOC G _SOC .

When the working condition is a non-braking condition, the G(v(t _f ),x(t _f ),t _f ) satisfies the following formula: G(v(t _f ),x(t _f ),t _f )=ω _time G _time .

Wherein, ω _time G _time is the traffic efficiency evaluation index of the vehicle passing through the traffic light intersection, the G _time is related to the time when the vehicle passes the traffic light intersection, ω _SOC G _SOC is the braking energy recovery index, and the G _SOC is related to the time when the vehicle passes the traffic light intersection. the speed of the vehicle.

In this design, different optimization objectives are considered in the vehicle control, which can improve the comfort and safety of passengers, as well as improve the overall traffic efficiency of the road.

In one possible design, the G _SOC satisfies the following formula: G _SOC =(1/2mv _f ² -1/2mv ₀ ² )-W _a -W _f , where m is the mass of the vehicle and W _a is The energy of air resistance, W _f is the energy of rolling resistance.

In a possible design, the G _time satisfies the following formula: G _time =1/2t _f ² , where t _f is the moment when the vehicle passes the traffic light.

In a possible design, the efficiency evaluation index of the vehicle passing through the traffic light intersection is related to the speed of the vehicle.

In a possible design, the efficiency evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: L _v =(v(t)-v _f ) ² , where L _v is the efficiency evaluation index of the vehicle passing through the traffic light intersection.

In a possible design, the safety evaluation index of the vehicle passing through the traffic light intersection is related to the speed of the vehicle and the position of the vehicle speed.

In a possible design, the safety evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: L _safe =1-TCC(t)/TCC _max , where L _safe is the safety evaluation index of the vehicle passing through the traffic light intersection, TCC (t) is the collision time between the vehicle and the vehicle in front of the vehicle, and TCC _max is the maximum collision time between the vehicle and the vehicle in front of the vehicle.

In one possible design, the boundary constraint model is related to the speed of the vehicle and the location of the vehicle speed. Optionally, the boundary constraint model is related to one or more of the following information: the speed of the vehicle, the speed of the vehicle in front of the vehicle, or the distance between the vehicle and the vehicle in front of the vehicle.

In one possible design, the boundary constraint model satisfies the following formula:

where d _other is the distance between the vehicle and the vehicle in front of the vehicle, and v _other is the speed of the vehicle in front of the vehicle.

In a possible design, the comfort evaluation index of the vehicle passing through the traffic light intersection is related to the acceleration of the vehicle.

In a possible design, the comfort evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: _Lsoft =a(t) ² , _Lsoft is the comfort evaluation index of the vehicle passing through the traffic light intersection, a(t) is the acceleration of the vehicle at time t.

In this design, different optimization objectives are considered in vehicle control, which can improve passenger comfort and safety, as well as improve the overall traffic efficiency of the road.

In one possible design, the vehicle dynamics model is related to the speed of the vehicle.

In one possible design, the vehicle dynamics model satisfies the following formula:

where F _t is the driving force of the vehicle,

is the slope resistance of the road,

is the rolling friction force, μ is the road friction coefficient, 1/2C _D ρ _a Av(t) ² is the wind resistance, _CD is the air resistance coefficient, ρ _a is the air density, and A is the windward area of the vehicle.

In a possible design, the driving information of the vehicle includes one or more of the following: the second vehicle speed v(t) of the vehicle currently traveling, the acceleration a(t) of the vehicle currently traveling, the vehicle The current driving position.

In a possible situation, at the initial moment of vehicle control, the current speed of the vehicle may be the initial speed v ₀ , and the current acceleration of the vehicle may be the initial acceleration a ₀ . That is, in this case, v(t)=v ₀ and a(t)=a ₀ .

The road traffic information of the area where the vehicle is located includes one or more of the following: the color of the traffic light, the number of seconds in the traffic light, the distance between the vehicle and the traffic light, the speed limit in the area where the vehicle is located, the speed of the vehicle ahead of the vehicle, The distance between the vehicle and the vehicle in front of the vehicle.

In this design, more comprehensive and accurate vehicle driving information and road condition information can be obtained, the perception range of the vehicle can be improved, the perception capability of the vehicle can be enhanced, and the vehicle speed can be better planned and controlled.

In a second aspect, a vehicle control method is provided, comprising: acquiring driving information of a vehicle and road traffic information of an area where the vehicle is located, predicting a working condition of the vehicle passing through a traffic light intersection and a first speed of the vehicle passing through the traffic light intersection v _f ; if the working condition of the vehicle passing through the traffic light intersection is a non-braking condition, control the vehicle to pass through the traffic light intersection at the first vehicle speed v _f .

Among them, the non-braking conditions include accelerating through the traffic light intersection, or passing through the traffic light intersection at a constant speed.

Among them, passing through the traffic light intersection at a constant speed may be for the control vehicle to pass through the traffic light intersection at the first speed v _f . Optionally, the first vehicle speed v _f may be equal to the second vehicle speed v(t) at which the vehicle is currently traveling.

In a possible design, when obtaining the driving information of the vehicle and the road traffic information of the area where the vehicle is located, and predicting the working condition of the vehicle passing through the traffic light intersection and the first speed v _f of the vehicle passing through the traffic light intersection, it is possible to According to the driving information of the vehicle and the road traffic information of the area where the vehicle is located, a vehicle control model is constructed; based on the vehicle control model, the working conditions of the vehicle passing through the traffic light intersection and the first time when the vehicle passes the traffic light intersection are predicted. vehicle speed v _f .

In a possible design, the vehicle control model includes one or more of the following: a vehicle dynamics model, a physical constraint model, a boundary constraint model, and an optimization objective of the vehicle control model.

Under non-braking conditions, the optimization objective of the vehicle model may not include the vehicle energy recovery index.

In a possible design, when acquiring the driving information of the vehicle and the road traffic information of the area where the vehicle is located, the driving information collected by the collection module of the vehicle may be acquired, and the roadside device or the cloud server may acquire all the driving information. The road traffic information in the area where the vehicle is located.

In a possible design, when the working condition is a non-braking condition, the G(v(t _f ),x(t _f ),t _f ) satisfies the following formula: G(v(t _f ),x (t _f ),t _f )=ω _time G _time .

Wherein, ω _time G _time is the traffic efficiency evaluation index of the vehicle passing through the traffic light intersection, and the G _time is related to the time when the vehicle passes the traffic light intersection.

The safety evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: L _safe =1-TCC(t)/TCC _max , where L _safe is the safety evaluation index of the vehicle passing through the traffic light intersection, and TCC(t) is the vehicle The collision time with the vehicle in front of the vehicle, TCC _max is the maximum collision time between the vehicle and the vehicle in front of the vehicle.

In one possible design, the boundary constraint model is related to one or more of the following information: the speed of the vehicle, the speed of the vehicle in front of the vehicle, or the distance between the vehicle and the vehicle in front of the vehicle.

where F _t is the driving force of the vehicle,

is the slope resistance of the road,

In a third aspect, a vehicle control method is provided, comprising: acquiring driving information of a vehicle and road traffic information of an area where the vehicle is located, predicting the working condition of the vehicle passing through a traffic light intersection and a first speed of the vehicle passing through the traffic light intersection v _f . If the working condition of the vehicle passing through the traffic light intersection is the braking condition, the braking energy recovered by the vehicle is determined according to the first vehicle speed v _f and the second vehicle speed v(t) the vehicle is currently driving; The braking energy recovered by the vehicle controls the vehicle to perform braking. If the working condition of the vehicle passing through the traffic light intersection is a non-braking condition, the vehicle is controlled to pass through the traffic light intersection at the first vehicle speed v _f .

In a fourth aspect, a vehicle control device is provided, the vehicle control device having a function of implementing the vehicle control method of the first aspect, the second aspect or the third aspect. The functions can be implemented by hardware, or can be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions.

In a possible design, the structure of the vehicle control device includes an acquisition unit and a processing unit, and these units can perform the corresponding functions in the method examples of the first aspect, the second aspect or the third aspect. For details, please refer to the method examples. The detailed description is not repeated here.

In one possible design, the structure of the vehicle control device includes a processor and a memory. The processor is configured to support the vehicle control device to perform the corresponding functions in the method of the first aspect, the second aspect or the third aspect above. The memory is coupled to the processor and holds program instructions and data necessary for the target distance determination device. The processor is configured to read and execute the program instructions stored in the memory, and execute the method mentioned in any possible design of the first aspect, the second aspect or the third aspect.

In a fifth aspect, an embodiment of the present application further provides an automatic driving vehicle, and the automatic driving vehicle may include the vehicle control device mentioned in the fourth aspect above.

In a sixth aspect, an embodiment of the present application further provides an automatic driving assistance system, and the automatic driving assistance system may include the vehicle control device mentioned in the fourth aspect above.

In a seventh aspect, embodiments of the present application further provide a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions, when called by the computer, are used to make The computer executes the method mentioned in the first aspect, the second aspect, the third aspect, or any possible designs of the first aspect, the second aspect, and the third aspect. Illustratively, a computer-readable storage medium can be any available medium that can be accessed by a computer. Taking this as an example but not limited to: computer readable media may include non-transitory computer readable media, random-access memory (RAM), read-only memory (ROM), electrically erasable Except programmable read only memory (electrically EPROM, EEPROM), CD-ROM or other optical disk storage, magnetic disk storage medium or other magnetic storage device, or capable of carrying or storing desired program code in the form of instructions or data structures and capable of Any other media accessed by a computer.

In an eighth aspect, the embodiments of the present application further provide a computer program product including instructions, which, when run on a computer, causes the computer to execute the first aspect, the second aspect, the third aspect, or the first aspect . The method mentioned in any possible design of the second aspect.

In a ninth aspect, an embodiment of the present application further provides a chip, the chip is connected to a memory, and is used for reading and executing program instructions stored in the memory, so as to realize the above-mentioned first aspect, the second aspect, or the third aspect. The method mentioned in any possible design of the first aspect, the second aspect, and the third aspect.

For each aspect of the above-mentioned second aspect to the ninth aspect and possible technical effects achieved by each aspect, please refer to the above description of the technical effect achieved by the first aspect or various possible solutions in the first aspect, which will not be repeated here.

Description of drawings

FIG. 1 is a flowchart of a vehicle control method provided by an embodiment of the present application;

FIG. 2 is a flowchart of another vehicle control method provided by an embodiment of the present application;

3 is a flowchart of another vehicle control method provided by an embodiment of the present application;

4 is a block diagram of a vehicle control method provided by an embodiment of the present application;

FIG. 5 is a flowchart of another vehicle control method provided by an embodiment of the present application;

FIG. 6 is a schematic structural diagram of a communication device according to an embodiment of the present application;

FIG. 7 is a schematic structural diagram of a communication apparatus according to an embodiment of the present application.

Detailed ways

The present application will be described in further detail below with reference to the accompanying drawings.

The technical solutions of the embodiments of the present application can be applied to various communication systems, for example: a fourth generation (4th Generation, 4G) system, a 4G system including an LTE system, a worldwide interoperability for microwave access (WiMAX) communication system , 5th Generation (5G) systems, such as NR, 6G systems, and future communication systems.

The technical solutions of the embodiments of the present application can be applied to unmanned driving (unmanned driving), driver assistance (ADAS), intelligent driving (intelligent driving), connected driving (connected driving), and intelligent network driving (Intelligent network driving). ), car sharing (car sharing), smart car (smart/intelligent car), digital car (digital car), unmanned car (unmanned car/driverless car/pilotless car/automobile), internet of vehicles (IoV) , autonomous car (self-driving car, autonomous car), vehicle-road coordination (cooperative vehicle infrastructure, CVIS), intelligent transportation (intelligent transport system, ITS), vehicle communication (vehicular communication) and other technical fields.

Some terms in the embodiments of the present application are explained below to facilitate understanding by those skilled in the art.

1) A battery electric vehicle (BEV), also known as an electric vehicle, is powered by the on-board power supply and drives the wheels with a motor. That is to say, the power source of the pure electric vehicle can provide electric energy, and the electric motor of the pure electric vehicle can convert the electric energy of the power source into mechanical energy to drive the wheels. According to different uses, pure electric vehicles can include electric cars, electric vans and electric buses.

In the embodiments of the present application, the vehicle is a pure electric vehicle as an example for description. It should be noted that the vehicle control method provided in the embodiments of the present application is applicable to vehicles with energy storage devices, for example, including other vehicles powered by power sources, or hybrid vehicles with gasoline and electricity.

2) Braking energy recovery, using the vehicle to convert the braking efficiency into electrical energy storage and recycling it into the battery during braking and deceleration, which is equivalent to expanding the capacity of the vehicle's battery and increasing the vehicle's cruising range. In addition, braking energy recovery can also reduce vehicle wear and improve vehicle driving stability.

3) Roadside equipment, including roadside unit (RSU), roadside intelligent facilities (including cameras, millimeter-wave radar, a small amount of lidar, environmental perception equipment, and intelligent traffic lights, intelligent signs, etc.), etc. The roadside device may also include a multi-access edge computing (multi-access edge computing, MEC) device and the like.

The roadside device can acquire the position and speed information of vehicles in the area where the roadside device is located, and can also detect the traffic flow in the area where the roadside device is located. The roadside equipment (such as RSU) can be connected to the traffic lights (also called traffic lights or signal lights) in the area where it is located to obtain the color and seconds of the traffic lights (usually countdown seconds). The roadside equipment (such as RSU) can be connected to the camera/lidar in its area to detect whether there are abnormal conditions on the road (traffic accident, foggy weather, etc.). Optionally, the roadside device may also undertake a part of data processing and computing functions.

The roadside device can interact with the vehicle, for example, the vehicle can report driving information to the roadside device, and the roadside device can deliver the road traffic information in the area where the vehicle is located to the vehicle.

4) Cloud server, namely cloud management platform or intelligent vehicle cloud service platform, also known as cloud equipment, can analyze and process the information of massive vehicles, so as to plan the driving route, speed and cycle of signal lights. The cloud server can interact with roadside equipment and vehicles. For example, the vehicle can report driving information to the cloud server, and the cloud server can issue the planned driving route and vehicle speed to the vehicle. Alternatively, the cloud server may directly issue the road traffic information in the area where the vehicle is located to the vehicle. In one possibility, the cloud server is a traffic center.

5) The driving information of the vehicle, the information related to the driving process of the vehicle, including but not limited to one or more of the following: the speed of the vehicle/speed of the vehicle (that is, the speed of the vehicle), the acceleration of the vehicle/the acceleration of the vehicle, Or where the vehicle is driving/where the vehicle is. Optionally, the driving information of the vehicle may be collected by a collection module such as an on-board sensor and a camera of the vehicle itself. Or the driving information of the vehicle may be collected by roadside equipment in the area where the vehicle is located. In some scenarios, the vehicle may also collect and report road traffic information in the area where it is located, for example, may collect and report traffic light information and/or abnormal conditions in the area where it is located.

In the embodiment of the present application, the speed of the vehicle may include: v ₀ , v(t), v _f or v _target . Assuming that a vehicle control is performed in the time period from t ₀ to t _f (t ₀ is the initial time of vehicle control, t _f is the end time of vehicle speed control), the vehicle speed of the vehicle at time t ₀ is v ₀ , and the The speed of the vehicle at time t (generally the current speed) is v(t), the speed of the vehicle at time t _f is v _f , and the speed of the vehicle passing through the traffic light intersection is v _target . In some possible situations, for example, at the initial time t ₀ of vehicle control, the vehicle is currently traveling at a speed v(t)=v ₀ , and, for example, at the end time t _f of vehicle control, the vehicle is currently traveling The vehicle speed v(t)=v _f , and if the vehicle passes through the traffic light intersection at the end time t _f of vehicle control, then v _f =v _target . In the embodiment of the present application, the process of vehicle control is mainly a process of performing vehicle speed planning control on the vehicle.

The acceleration of the vehicle may include: a ₀ or a(t). Assuming that a vehicle control is performed in the time period from t ₀ to t _f , the acceleration of the vehicle at time t ₀ is a ₀ , and the acceleration of the vehicle at time t (generally refers to the acceleration of the current driving) is a(t) . In some possible cases, for example, at the initial time t ₀ of vehicle control, the acceleration a(t)=a ₀ of the vehicle is currently traveling.

It should be noted that, within the time period that the vehicle travels from a certain position to the traffic light intersection, one or more vehicle controls may be performed, and may include one or more time periods from t ₀ to t _f . In the embodiment of the present application, a vehicle control description is mainly performed within the time duration of the vehicle traveling from a certain position to the traffic light intersection, that is, t _f is the time for the vehicle to pass through the traffic light intersection, and v _f =v _target .

It can be understood that, in the embodiments of the present application, unless otherwise specified, the concepts of "time" and "moment" can be replaced with each other.

6) Road traffic information, refers to the road traffic information of the area where the vehicle is located, including but not limited to one or more of the following: the color of the traffic light, the number of seconds in the traffic light, the distance between the vehicle and the traffic light, the speed limit in the area where the vehicle is located, The speed of the vehicle in front of the vehicle, the distance between the vehicle and the vehicle in front of the vehicle, the traffic flow in the area where the vehicle is located, the weather information in the area where the vehicle is located, and the congestion in the area where the vehicle is located.

It can be understood that, unless otherwise specified, the vehicles involved in the embodiments of the present application are smart vehicles, which can interact with roadside devices, cloud servers, and the like.

7) The terms "system" and "network" in the embodiments of this application may be used interchangeably. "Plurality" refers to two or more than two, and in view of this, "plurality" may also be understood as "at least two" in the embodiments of the present application. "At least one" can be understood as one or more, such as one, two or more. For example, including at least one means including one, two or more, and does not limit which ones are included. For example, if at least one of A, B, and C is included, then A, B, C, A and B, A and C, B and C, or A and B and C may be included. Similarly, the understanding of descriptions such as "at least one" is similar. "And/or", which describes the association relationship of the associated objects, means that there can be three kinds of relationships, for example, A and/or B, which can mean that A exists alone, A and B exist at the same time, and B exists alone. In addition, the character "/", unless otherwise specified, generally indicates that the related objects are an "or" relationship.

Unless stated to the contrary, ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, sequence, priority, or importance of multiple objects. Moreover, the description of "first" and "second" does not limit the objects to be necessarily different.

With the continuous development of technologies such as autonomous driving, vehicle-road collaboration (collaboration between vehicles and roadside equipment), and vehicle-cloud collaboration (collaboration between vehicles and cloud servers), vehicles (such as smart cars) can The device and cloud server obtain more comprehensive perception information around the vehicle. In the urban traffic environment, traffic light intersections are one of the working conditions faced by vehicles. The perception of traffic lights by on-board sensors of vehicles is easily affected by factors such as illumination, occlusion, and distance, and the description information of traffic lights cannot be obtained. With the development of technologies such as vehicle-road collaboration and vehicle-cloud collaboration, vehicles can obtain more comprehensive and accurate perception information through roadside devices or cloud servers. The distance of the traffic light intersection, the driving of the vehicle is planned in advance, so that the vehicle can pass the traffic light intersection safely and smoothly.

Generally, the vehicle communicates with the outside world (vehicle to everything, V2X) through the vehicle, obtains traffic lights and other vehicle information, and then performs vehicle speed planning control to optimize one or more of fuel economy, traffic efficiency, and comfort. The target is calculated to obtain the speed control result of the vehicle. The speed control method at traffic light intersections based on vehicle-road coordination is mostly implemented for fuel vehicles. The optimization goal in vehicle speed planning considers one or more of fuel economy, traffic efficiency, and comfort, and is not suitable for power-driven vehicles. vehicle.

Based on this, an embodiment of the present application provides a vehicle planning method. In this method, the working conditions and the first vehicle speed of the vehicle passing through the traffic light intersection can be planned according to the driving information of the vehicle and the road traffic information of the area where the vehicle is located. The working condition of the vehicle passing through the traffic light intersection is the braking working condition, and the braking energy recovered by the vehicle can be determined according to the first speed of the vehicle passing through the traffic light intersection and the second speed of the vehicle currently traveling, which can be determined according to the energy recovered by the vehicle. , control the vehicle to brake, thereby improving the braking energy recovery of the pure vehicle.

A possible vehicle control process provided by the embodiments of the present application is shown in FIG. 1 , and includes the following steps:

S101: The first device acquires the driving information of the vehicle and the road traffic information of the area where the vehicle is located, and predicts the working condition of the vehicle passing through the traffic light intersection and the first vehicle speed v _f of the vehicle passing through the traffic light intersection.

The first device involved in the embodiments of the present application may be the vehicle itself, or may be a roadside device, or may be a cloud server, or may be other devices, which are not limited herein.

Optionally, a collection module may be installed on the vehicle, and the vehicle collects the driving information of the vehicle through the installed collection module. The first device (eg, not the vehicle) may acquire the driving information in the vehicle, or the first device (eg, the vehicle) may acquire the driving information through the acquisition module.

Optionally, the first device may acquire road traffic information of the area where the vehicle is located from a roadside device or a cloud server. Taking the vehicle as the first device as an example, when the vehicle enters the communication range of the roadside device, and/or the vehicle enters the broadcast range of the cloud server, the roadside device Or the cloud server may send the road traffic information to the vehicle. That is, in the embodiment of the present application, the first device also takes into account the information obtained from the cloud server during the vehicle control process.

In a possible implementation, the first device may predict the first vehicle speed v _f of the vehicle passing through the traffic light intersection according to the remaining time of the traffic light and the distance between the vehicle and the traffic light intersection. The first vehicle speed v _f is the reference vehicle speed/target vehicle speed of the vehicle passing through the traffic light intersection.

The first device determines that the vehicle passes the traffic light when the remaining time of the traffic light is 0 (or the color of the traffic light changes) according to the distance between the vehicle and the traffic light intersection and the remaining time of the traffic light. The speed of the intersection v _light .

For example, when the traffic light is a red light, if the current second vehicle speed v(t)<v _light of the vehicle is traveling, the vehicle is traveling at the vehicle speed v(t). When the red light turns green, the vehicle has not reached the stop line of the traffic light intersection, the vehicle can pass through the traffic light intersection at a constant speed, and the first vehicle speed v _f of the vehicle passing through the traffic light intersection can be v (t). The vehicle may decelerate without braking. If the second vehicle speed v(t) at which the vehicle is currently traveling>v _light , the vehicle is traveling at the vehicle speed v(t). When the vehicle reaches the stop line of the traffic light intersection, the traffic light is still a red light, the vehicle can slow down or stop, and the first speed of the vehicle passing through the traffic light intersection can be v _sub (after deceleration vehicle speed) or 0 (vehicle speed when stopped). The vehicle can be braked to slow down.

For another example, when the traffic light is green, if the current second vehicle speed v(t)<v _light of the vehicle is traveling, the vehicle is traveling at the vehicle speed v(t). When the vehicle reaches the stop line at the traffic light intersection, the green light changes to red. The vehicle may be accelerated or stopped, and the first vehicle speed of the vehicle passing through the traffic light intersection may be v _add (vehicle speed after acceleration) or 0 (vehicle speed when stopped). If the second vehicle speed v(t) at which the vehicle is currently traveling>v _light , the vehicle is traveling at the vehicle speed v(t). The vehicle may pass through the traffic/traffic light intersection at a constant speed, and the first vehicle speed v _f of the vehicle passing through the traffic light intersection may be v ₀ .

In another possible implementation, the first device constructs a vehicle control model according to the driving information of the vehicle and the road traffic information of the area where the vehicle is located; the first device predicts the vehicle control model based on the vehicle control model. The working conditions of the vehicle passing through the traffic light intersection and the first vehicle speed v _f of the vehicle passing through the traffic light intersection. Optionally, based on the vehicle control model, the first device may also predict the acceleration of the vehicle passing through a traffic light intersection. The vehicle control model may also be referred to as a vehicle speed planning control model.

The vehicle model includes, but is not limited to, one or more of the following: a vehicle dynamics model, a physical constraint model, a boundary constraint model, an optimization objective of the vehicle control model, or a vehicle state change matrix. That is, one or more of a vehicle dynamics model, a physical constraint model, a boundary constraint model, an optimization objective of the vehicle control model, or a vehicle state change matrix may be considered when constructing the vehicle model. Or the vehicle model is related to one or more of the following: a vehicle dynamics model, a physical constraint model, a boundary constraint model, an optimization objective of the vehicle control model, or a vehicle state change matrix.

The vehicle dynamics model is the vehicle dynamics constraints. The vehicle dynamics model may be related to one or more of the following information: the speed of the vehicle, the driving force of the vehicle (which may be provided by an electric motor of the vehicle), the slope resistance of the road, the wind resistance, the air resistance Resistance, or the windward area of the vehicle (referring to the projected area of the vehicle in the direction of travel, which can be calculated by digital photography or engineering drawings), etc. Optionally, the vehicle dynamics model may satisfy the following formula:

where m is the mass/weight of the vehicle, a(t) is the acceleration of the vehicle at time t, F _t is the driving force of the vehicle,

is the slope resistance of the road,

The physical constraint model includes a speed constraint model of the vehicle, and/or an acceleration constraint model of the vehicle. The physical constraint model is related to the speed of the vehicle and/or the acceleration of the vehicle. The maximum value (v _max ) and the minimum value (v _min ) of the speed in the vehicle speed constraint model are limited by the mechanical performance of the vehicle, and can be set according to the actual conditions of different vehicles. For example, in the vehicle speed constraint model, v _min ≤ v(t) ≤ v _max , that is, the speed v(t) of the vehicle at time t is not less than (ie greater than or equal to) the minimum speed v _min of the vehicle, and not greater than (ie less than or equal to) the maximum speed v _max of the vehicle. Optionally v _min ≤ v(t) ≤ v _max and v(t) ≤ v _rmax , where v _rmax is the speed limit in the area where the vehicle is located (the maximum traveling speed in the area). The maximum value (a _max ) and the minimum value (a _min ) of the acceleration in the acceleration constraint model are limited by the mechanical performance of the vehicle, and can be set according to actual conditions of different vehicles. For example, in the acceleration constraint model, a _min ≤ a(t) ≤ a _max , that is, the acceleration a(t) of the vehicle at time t is not less than the minimum acceleration a _min of the vehicle, and not greater than the vehicle's acceleration a(t) Maximum acceleration a _max . The physical constraint model may be used to analyze the traffic efficiency and/or safety of the vehicle. in

Any time t belongs to the range of [t ₀ , t _f ]. [t ₀ , t _f ] indicates that the time range for controlling the vehicle is t ₀ to t _f . t ₀ is the starting time of the vehicle control, and t ₀ may be the time at which the traffic light conditions are determined, or may be the time at which the driving information of the vehicle is acquired, etc., which is not limited herein. t _f is the end time of the vehicle control, and in the scenario of passing the traffic light intersection, t _f may be the estimated time when the vehicle passes the traffic light intersection.

The boundary constraint model is used to constrain the vehicle to not collide with a vehicle in front of the vehicle (referred to as the vehicle in front). The boundary constraint model is related to one or more of the following information: the speed of the vehicle, the speed of a vehicle in front of which the vehicle is traveling, or the distance between the vehicle and a vehicle in front of the vehicle. The boundary constraint model can be used to analyze the safety of the vehicle. Optionally, the boundary constraint model may constrain the collision time TCC(t) between the vehicle and the preceding vehicle to not be less than the minimum collision time TCC _min , so as to ensure that the vehicle does not collide with the preceding vehicle. For example, the boundary constraint model can satisfy the following formula:

where d _other is the distance between the vehicle and the preceding vehicle, and v _other is the speed of the preceding vehicle.

The optimization objectives of the vehicle control model include, but are not limited to, one or more of the following: an efficiency evaluation index for the vehicle passing through a traffic light intersection, a safety evaluation index, a comfort evaluation index, or a vehicle energy recovery index. In a possible scenario, when the working condition of the vehicle passing through the traffic light intersection is a braking working condition, the vehicle control model includes the vehicle energy recovery index.

The optimization objective of the vehicle control model may be related to one or more of the following information: the speed of the vehicle, the position of the vehicle, the acceleration of the vehicle, or the time at which the vehicle passes through a traffic light intersection.

For example, the optimization objective of the vehicle control model satisfies the following formula:

J is the optimization objective of the vehicle control model, G(v(t _f ), x(t _f ), t _f ) is the optimization objective of the non-integral term,

is the optimization objective of the integral term, t ₀ is the current driving time of the vehicle, t _f is the end time of the vehicle control, v is the speed of the vehicle, x is the position of the vehicle, and a is the vehicle acceleration. In the scenario of passing the traffic light intersection, t _f may be the estimated time when the vehicle passes the traffic light intersection.

L(v,x,a) is related to one or more of the efficiency evaluation index, the safety evaluation index or the comfort evaluation index. Optionally, the efficiency evaluation index, the safety evaluation index, and the comfort evaluation index may be normalized, so that the value of each index is in the range [0, 1]. For example L(v,x,a)=ω _v L _v +ω _safe L _safe +ω _soft L _soft . Wherein L(v,x,a) is used to represent the optimization objective of the integral term, L _v is the efficiency evaluation index, ω _v is the weight of the efficiency evaluation index, L _safe is the safety evaluation index, ω _safe is the weight of the safety evaluation index, _Lsoft is the comfort evaluation index, and _ωsoft is the weight of the comfort evaluation index. In the embodiments of the present application, the values of the weights such as ω _v , ω _safe , and ω _soft are not limited.

The efficiency evaluation index of the vehicle passing through the traffic light intersection is the evaluation index of the vehicle speed dimension, which may be related to the speed of the vehicle. Optionally, the efficiency evaluation index is one of the speed v(t) of the vehicle at time t, the speed of the vehicle at the end of the vehicle control, or the speed of the vehicle passing through a traffic light intersection. or more related. The efficiency evaluation index of the vehicle passing through the traffic light intersection is related to the speed of the vehicle. For example, the efficiency evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: L _v =(v(t)-v _f ) ² . Wherein L _v is the efficiency evaluation index of the vehicle passing through the traffic light intersection, and v _f is the speed of the vehicle at the end of the vehicle control. In the scenario of passing a traffic light intersection, the speed of the vehicle at the end of the vehicle control may be the same as the speed of the vehicle passing through the traffic light intersection, and the v _f may also be expressed as the speed of the vehicle passing through the traffic light intersection. v _target .

The safety evaluation index of the vehicle passing through the traffic light intersection is related to the speed of the vehicle and the position of the vehicle speed. For example, the safety evaluation index of the vehicle passing through a traffic light intersection may be related to the collision time between the vehicle and the preceding vehicle. Optionally, the safety evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: L _safe =1-TCC(t)/TCC _max , where L _safe is the safety evaluation index of the vehicle passing through the traffic light intersection, and TCC(t) is The collision time between the vehicle and the vehicle in front of the vehicle, TCC _max is the maximum collision time between the vehicle and the vehicle in front of the vehicle.

The comfort evaluation index of the vehicle passing through the traffic light intersection may be related to the acceleration of the vehicle. Optionally, the comfort evaluation index is related to the acceleration a(t) of the vehicle at time t. For example, the comfort evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: _Lsoft =a(t) ² , _Lsoft is the comfort evaluation index of the vehicle passing through the traffic light intersection, and a(t) is the vehicle at t time acceleration.

In the scenario of a communication traffic light intersection, the working conditions of the vehicle passing through the traffic light intersection include a braking working condition and a non-braking working condition. The braking conditions include decelerating through a traffic light intersection or decelerating to a stop. Non-braking conditions include passing the traffic light intersection or accelerating through the traffic light intersection at a constant speed (ie, constant speed).

Optionally, when the working condition is a braking working condition, a(t) represents the braking acceleration of the vehicle. The braking acceleration a(t) of the vehicle satisfies the following conditions: a _min ≤az _≤a (t)≤a _max , a _min is the minimum acceleration of the vehicle, a _max is the maximum acceleration of the vehicle, a _z is the acceleration related to the braking intensity z of the vehicle. Pure vehicles are affected by the braking intensity Z. When the braking acceleration of the vehicle is greater than _az and the vehicle is under emergency braking, the vehicle only uses mechanical braking without braking energy recovery. However, when the braking acceleration of the vehicle is greater than _az and the vehicle is under non-emergency braking, under braking conditions, the vehicle is prevented from emergency braking, and the vehicle can recover braking as much as possible energy.

The G(v(t _f ), x(t _f ), t _f ) is related to one or more of the following information: the time when the vehicle passes through the traffic light intersection, the speed of the vehicle.

When the working condition is a braking condition, the G(v(t _f ),x(t _f ),t _f ) can be used with the traffic efficiency evaluation index of the vehicle passing through the traffic light intersection, and the braking energy recovery indicator related. The traffic efficiency index is an evaluation index of the time dimension. For example, G(v(t _f ), x(t _f ), t _f ) satisfies the following formula: G(v(t _f ), x(t _f ), t _f )=ω _time G _time -ω _SOC G _SOC . where ω _time G _time is the traffic efficiency evaluation index, ω _time is the weight used to calculate the traffic efficiency evaluation index, ω _SOC G _SOC is the braking energy recovery index, ω _SOC is the braking energy recovery index used to calculate The weight of the indicator, the G _SOC is related to the speed of the vehicle. In the embodiments of the present application, the values of the weights such as ω _time and ω _SOC are not limited.

G _time is related to time, and optional is related to the moment when the vehicle passes through the traffic light intersection. For example, G _time satisfies the following formula: G _time =1/2t _f ² .

G _SOC may be related to one or more of the mass/weight of the vehicle, the air resistance (or the energy of the air resistance), or the rolling resistance (or the energy of the rolling resistance). For example, assuming that the vehicle decelerates from the vehicle speed v ₀ to v _f , G _SOC satisfies the following formula: G _SOC =(1/2mv _f ² -1/2mv ₀ ² )-W _a -W _f , where W _a is the air resistance Energy (ie the energy consumed/work done when the vehicle resists air resistance), W _f is the energy of the rolling resistance (ie the energy consumed/work done when the vehicle resists rolling resistance). Taking into account the braking energy recovery index, the vehicle can recover as much braking energy as possible.

When the working condition is a non-braking working condition, the G(v(t _f ),x(t _f ),t _f ) may be related to the evaluation index of the traffic efficiency of the vehicle passing through the traffic light intersection. For example, G(v(t _f ), x(t _f ), t _f ) satisfies the following formula: G(v(t _f ), x(t _f ), t _f )=ω _time G _time . In non-braking conditions, braking energy recovery is not considered.

Optionally, the vehicle state change matrix is related to the position of the vehicle and the speed of the vehicle. For example, the vehicle state change matrix satisfies the following formula: [x(t)v(t)] ^T , where x(t) represents the position of the vehicle at time t, or the displacement of the vehicle at time t.

S102: If the working condition of the vehicle passing through the traffic light intersection is the braking condition, the first device determines the vehicle according to the first vehicle speed v _f and the second vehicle speed v(t) currently traveling by the vehicle Recovered braking energy.

Exemplarily, the first device may determine the braking energy recovered by the vehicle according to the vehicle speed (eg, the first vehicle speed v _f ), the acceleration, and the second vehicle speed v(t) calculated by the vehicle control model.

In the embodiments of the present application, the process of recovering braking energy of the vehicle is not limited. For example, the vehicle calculates a feedback torque under braking conditions according to the first vehicle speed v _f and the second vehicle speed v(t) that the vehicle is currently driving, and determines the braking recovered by the vehicle based on the feedback torque energy.

Optionally, the vehicle may convert the braking energy into electrical energy and store it in a battery of the vehicle to realize the recovery of braking energy.

S103: The first device controls the vehicle to brake according to the braking energy recovered by the vehicle.

In this S103, the first device controls the vehicle to brake and decelerate from v ₀ to v _f .

When the first device is not the vehicle, the first device may send v _f or braking acceleration a(t) to the vehicle, and the first vehicle performs braking and deceleration.

Optionally, if the working condition is a non-braking working condition, the first device controls the vehicle to maintain the current vehicle speed v ₀ or accelerate through a traffic light intersection.

In a possible implementation, the first device may execute S101-S103 at intervals of time T, and may control the vehicle in time, and respond to emergencies occurring during the driving of the vehicle in time.

In the vehicle control method provided by the embodiment of the present application, considering the braking energy recovery of the vehicle, when the vehicle brakes in the traffic light intersection scene, on the premise of ensuring the braking stability and safety of the vehicle, as much as possible It can recover braking energy, improve the energy utilization rate of the vehicle, and extend the cruising range of the vehicle.

FIG. 2 provides another possible vehicle control process according to the embodiment of the present application, which includes the following steps:

S201: The first device obtains vehicle driving information and road traffic information in the area where the vehicle is located, and predicts the operating conditions of the vehicle passing through the traffic light intersection and the first vehicle speed v _f of the vehicle passing through the traffic light intersection.

For the implementation process of this S201, reference may be made to the above-mentioned S101, and the similarities will not be repeated.

S202: If the working condition of the vehicle passing through the traffic light intersection is a non-braking condition, the first device controls the vehicle to pass through the traffic light intersection at the first vehicle speed v _f .

In a non-braking condition, the first device may control the vehicle to pass through a traffic light intersection at a constant speed at the first vehicle speed v _f . Optionally, when passing through a traffic light intersection at a constant speed, the first vehicle speed v _f may be equal to the second vehicle speed v ₀ currently traveling by the vehicle.

Or under non-braking conditions, the first device may control the vehicle to accelerate through the traffic light intersection at the first vehicle speed v _f .

In a possible implementation, the first device can execute S101-S103 every time T, can control the vehicle in time, and respond in time to emergencies that occur during the running of the vehicle.

In the vehicle control method provided by the embodiments of the present application, considering more comprehensive road condition information, the perception range of the vehicle can be improved, the perception capability of the vehicle can be enhanced, the comfort and safety of passengers can be improved, and the road safety can be improved. overall traffic efficiency.

FIG. 3 provides another possible vehicle control process according to the embodiment of the present application, which includes the following steps:

S301: The first device acquires the driving information of the vehicle and the road traffic information of the area where the vehicle is located, and predicts the working condition of the vehicle passing through the traffic light intersection and the first vehicle speed v _f of the vehicle passing through the traffic light intersection.

S302: If the working condition of the vehicle passing through the traffic light intersection is a braking condition, the first device determines the vehicle according to the first vehicle speed v _f and the second vehicle speed v(t) currently traveling by the vehicle Recovered braking energy. The first device controls the vehicle to brake according to the braking energy recovered by the vehicle.

For the implementation process of S301-S302, reference may be made to the above-mentioned S101-S103, and the similarities will not be repeated.

S303: If the working condition of the vehicle passing through the traffic light intersection is a non-braking condition, the first device controls the vehicle to pass through the traffic light intersection at the first vehicle speed v _f .

For the implementation process of S303, reference may be made to the above-mentioned S202, and the similarities will not be repeated.

In the vehicle control method provided by the embodiments of the present application, considering more comprehensive road condition information, the perception range of the vehicle can be improved, the perception capability of the vehicle can be enhanced, the comfort and safety of passengers can be improved, and the road safety can be improved. overall traffic efficiency. And when the vehicle brakes in the traffic light intersection scene, considering the braking energy recovery of the vehicle, on the premise of ensuring the braking stability and safety of the vehicle, recover as much braking energy as possible to improve the energy of the vehicle Utilization rate, extending the cruising range of the vehicle.

The above embodiment will be described below with a specific embodiment. FIG. 4 is a block diagram of the vehicle control process, and the specific steps are shown in FIG. 5 :

S501: The vehicle collects travel information through a collection device.

The collection device is installed on the vehicle. The collection device may be a vehicle-mounted sensor and/or a camera, or the like. Optionally, the vehicle may obtain information such as the color and seconds of the traffic lights through the camera.

S502: The vehicle obtains road traffic information from a roadside device or a cloud server.

Roadside devices or cloud servers can obtain road traffic information from traffic centers, traffic lights, and roadside cameras, etc., and send it to smart cars in the communication area.

For example, in the scenario where the vehicle passes through a traffic light intersection, when the vehicle enters the communication range of the roadside device, the roadside device sends the road traffic information to the vehicle; When the cloud server in the area is within the broadcast range of the cloud server, the cloud server sends road traffic information to the vehicle.

S503: According to the driving information and road traffic information, based on the vehicle dynamics model, the physical constraint model and the boundary constraint model, the efficiency, comfort, safety and energy recovery (optional) of the vehicle passing through the traffic light intersection are taken as Optimize the target and build the vehicle control model.

Wherein, in the braking condition, the vehicle control model may consider the energy recovery index. In the non-braking condition, the vehicle control model may not consider the energy recovery index.

S504: Based on the vehicle control model, the vehicle plans in advance the working conditions and vehicle speed of the vehicle passing through the traffic light intersection.

S505: In a non-braking condition, the vehicle controls the accelerator according to the planned vehicle speed.

Under braking conditions, based on the braking energy recovery control strategy, the maximum braking energy is recovered.

S506: In a braking condition, the vehicle recovers braking energy based on a braking energy recovery algorithm, and controls the braking.

In this S506, the braking energy recovered by the vehicle may be the maximum recovered braking energy.

S507: The vehicle repeats the steps of S501-S506 according to the time interval T to plan and control the vehicle speed.

In this embodiment, the vehicle obtains road traffic information in a roadside device or a cloud server, and can obtain more accurate, real-time and reliable road condition information, improve the perception range of the vehicle, and enhance the perception capability of the vehicle. The vehicle plans the operating conditions and vehicle speed of the vehicle in advance based on the acquired vehicle driving information and road traffic information, and considers different optimization objectives in the vehicle speed planning, which can improve the comfort and safety of passengers on the vehicle, and Improve the overall traffic efficiency of the road, and can improve the energy utilization rate of the vehicle and extend the cruising range of the vehicle.

It can be understood that the embodiments of the present application are mainly aimed at the speed control of the vehicle in the traffic light scene in the Internet of Vehicles environment. Of course, it can also be applied to other road traffic scenarios, such as ramps, vehicle-congested road sections and other vehicle braking conditions.

The vehicle control method according to the embodiment of the present application has been described in detail above with reference to FIGS. 1 to 5 . Based on the same technical concept as the above-mentioned vehicle control method, an embodiment of the present application further provides a communication device. As shown in FIG. 6 , the communication apparatus 600 includes an acquisition unit 601 and a processing unit 602 .

In one embodiment, specifically:

an obtaining unit 601, configured to obtain the driving information of the vehicle and the road traffic information of the area where the vehicle is located, and predict the working condition of the vehicle passing through the traffic light intersection and the first vehicle speed v _f of the vehicle passing through the traffic light intersection;

The processing unit 602 is configured to determine the recovery of the vehicle according to the first vehicle speed v _f and the current second vehicle speed v(t) of the vehicle if the working condition of the vehicle passing through the traffic light intersection is the braking condition The braking energy of the vehicle is controlled; according to the braking energy recovered by the vehicle, the vehicle is controlled to perform braking.

In an optional implementation manner, the processing unit 602 is specifically configured to construct a vehicle control model according to the driving information of the vehicle and the road traffic information of the area where the vehicle is located; based on the vehicle control model, predict the The working condition of the vehicle passing through the traffic light intersection and the first vehicle speed v _f of the vehicle passing through the traffic light intersection.

In an optional embodiment, the vehicle control model includes one or more of the following: a vehicle dynamics model, a physical constraint model, a boundary constraint model and an optimization objective of the vehicle control model;

The physical constraint model includes a speed constraint model of the vehicle, and/or an acceleration constraint model of the vehicle;

The boundary constraint model is used to constrain the vehicle not to collide with a vehicle in front of the vehicle;

The optimization objectives of the vehicle control model include one or more of the following: an efficiency evaluation index of the vehicle passing through a traffic light intersection, a safety evaluation index, a comfort evaluation index, and a vehicle energy recovery index.

In an optional implementation manner, the obtaining unit 601 is specifically configured to obtain the driving information collected by the collecting module of the vehicle, and obtain the road traffic in the area where the vehicle is located from a roadside device or a cloud server information.

In an optional embodiment, when the working condition is a braking condition, the optimization objective of the vehicle control model includes the vehicle energy recovery index.

In an optional embodiment, when the working condition is the braking condition, the braking acceleration a(t) of the vehicle satisfies the following conditions: a _min ≤a _z ≤a(t)≤a _max , a _min is the minimum acceleration of the vehicle, a _max is the maximum acceleration of the vehicle, and a _z is the acceleration related to the braking intensity Z of the vehicle.

In an optional embodiment, the optimization objective of the vehicle control model is related to one or more of the following information: the speed of the vehicle, the position of the vehicle, the acceleration of the vehicle, or the passage of the vehicle The moment at the traffic light intersection.

In an optional embodiment, the optimization objective of the vehicle control model satisfies the following formula:

J is the optimization target of the vehicle control model, t ₀ is the initial moment of vehicle control, t _f is the moment when the vehicle passes the traffic light intersection, v is the speed of the vehicle, x is the position of the vehicle, a is the the acceleration of the vehicle.

In an optional embodiment, the G(v(t _f ),x(t _f ),t _f ) is related to one or more of the following information: the time when the vehicle passes through the traffic light intersection, the speed.

In an optional embodiment, when the working condition is the braking condition, the G(v(t _f ), x(t _f ), t _f ) satisfies the following formula:

G(v(t _f ),x(t _f ),t _f )=ω _time G _time -ω _SOC G _SOC ;

When the working condition is a non-braking condition, the G(v(t _f ),x(t _f ),t _f ) satisfies the following formula:

G(v(t _f ),x(t _f ),t _f )=ω _time G _time ;

In an optional embodiment, the G _SOC satisfies the following formula:

G _SOC =(1/2mv _f ² -1/2mv ₀ ² )-W _a -W _f , where m is the mass of the vehicle, _{Wa is the energy of air resistance, and W f} _is the energy of rolling resistance.

In an optional embodiment, the G _time satisfies the following formula:

G _time = 1/2t _f ² .

In an optional implementation manner, the efficiency evaluation index of the vehicle passing through the traffic light intersection is related to the speed of the vehicle.

In an optional implementation manner, the efficiency evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: L _v =(v(t)-v _f ) ² , where L _v is the efficiency of the vehicle passing through the traffic light intersection evaluation indicators.

In an optional implementation manner, the safety evaluation index of the vehicle passing through a traffic light intersection is related to the speed of the vehicle and the position of the vehicle speed.

In an optional embodiment, the safety evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: L _safe =1-TCC(t)/TCC _max , where L _safe is the safety evaluation of the vehicle passing through the traffic light intersection The index, TCC(t) is the collision time between the vehicle and the vehicle in front of the vehicle, and TCC _max is the maximum collision time between the vehicle and the vehicle in front of the vehicle.

In an optional implementation manner, the comfort evaluation index of the vehicle passing through a traffic light intersection is related to the acceleration of the vehicle.

In an optional implementation manner, the comfort evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: _Lsoft =a(t) ² , _Lsoft is the comfort evaluation index of the vehicle passing through the traffic light intersection, a (t) is the acceleration of the vehicle at time t.

In an optional embodiment, the vehicle dynamics model is related to the speed of the vehicle.

In an optional embodiment, the vehicle dynamics model satisfies the following formula:

where F _t is the driving force of the vehicle,

is the slope resistance of the road,

In an optional embodiment, the boundary constraint model is related to the speed of the vehicle and the position of the vehicle speed. Optionally, the boundary constraint model is related to one or more of the following information: the speed of the vehicle, the speed of the vehicle in front of the vehicle, or the distance between the vehicle and the vehicle in front of the vehicle.

In an optional embodiment, the boundary constraint model satisfies the following formula:

In an optional implementation manner, the driving information of the vehicle includes one or more of the following: the second vehicle speed v(t) of the current driving of the vehicle, the acceleration v(t) of the current driving of the vehicle, The current location of the vehicle.

In another embodiment, specifically:

The processing unit 602 is configured to control the vehicle to pass through the traffic light intersection at the first vehicle speed v _f if the working condition of the vehicle passing through the traffic light intersection is a non-braking condition.

In yet another embodiment, specifically:

The processing unit 602 is configured to determine the recovery of the vehicle according to the first vehicle speed v _f and the current second vehicle speed v(t) of the vehicle if the working condition of the vehicle passing through the traffic light intersection is the braking condition The braking energy of the vehicle is controlled; according to the braking energy recovered by the vehicle, the vehicle is controlled to perform braking. If the working condition of the vehicle passing through the traffic light intersection is a non-braking condition, the vehicle is controlled to pass through the traffic light intersection at the first vehicle speed v _f .

It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and other division methods may be used in actual implementation. Each functional unit in the embodiments of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, and the computer software products are stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes .

Based on the above embodiments, an embodiment of the present application further provides a communication apparatus, and the communication apparatus can implement the above embodiments. Referring to FIG. 7, the communication apparatus 700 may include a processor 701 and a memory 702, wherein:

The processor 701 may be a central processing unit (central processing unit, CPU), a network processor (network processor, NP), or a combination of CPU and NP, and so on. The processor 701 may further include a hardware chip. The above-mentioned hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above-mentioned PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a general array logic (generic array logic, GAL) or any combination thereof. When the processor 701 implements the above functions, it can be implemented by hardware, and of course, it can also be implemented by executing corresponding software by hardware.

The processor 701 and the memory 702 are connected to each other. Optionally, the processor 701 and the memory 702 may be connected to each other through a bus 703; the bus 703 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. . The bus can be divided into address bus, data bus, control bus and so on. For ease of presentation, only one thick line is used in FIG. 7, but it does not mean that there is only one bus or one type of bus.

In an optional implementation manner, the memory 702, coupled with the processor 701, is used for storing programs and the like. Specifically, the program may include program code, the program code including computer operation instructions. The memory 702 may include RAM and may also include non-volatile memory, such as at least one disk storage. The processor 701 executes the application program stored in the memory 702 to realize the above-mentioned functions, thereby realizing the function of the communication device 700, that is, realizing the vehicle control method.

In one embodiment, specifically, when the communication device 700 implements the vehicle control method, it may include:

The processor 701 is configured to call the program instructions in the memory 702 to execute:

Acquire the driving information of the vehicle and the road traffic information of the area where the vehicle is located, and predict the working condition of the vehicle passing through the traffic light intersection and the first vehicle speed v _f of the vehicle passing through the traffic light intersection;

If the working condition of the vehicle passing through the traffic light intersection is the braking working condition, the braking energy recovered by the vehicle is determined according to the first vehicle speed v _f and the second vehicle speed v(t) at which the vehicle is currently traveling;

The vehicle is controlled to brake according to the braking energy recovered by the vehicle.

In an optional implementation manner, the processor 701 is specifically configured to: construct a vehicle control model according to the driving information of the vehicle and the road traffic information of the area where the vehicle is located; based on the vehicle control model, predict The working condition of the vehicle passing through the traffic light intersection and the first vehicle speed v _f of the vehicle passing through the traffic light intersection.

In an optional implementation manner, the processor 701 is specifically configured to: acquire driving information collected by a collection module of the vehicle, and acquire road traffic in the area where the vehicle is located from a roadside device or a cloud server information.

G(v(t _f ),x(t _f ),t _f )=ω _time G _time -ω _SOC G _SOC ;

G(v(t _f ),x(t _f ),t _f )=ω _time G _time ;

In an optional embodiment, the G _SOC satisfies the following formula:

In an optional embodiment, G _time satisfies the following formula:

G _time = 1/2t _f ² .

The comfort evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: _Lsoft =a(t) ² , _Lsoft is the comfort evaluation index of the vehicle passing through the traffic light intersection, and a(t) is the vehicle at time t acceleration.

In an optional embodiment, in an optional embodiment, the vehicle dynamics model satisfies the following formula:

where F _t is the driving force of the vehicle,

is the slope resistance of the road,

In an optional implementation manner, the driving information of the vehicle includes one or more of the following: the second vehicle speed v(t) of the current driving of the vehicle, the acceleration v(t) of the current driving of the vehicle, the current location of the vehicle;

In another embodiment, specifically, when the communication device 700 implements the vehicle control method, it may include:

If the working condition of the vehicle passing through the traffic light intersection is a non-braking condition, the vehicle is controlled to pass through the traffic light intersection at the first vehicle speed v _f .

In yet another embodiment, specifically, when the communication device 700 implements the vehicle control method, it may include:

If the working condition of the vehicle passing through the traffic light intersection is the braking condition, the braking energy recovered by the vehicle is determined according to the first vehicle speed v _f and the second vehicle speed v(t) the vehicle is currently driving; The braking energy recovered by the vehicle controls the vehicle to brake;

Based on the above embodiments, the embodiments of the present application further provide an automatic driving vehicle, and the automatic driving vehicle may include the communication device shown in FIG. 6 or FIG. 7 to implement the above embodiments.

An embodiment of the present application further provides an automatic driving assistance system, and the automatic driving assistance system may include the communication device shown in FIG. 6 or FIG. 7 to implement the foregoing embodiments.

Based on the above embodiments, the embodiments of the present application further provide a computer-readable storage medium, where the computer-readable storage medium is used to store a computer program. When the computer program is executed by a computer, the computer can implement the methods provided by the above-mentioned embodiments. vehicle control method.

Embodiments of the present application further provide a computer program product, where the computer program product is used to store a computer program, and when the computer program is executed by a computer, the computer can implement the vehicle control method provided by the above method embodiments.

An embodiment of the present application further provides a chip, where the chip is coupled with a memory, and the chip is used to implement the vehicle control method provided by the above method embodiments.

In this application, plural means two or more.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the scope of the embodiments of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

A vehicle control method, comprising:

Acquire the driving information of the vehicle and the road traffic information of the area where the vehicle is located, and predict the working condition of the vehicle passing through the traffic light intersection and the first vehicle speed v f of the vehicle passing through the traffic light intersection;

If the working condition of the vehicle passing through the traffic light intersection is the braking working condition, the braking energy recovered by the vehicle is determined according to the first vehicle speed v f and the second vehicle speed v(t) at which the vehicle is currently traveling;

The vehicle is controlled to brake according to the braking energy recovered by the vehicle.
The method according to claim 1, characterized in that, by acquiring the driving information of the vehicle and the road traffic information of the area where the vehicle is located, predicting the working condition of the vehicle passing through the traffic light intersection and the first time the vehicle passes through the traffic light intersection. a vehicle speed v f , including:

constructing a vehicle control model according to the driving information of the vehicle and the road traffic information of the area where the vehicle is located;

Based on the vehicle control model, the operating conditions of the vehicle passing through the traffic light intersection and the first vehicle speed v f of the vehicle passing through the traffic light intersection are predicted.
The method of claim 2, wherein the vehicle control model comprises one or more of the following: a vehicle dynamics model, a physical constraint model, a boundary constraint model and an optimization objective of the vehicle control model;

The physical constraint model includes a speed constraint model of the vehicle, and/or an acceleration constraint model of the vehicle;

The boundary constraint model is used to constrain the vehicle not to collide with a vehicle in front of the vehicle;

The optimization objectives of the vehicle control model include one or more of the following: an efficiency evaluation index of the vehicle passing through a traffic light intersection, a safety evaluation index, a comfort evaluation index, and a vehicle energy recovery index.
The method according to any one of claims 1-3, wherein the acquiring the driving information of the vehicle and the road traffic information of the area where the vehicle is located comprises:

The driving information collected by the collection module of the vehicle is obtained, and the road traffic information of the area where the vehicle is located is obtained from a roadside device or a cloud server.
The method of claim 3, wherein, when the working condition is a braking condition, the optimization target of the vehicle control model includes the vehicle energy recovery index.
The method according to claim 3, wherein when the working condition is a braking condition, the braking acceleration a(t) of the vehicle satisfies the following conditions: a min ≤a z ≤a(t)≤a max and a min are the minimum acceleration of the vehicle, a max is the maximum acceleration of the vehicle, and a z is the acceleration related to the braking intensity Z of the vehicle.
The method of claim 3 or 5, wherein the optimization objective of the vehicle control model is related to one or more of the following information: the speed of the vehicle, the position of the vehicle, the acceleration of the vehicle, Or the moment when the vehicle passes through a traffic light intersection.
The method of claim 7, wherein the optimization objective of the vehicle control model satisfies the following formula:

J is the optimization target of the vehicle control model, t 0 is the initial moment of vehicle control, t f is the moment when the vehicle passes the traffic light intersection, v is the speed of the vehicle, x is the position of the vehicle, a is the the acceleration of the vehicle.
The method of claim 8, wherein the G(v(t f ),x(t f ),t f ) is related to one or more of the following information: the time when the vehicle passes through the traffic light intersection, the speed of the vehicle.
The method of claim 9, wherein when the working condition is a braking condition, the G(v(t f ), x(t f ), t f ) satisfies the following formula:

G(v(t f ),x(t f ),t f )=ω time G time -ω SOC G SOC ;

When the working condition is a non-braking condition, the G(v(t f ),x(t f ),t f ) satisfies the following formula:

G(v(t f ),x(t f ),t f )=ω time G time ;

Wherein, ω time G time is the traffic efficiency evaluation index of the vehicle passing through the traffic light intersection, the G time is related to the time when the vehicle passes the traffic light intersection, ω SOC G SOC is the braking energy recovery index, and the G SOC is related to the time when the vehicle passes the traffic light intersection. the speed of the vehicle.
The method of claim 10, wherein the G SOC satisfies the following formula:

G SOC =(1/2mv f 2 -1/2mv 0 2 )-W a -W f , where m is the mass of the vehicle, Wa is the energy of air resistance, and W f is the energy of rolling resistance.
The method of claim 10, wherein the G time satisfies the following formula:

G time = 1/2t f 2 .
The method according to claim 3, 5 or 7, wherein the efficiency evaluation index of the vehicle passing through the traffic light intersection is related to the speed of the vehicle.
The method according to claim 13, wherein the efficiency evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: L v =(v(t)-v f ) 2 , wherein L v is the vehicle passing the traffic light Efficiency evaluation index of intersection.
The method according to claim 3, 5 or 7, wherein the safety evaluation index of the vehicle passing through a traffic light intersection is related to the speed of the vehicle and the position of the vehicle speed.
The method according to claim 15, wherein the safety evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: L safe =1-TCC(t)/TCC max , wherein L safe is the vehicle passing through the traffic light intersection TCC(t) is the collision time between the vehicle and the vehicle in front of the vehicle, and TCC max is the maximum collision time between the vehicle and the vehicle in front of the vehicle.
The method according to claim 3, 5 or 7, wherein the comfort evaluation index of the vehicle passing through a traffic light intersection is related to the acceleration of the vehicle.
The method according to claim 17, wherein the comfort evaluation index of the vehicle passing through the traffic light intersection satisfies the following formula: Lsoft =a(t) 2 , and Lsoft is the comfort evaluation of the vehicle passing through the traffic light intersection index, a(t) is the acceleration of the vehicle at time t.
4. The method of claim 3, wherein the vehicle dynamics model is related to the speed of the vehicle.
The method of claim 19, wherein the vehicle dynamics model satisfies the following formula:
where F t is the driving force of the vehicle,
is the slope resistance of the road,
is the rolling friction force, μ is the road friction coefficient, 1/2C D ρ a Av(t) 2 is the wind resistance, CD is the air resistance coefficient, ρ a is the air density, and A is the windward area of the vehicle.
4. The method of claim 3, wherein the boundary constraint model is related to one or more of the following information: the speed of the vehicle, the speed of a vehicle ahead of the vehicle, or the relationship between the vehicle and the vehicle. The distance traveled by the vehicle ahead.
The method of claim 21, wherein the boundary constraint model satisfies the following formula:
where d other is the distance between the vehicle and the vehicle in front of the vehicle, and v other is the speed of the vehicle in front of the vehicle.
The method according to any one of claims 1-22, wherein the driving information of the vehicle includes one or more of the following: a second vehicle speed v(t) of the vehicle currently driving, the current Driving acceleration a(t), the current driving position of the vehicle;

The road traffic information of the area where the vehicle is located includes one or more of the following: the color of the traffic light, the number of seconds in the traffic light, the distance between the vehicle and the traffic light, the speed limit in the area where the vehicle is located, the speed of the vehicle ahead of the vehicle, The distance between the vehicle and the vehicle in front of the vehicle.
A vehicle control device, comprising:

an acquisition unit, configured to acquire the driving information of the vehicle and the road traffic information of the area where the vehicle is located, and predict the working condition of the vehicle passing through the traffic light intersection and the first vehicle speed v f of the vehicle passing through the traffic light intersection;

The processing unit is configured to determine, according to the first vehicle speed v f and the second vehicle speed v(t) at which the vehicle is currently traveling, if the working condition of the vehicle passing through the traffic light intersection is the braking condition, Braking energy; control the vehicle to brake according to the braking energy recovered by the vehicle.
The apparatus according to claim 24, wherein the processing unit is specifically configured to construct a vehicle control model according to the driving information of the vehicle and the road traffic information of the area where the vehicle is located; based on the vehicle control model , predict the operating conditions of the vehicle passing through the traffic light intersection and the first vehicle speed v f of the vehicle passing through the traffic light intersection.
The apparatus of claim 25, wherein the vehicle control model comprises one or more of the following: a vehicle dynamics model, a physical constraint model, a boundary constraint model and an optimization objective of the vehicle control model;

The physical constraint model includes a speed constraint model of the vehicle, and/or an acceleration constraint model of the vehicle;

The boundary constraint model is used to constrain the vehicle not to collide with a vehicle in front of the vehicle;

The optimization objectives of the vehicle control model include one or more of the following: an efficiency evaluation index of the vehicle passing through a traffic light intersection, a safety evaluation index, a comfort evaluation index, and a vehicle energy recovery index.
The device of claim 26, wherein when the working condition is a braking condition, the optimization objective of the vehicle control model includes the vehicle energy recovery index.
The device according to claim 26, wherein when the working condition is a braking condition, the braking acceleration a(t) of the vehicle satisfies the following condition: a min ≤a z ≤a(t)≤a max and a min are the minimum acceleration of the vehicle, a max is the maximum acceleration of the vehicle, and a z is the acceleration related to the braking intensity Z of the vehicle.
The apparatus of any one of claims 26-28, wherein the optimization objective of the vehicle control model is related to one or more of the following information: the speed of the vehicle, the position of the vehicle, the vehicle acceleration, or the moment when the vehicle passes through the traffic light intersection.
The device of claim 29, wherein the optimization objective of the vehicle control model satisfies the following formula:

J is the optimization target of the vehicle control model, t 0 is the initial moment of vehicle control, t f is the moment when the vehicle passes the traffic light intersection, v is the speed of the vehicle, x is the position of the vehicle, a is the the acceleration of the vehicle.
The apparatus of claim 30, wherein the G(v(t f ),x(t f ),t f ) is related to one or more of the following information: the time when the vehicle passes through the traffic light intersection, the speed of the vehicle.
The device according to claim 31, wherein when the working condition is a braking condition, the G(v(t f ), x(t f ), t f ) satisfies the following formula:

G(v(t f ),x(t f ),t f )=ω time G time -ω SOC G SOC ;

When the working condition is a non-braking condition, the G(v(t f ),x(t f ),t f ) satisfies the following formula:

G(v(t f ),x(t f ),t f )=ω time G time ;

Wherein, ω time G time is the traffic efficiency evaluation index of the vehicle passing through the traffic light intersection, the G time is related to the time when the vehicle passes the traffic light intersection, ω SOC G SOC is the braking energy recovery index, and the G SOC is related to the time when the vehicle passes the traffic light intersection. the speed of the vehicle.
The apparatus of claim 32, wherein the G SOC satisfies the following formula:

G SOC =(1/2mv f 2 -1/2mv 0 2 )-W a -W f , where m is the mass of the vehicle, Wa is the energy of air resistance, and W f is the energy of rolling resistance.
The apparatus of claim 32, wherein the G time satisfies the following formula:

G time = 1/2t f 2 .
A communication device, comprising a processor and a memory;

the memory is used to store computer-executable instructions;

The processor is configured to execute computer-implemented instructions stored in the memory to cause the communication device to perform the method of any one of claims 1 to 23.
A communication device, comprising a processor and an interface circuit;

The interface circuit is configured to receive code instructions and transmit them to the processor; the processor executes the code instructions to execute the method according to any one of claims 1 to 23.
A readable storage medium, characterized in that, the readable storage medium is used for storing instructions, and when the instructions are executed, the method according to any one of claims 1-23 is implemented.
An automatic driving vehicle, characterized by comprising the vehicle control device according to any one of claims 24-34.
An automatic driving assistance system, characterized by comprising the vehicle control device according to any one of claims 24-34.