WO2022057745A1 - Assisted-driving control method and apparatus - Google Patents

Abstract

The present application provides an assisted-driving control method, said method comprising: an assisted-driving control method, said method comprising: determining forward road features; on the basis of a road model, determining a first control coefficient corresponding to said forward road features, said road model characterizing a correlation between different road features and the first control coefficient; determining current weather features; on the basis of a weather model, determining a second control coefficient corresponding to the current weather features, said weather model characterizing a correlation between different weather features and the second control coefficient; on the basis of the first control coefficient, the second control coefficient, and driving behavior features, performing assisted driving.

Description

A control method and device for assisting driving

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of the Chinese patent application filed on September 15, 2020 with the application number 202010977840.9 and titled "A Control Method and Device for Assisted Driving", the entire contents of which are incorporated herein by reference Applying.

technical field

The present application relates to a control method for assisted driving, and in particular, to a warning method and device for assisted driving.

Background technique

Dangerous driving is likely to cause a traffic accident and violate the criminal law, and is generally sentenced for the crime of causing a traffic accident and the crime of dangerous driving. Dangerous driving behaviors include: running red lights, speeding, and three rushes and one overtaking. In addition, bad driving behavior will cause excessive wear and tear of car "wear parts" and premature replacement. If you develop proper car habits, you can extend the service life of some wear parts.

Currently, there is a service for generating monthly driving behavior reports for car owners in the prior art, and the records include: the total mileage of the month, the number of overtaking, the number of high-speed cornering, and the number of emergency braking.

However, the purpose of the current report is still to remind car owners, so that car owners can pay attention to their behavior in the subsequent driving process after understanding their own driving behavior problems. At present, the prompt function of assisted driving is generated based on a fixed program, which is relatively rigid and cannot effectively solve the safety problem.

SUMMARY OF THE INVENTION

Embodiments of the present application provide a control method and device for assisting driving, which can improve driving safety.

To achieve the above object, the embodiments of the present application adopt the following technical solutions:

In a first aspect, there is provided a control method for assisting driving, the method comprising: determining a front road feature; determining a first control coefficient corresponding to the front road feature based on a road model, the road model representing different road features and different Correspondence between the first control coefficients; determining the current weather feature; determining the second control coefficient corresponding to the current weather feature based on the weather model, the weather model representing the correspondence between different weather features and different second control coefficients; Assisted driving is performed based on the first control coefficient, the second control coefficient, and the driving behavior feature.

In the above technical solution, road conditions, driving weather and driving behavior habits of the driver are comprehensively considered, so that personalized reminders can be provided, thereby improving the safety of driving.

In a possible implementation manner of the first aspect, the assisted driving behavior includes a driving warning or a driving takeover.

In the above technical solution, the driver is reminded by means of driving warning to correct the driver's bad driving habits in real time; the vehicle operation is taken over in a dangerous situation to assist driving, thereby avoiding potential safety hazards.

In a possible implementation manner of the first aspect, the road feature includes at least one of a road type, a road material, a road gradient, or a road curvature.

In a possible implementation manner of the first aspect, the weather feature includes at least one of light intensity, rainfall, snowfall, or wind.

In a possible implementation manner of the first aspect, the driving behavior characteristic includes at least one of a driving vehicle speed, a centripetal acceleration, an accelerator pedal opening, or a brake pedal opening.

In a possible implementation manner of the first aspect, the determining of the road ahead feature includes: determining the location of the road ahead; and determining the feature of the road ahead based on the location of the road ahead.

In a possible implementation manner of the first aspect, the determining the position of the road ahead includes: determining the position of the road ahead according to navigation information; or determining the position of the road ahead by predicting whether the travel is a conventional route.

In a possible implementation manner of the first aspect, the driving behavior feature is used to characterize the driving habit of the driver.

In the above technical solution, the road position is determined through navigation or conventional route judgment, so as to obtain the road characteristics of the corresponding trip, so that the location information or time information that needs to be alerted for the entire trip can be calculated in advance. On the one hand, the driver can understand the road driving situation in advance. On the other hand, it also reduces the pressure on vehicle computing resources for real-time computing.

In a second aspect, the present application provides a control device for assisting driving, the device comprising: a road feature determination module, used for determining the front road feature; a first coefficient determination module, used for determining the front road feature corresponding to the road model based on The first control coefficient of , the road model represents the corresponding relationship between different road features and the first control coefficient; the weather feature determination module is used to determine the current weather feature; the second coefficient determination module is used to determine based on the weather model the second control coefficient corresponding to the current weather feature, where the weather model represents the correspondence between different weather features and the second control coefficient; the execution module is configured to, based on the first control coefficient, the second control coefficient and Driving behavior features perform assisted driving.

In a possible implementation manner of the second aspect, the assisted driving includes a driving warning or a driving takeover.

In a possible implementation manner of the second aspect, the road feature includes at least one of a road type, a road material, a road gradient, or a road curvature.

In a possible implementation manner of the second aspect, the weather feature includes at least one of light intensity, rainfall, snowfall, or wind.

In a possible implementation manner of the second aspect, the driving behavior characteristic includes at least one of a driving vehicle speed, a centripetal acceleration, an accelerator pedal opening, or a brake pedal opening.

In a possible implementation manner of the second aspect, the road feature determination module is specifically configured to: determine the position of the road ahead; and determine the feature of the road ahead based on the position of the road ahead.

In a possible implementation manner of the second aspect, the road feature determination module is specifically configured to: determine the position of the road ahead according to navigation information; or determine the position of the road ahead by predicting whether the itinerary is a conventional route.

In a possible implementation manner of the second aspect, the driving behavior feature is used to characterize the driving habit of the driver.

In a third aspect, the present application provides a computer-readable storage medium, where a computer program is stored in the storage medium, and the computer program is used to execute the control method for assisted driving according to any one of the first aspect above.

In a fourth aspect, the present application provides an electronic device, the electronic device comprising: a processor; a memory for storing computer-executable instructions; the processor for executing the computer-executable instructions to drive the The electronic device implements the control method for assisted driving according to any one of the first aspect above.

In a fifth aspect, the present application provides a computer program product, which, when running on a computer, enables the computer to execute the control method for assisted driving provided by any of the possible implementations of the first aspect above.

In a sixth aspect, the present application provides a chip, including a processor and an interface, where the interface is configured to read the processor-executable instructions from an external memory, and the processor is configured to execute any one of the first aspects above A control method for assisted driving provided by a possible implementation of the item.

In a seventh aspect, the present application provides a vehicle, which is used to execute any one of the above-mentioned control methods for assisted driving provided in the first aspect.

In an eighth aspect, the present application provides a server, which is configured to execute any one of the above-mentioned control methods for assisted driving provided in the first aspect.

Understandably, any of the above-mentioned control devices, computer-readable storage media, electronic devices, computer program products, chips, vehicles, and servers for assisting driving can be implemented by the corresponding control methods provided above. Therefore, for the beneficial effects that can be achieved, reference may be made to the beneficial effects of the corresponding control methods provided above, which will not be repeated here.

Description of drawings

FIG. 1 is a schematic structural diagram of a vehicle with an assisted driving function provided by an embodiment of the application;

2 is a schematic structural diagram of a computer system for assisting driving provided by an embodiment of the present application;

3 is a schematic structural diagram of an assisted driving control system according to an embodiment of the present application;

FIG. 4 is an application schematic diagram of a cloud-side command-assisted driving behavior control system provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of an assisted driving driving control method provided by an embodiment of the present application;

6 is a scene diagram of an assisted driving control method in a navigation mode provided by an embodiment of the present application;

7 is a scene diagram of an assisted driving control method in a non-navigation mode provided by an embodiment of the present application;

FIG. 8 is an assisted driving control device provided by an embodiment of the present application;

FIG. 9 is a schematic structural diagram of a computer program product provided by an embodiment of the present application.

detailed description

FIG. 1 is a functional block diagram of a vehicle 100 with a driving assistance function provided by an embodiment of the present application. In one embodiment, the vehicle 100 is configured in an assisted driving mode.

Vehicle 100 may include various subsystems, such as travel system 102 , sensor system 104 , control system 106 , one or more peripherals 108 and power supply 110 , computer system 112 , and user interface 116 . Alternatively, vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple elements. Additionally, each of the subsystems and elements of the vehicle 100 may be interconnected by wire or wirelessly.

The travel system 102 may include components that provide powered motion for the vehicle 100 . In one embodiment, propulsion system 102 may include engine 118 , energy source 119 , transmission 120 , and wheels/tires 121 . The engine 118 may be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a gasoline engine and electric motor hybrid engine, an internal combustion engine and an air compression engine hybrid engine. Engine 118 converts energy source 119 into mechanical energy.

Examples of energy sources 119 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. The energy source 119 may also provide energy to other systems of the vehicle 100 .

Transmission 120 may transmit mechanical power from engine 118 to wheels 121 . Transmission 120 may include a gearbox, a differential, and a driveshaft. In one embodiment, transmission 120 may also include other devices, such as clutches. Among other things, the drive shaft may include one or more axles that may be coupled to one or more wheels 121 .

The sensor system 104 may include several sensors that sense information about the environment surrounding the vehicle 100 . For example, the sensor system 104 may include a positioning system 122 (which may be a GPS system, a Beidou system or other positioning system), an inertial measurement unit (IMU) 124, a radar 126, a laser rangefinder 128, and camera 130. The sensor system 104 may also include sensors of the internal systems of the vehicle 100 being monitored (eg, an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors can be used to detect objects and their corresponding characteristics (position, shape, orientation, velocity, etc.). This detection and identification is a critical function for the safe operation of the autonomous vehicle 100 .

The positioning system 122 may be used to estimate the geographic location of the vehicle 100 . The IMU 124 is used to sense position and orientation changes of the vehicle 100 based on inertial acceleration. In one embodiment, IMU 124 may be a combination of an accelerometer and a gyroscope.

Radar 126 may utilize radio signals to sense objects within the surrounding environment of vehicle 100 . In some embodiments, in addition to sensing objects, radar 126 may be used to sense the speed and/or heading of objects.

The laser rangefinder 128 may utilize laser light to sense objects in the environment in which the vehicle 100 is located. In some embodiments, the laser rangefinder 128 may include one or more laser sources, laser scanners, and one or more detectors, among other system components.

Camera 130 may be used to capture multiple images of the surrounding environment of vehicle 100 . Camera 130 may be a still camera or a video camera.

The control system 106 controls the operation of the vehicle 100 and its components. Control system 106 may include various elements including steering system 132 , throttle 134 , braking unit 136 , sensor fusion algorithms 138 , computer vision system 140 , route control system 142 , and obstacle avoidance system 144 .

The steering system 132 is operable to adjust the heading of the vehicle 100 . For example, it can be a steering wheel system.

The throttle 134 is used to control the operating speed of the engine 118 and thus the speed of the vehicle 100 .

The braking unit 136 is used to control the deceleration of the vehicle 100 . The braking unit 136 may use friction to slow the wheels 121 . In other embodiments, the braking unit 136 may convert the kinetic energy of the wheels 121 into electrical current. The braking unit 136 may also take other forms to slow the wheels 121 to control the speed of the vehicle 100 .

Computer vision system 140 may be operable to process and analyze images captured by camera 130 in order to identify objects and/or features in the environment surrounding vehicle 100 . The objects and/or features may include traffic signals, road boundaries and obstacles. Computer vision system 140 may use object recognition algorithms, Structure from Motion (SFM) algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system 140 may be used to map the environment, track objects, estimate the speed of objects, and the like.

The route control system 142 is used to determine the travel route of the vehicle 100 . In some embodiments, the route control system 142 may combine data from the sensors 138 , the GPS 122 , and one or more predetermined maps to determine a driving route for the vehicle 100 .

The obstacle avoidance system 144 is used to identify, evaluate, and avoid or otherwise traverse potential obstacles in the environment of the vehicle 100 .

Of course, in one example, the control system 106 may additionally or alternatively include components other than those shown and described. Alternatively, some of the components shown above may be reduced.

Vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users through peripheral devices 108 . Peripherals 108 may include a wireless communication system 146 , an onboard computer 148 , a microphone 150 and/or a speaker 152 .

In some embodiments, peripherals 108 provide a means for a user of vehicle 100 to interact with user interface 116 . For example, the onboard computer 148 may provide information to the user of the vehicle 100 . User interface 116 may also operate on-board computer 148 to receive user input. The onboard computer 148 can be operated via a touch screen. In other cases, peripheral devices 108 may provide a means for vehicle 100 to communicate with other devices located within the vehicle. For example, microphone 150 may receive audio (eg, voice commands or other audio input) from a user of vehicle 100 . Similarly, speakers 152 may output audio to a user of vehicle 100 .

Wireless communication system 146 may wirelessly communicate with one or more devices, either directly or via a communication network. For example, wireless communication system 146 may use 3G cellular communications, such as CDMA, EVDO, GSM/GPRS, or 4G cellular communications, such as LTE. Or 5G cellular communications. The wireless communication system 146 may communicate with a wireless local area network (WLAN) using WiFi. In some embodiments, the wireless communication system 146 may communicate directly with the device using an infrared link, Bluetooth, or ZigBee. Other wireless protocols, such as various vehicle communication systems, for example, wireless communication system 146 may include one or more dedicated short range communications (DSRC) devices, which may include communication between vehicles and/or roadside stations public and/or private data communications.

The power supply 110 may provide power to various components of the vehicle 100 . In one embodiment, the power source 110 may be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such a battery may be configured as a power source to provide power to various components of the vehicle 100 . In some embodiments, power source 110 and energy source 119 may be implemented together, such as in some all-electric vehicles.

Some or all of the functions of the vehicle 100 are controlled by the computer system 112 . Computer system 112 may include at least one processor 113 that executes instructions 115 stored in a non-transitory computer-readable medium such as memory 114 . Computer system 112 may also be multiple computing devices that control individual components or subsystems of vehicle 100 in a distributed fashion.

The processor 113 may be any conventional processor, such as a commercially available CPU. Alternatively, the processor may be a dedicated device such as an ASIC or other hardware-based processor. Although FIG. 1 functionally illustrates the processor, memory, and other elements of the computer 110 in the same block, one of ordinary skill in the art will understand that the processor, computer, or memory may actually include a processor, a computer, or a memory that may or may not Multiple processors, computers, or memories stored within the same physical enclosure. For example, the memory may be a hard drive or other storage medium located within an enclosure other than computer 110 . Thus, reference to a processor or computer will be understood to include reference to a collection of processors or computers or memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components such as the steering and deceleration components may each have their own processor that only performs computations related to component-specific functions .

In various aspects described herein, a processor may be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the vehicle while others are performed by a remote processor, including taking steps necessary to perform a single maneuver.

In some embodiments, the memory 114 may contain instructions 115 (eg, program logic) executable by the processor 113 to perform various functions of the vehicle 100 , including those described above. The memory 114 may also contain additional instructions, including sending data to, receiving data from, interacting with, and/or controlling one or more of the propulsion system 102 , the sensor system 104 , the control system 106 , and the peripherals 108 . instruction.

In addition to instructions 115, memory 114 may store data such as road maps, route information, vehicle location, direction, speed, and other such vehicle data, among other information. Such information may be used by the vehicle 100 and the computer system 112 during operation of the vehicle 100 in autonomous, semi-autonomous and/or manual modes.

A user interface 116 for providing information to or receiving information from a user of the vehicle 100 . Optionally, user interface 116 may include one or more input/output devices within the set of peripheral devices 108 , such as wireless communication system 146 , onboard computer 148 , microphone 150 and speaker 152 .

Computer system 112 may control functions of vehicle 100 based on input received from various subsystems (eg, travel system 102 , sensor system 104 , and control system 106 ) and from user interface 116 . For example, computer system 112 may utilize input from control system 106 in order to control steering unit 132 to avoid obstacles detected by sensor system 104 and obstacle avoidance system 144 . In some embodiments, computer system 112 is operable to provide control of various aspects of vehicle 100 and its subsystems.

Alternatively, one or more of these components described above may be installed or associated with the vehicle 100 separately. For example, memory 114 may exist partially or completely separate from vehicle 100 . The above-described components may be communicatively coupled together in a wired and/or wireless manner.

Optionally, the above component is just an example. In practical applications, components in each of the above modules may be added or deleted according to actual needs, and FIG. 1 should not be construed as a limitation on the embodiments of the present application.

A car traveling on a road, such as vehicle 100 above, can identify objects within its surroundings to determine an adjustment to the current speed. The objects may be other vehicles, traffic control equipment, or other types of objects. In some examples, each identified object may be considered independently, and based on the object's respective characteristics, such as its current speed, acceleration, distance from the vehicle, etc., may be used to determine the speed at which the car is to be adjusted.

Optionally, the vehicle 100 or a computing device associated with the vehicle 100 (eg, computer system 112, computer vision system 140, memory 114 of FIG. rain, ice on the road, etc.) to predict the behavior of the identified object. Optionally, each identified object is dependent on the behavior of the other, so it is also possible to predict the behavior of a single identified object by considering all identified objects together. The vehicle 100 can adjust its speed based on the predicted behavior of the identified object. In other words, the assisted driving vehicle can determine what steady state the vehicle will need to adjust to (eg, accelerate, decelerate, or stop) based on the predicted behavior of the object. In this process, other factors may also be considered to determine the speed of the vehicle 100, such as the lateral position of the vehicle 100 in the road being traveled, the curvature of the road, the proximity of static and dynamic objects, and the like.

In addition to providing instructions to adjust the speed of the assisted vehicle, the computing device may also provide instructions to modify the steering angle of the vehicle 100 so that the assisted vehicle follows a given lane line and/or maintains contact with objects in the vicinity of the vehicle (eg, safe lateral and longitudinal distances for cars in adjacent lanes on the road.

The above-mentioned vehicle 100 can be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawn mower, a recreational vehicle, a playground vehicle, construction equipment, a tram, a golf cart, a train, a cart, etc. The application examples are not particularly limited.

After the above-mentioned vehicle 100 is introduced, the computer system for assisting driving involved in the present application will be introduced below.

According to FIG. 2 , computer system 101 includes processor 103 coupled to system bus 105 . The processor 103 may be one or more processors, each of which may include one or more processor cores. A video adapter 107, which can drive a display 109, is coupled to the system bus 105. System bus 105 is coupled to input-output (I/O) bus 113 through bus bridge 111 . I/O interface 115 is coupled to the I/O bus. I/O interface 115 communicates with various I/O devices, such as input device 117 (eg, keyboard, mouse, touch screen, etc.), media tray 121, (eg, CD-ROM, multimedia interface, etc.). Transceiver 123 (which can transmit and/or receive radio communication signals), camera 155 (which can capture sceneries and dynamic digital video images) and external USB interface 125 . Wherein, optionally, the interface connected to the I/O interface 115 may be a USB interface.

The processor 103 may be any conventional processor, including a reduced instruction set computing ("RISC") processor, a complex instruction set computing ("CISC") processor, or a combination thereof. Alternatively, the processor may be a special purpose device such as an application specific integrated circuit ("ASIC"). Optionally, the processor 103 may be a neural network processor or a combination of a neural network processor and the above-mentioned conventional processors.

Alternatively, in various embodiments described herein, computer system 101 may be located remotely from the assisted driving vehicle and may communicate wirelessly with the assisted driving vehicle. In other aspects, some of the processes described herein are performed on a processor disposed within the assisted driving vehicle and others are performed by a remote processor, including taking actions required to perform a single maneuver.

Computer 101 may communicate with software deployment server 149 through network interface 129 . Network interface 129 is a hardware network interface, such as a network card. The network 127 may be an external network, such as the Internet, or an internal network, such as an Ethernet network or a virtual private network (VPN). Optionally, the network 127 may also be a wireless network, such as a WiFi network, a cellular network, and the like.

The hard disk drive interface is coupled to the system bus 105 . The hard drive interface is connected to the hard drive. System memory 135 is coupled to system bus 105 . Data running in system memory 135 may include operating system 137 and application programs 143 of computer 101 .

The operating system includes a Shell 139 and a kernel 141 . Shell 139 is an interface between the user and the kernel of the operating system. The shell is the outermost layer of the operating system. The shell manages the interaction between the user and the operating system: waiting for user input, interpreting the user's input to the operating system, and processing various operating system outputs.

Kernel 141 consists of those parts of the operating system that manage memory, files, peripherals, and system resources. Interacting directly with hardware, the operating system kernel usually runs processes and provides inter-process communication, providing CPU time slice management, interrupts, memory management, IO management, and more.

The application program 143 includes programs related to controlling assisted driving of the car, for example, programs that control the route or speed of the car. Application 143 also exists on the system of software deployment server 149. In one embodiment, computer system 101 may download application 147 from deploying server 14 when application 141 needs to be executed.

The application program 143 involved in this application is a program for assisting driving related functions. Assisted driving functions include driving warnings, or taking over the driving of the vehicle in certain scenarios.

Embodiments of the present application provide a control method and device for assisting driving, which comprehensively considers road conditions, driving weather, and driving behavior habits of drivers, so that personalized reminders can be provided, thereby improving driving safety.

As shown in FIG. 3 , the assisted driving system provided by the embodiment of the present application includes a cloud server 11 , a vehicle terminal 12 , and a data provider 13 .

The cloud server will receive the road data, weather data and driving behavior data provided by the vehicle terminal 12 and the data provider 13, and the cloud server will store and process the received data and then model it to form a road model, weather model and driving behavior. Model. And the models are regularly updated, and the above three models are regularly delivered to the vehicle terminal.

The data provider 13 includes a road data provider, a weather data provider, and a high-precision map data provider. The data provided by the road data provider includes at least one of: road type, road material, road gradient, or road curvature. The data provided by the weather data provider includes at least one of light intensity, rainfall, snowfall, and wind. The data provided by the high-precision map data provider includes high-precision maps. The cloud server 11 can provide real-time high-precision map data for multiple vehicles 12 through a wireless network. The cloud server 11 includes a large-capacity storage space for storing maps. data, including high-precision maps, and is responsible for updating and distributing electronic maps.

The above road features can be provided by road information providers, or provided by high-precision map providers, or the vehicle or other vehicle terminals can upload the collected road features through sensors, such as visual cameras, millimeter-wave radars, and lidars.

The weather condition may be provided by a weather data provider, or may be obtained by the automatic wiper sensor or the visual camera of the vehicle terminal 12, and the weather influence information on the road surface.

The vehicle terminal 12 reports the driver's driving behavior data to the cloud server, and the driving behavior data includes the driver's driving behavior characteristics. Specifically, the driving behavior characteristics include: driving speed, centripetal acceleration, and accelerator pedal opening. , at least one of the brake pedal opening. The driving behavior feature is used to characterize the driver's driving habits, especially bad driving habits. The cloud server 11 establishes a driving behavior model based on the driving habit and the driving behavior feature, and the driving behavior model reflects the above-mentioned corresponding relationship between the driving habit of the driver and the driving behavior feature of the driver.

The above driving behavior characteristics can be determined by the information of the steering system 132 , the accelerator 134 , the braking unit 136 , the steering wheel and other components of the vehicle terminal as shown in FIG. 1 . The cloud server 11 collects statistics on the driving data of the driver according to the driving behavior data reported by the vehicle terminal 12 to determine the driving habits of the driver. For example, as shown in Table 1:

For example, assuming that the vehicle terminal 10s uploads data once, if the acceleration corresponding to the brake pedal opening in the uploaded data exceeds the abrupt deceleration threshold, the behavior is identified as a rapid deceleration behavior. Wherein, the acceleration threshold of the sudden deceleration is -1.67m/s ² by default. For another example, if the centripetal acceleration in the uploaded data exceeds the centripetal acceleration threshold, it is identified as a sharp turning behavior, wherein the centripetal acceleration threshold is 5 m/s ² by default.

Table 1

driving habits	Judgment criteria
frequent braking	Sudden deceleration/braking behavior statistics reach more than 6 times
frequent sharp turns	Sharp turn statistics reach more than 9 times
…	…

The reporting can be reported according to the first rule, and the reporting rule can be, for example: in a certain period, for example, at 12:00 every night, count the driving behavior data for one day, and calculate the frequency of a certain driving behavior of the driver. The calculation result is compared with the value of the driving behavior of the previous cycle. If the two values are inconsistent, upload it; if the two values are consistent, the upload is not performed.

During operation, the vehicle terminal 12 detects road conditions and weather conditions through sensors provided on the vehicle terminal, such as sensors such as lidar, camera millimeter-wave radar, ultrasonic waves, or combined inertial navigation. Optionally, the vehicle terminal 12 may obtain weather conditions through information provided by the wiper sensor, or determine weather conditions and road conditions through image information provided by a visual camera, and the vehicle terminal 12 may report the weather conditions and road conditions to the cloud server 11 . The report can be reported according to the second rule, and the report rule can be: when the vehicle collects a certain road condition or weather condition and reports it in real time, the time interval for data reporting can be less than a certain threshold, such as 10s.

The cloud server 11 stores and processes the received data, and then performs modeling to form a road model, a weather model, and a driving behavior model. The cloud server 11 can be modeled according to the experience of the technician, or the model can be modeled by a formula. The road model, weather model and driving behavior model may be in the form of a matrix, an array, or a table. The road model represents the corresponding relationship between the road feature and the first control coefficient, and the weather model represents the corresponding relationship between the weather feature and the second control coefficient. The first control coefficient reflects the degree of influence of road characteristics on road driving, and the second control coefficient reflects the degree of influence of weather characteristics on road driving.

For example, Table 2 shows one form of road model: a table. Table 2 shows the relationship between different road characteristics and the first control coefficient. For example, when the road type is an urban highway, the value of the first control coefficient is 1.

Table 2

For example, Table 3 shows the weather model in the form of a table. Table 3 shows the relationship between different weather features and the second control coefficient. For example, when the weather feature is no rain, the value of the second control coefficient is 1.

table 3

Table 4 shows that the driving feature model is in the form of a table, and Table 4 shows the values of driving features corresponding to different habits of the driver. For example, for a driver who has the habit of turning sharply, the value of the centripetal acceleration of the driving behavior characteristic is greater than the threshold value a1.

The vehicle terminal 12 may periodically download the road model, weather model, and driving behavior model from the cloud server 11 to the local. When the vehicle is turned on, the vehicle obtains the location information of the current road through the positioning system (such as GPS system, or Beidou system) or high-precision map in real time, and then determines the location information of the road ahead, such as through navigation information, or through pre-judgment described Whether the trip is a regular route or not to determine the road position ahead. Specifically, the date, time, and road location of the current itinerary can be compared with the historical itinerary. If the date, time, and road location of the current itinerary are the same as the historical itinerary, it is determined as a conventional route. For example, a commute route, or a route to pick up family members. Further, the road ahead may be a road within a certain threshold range from the current road position, for example, 100-1000m, or calculated in combination with the vehicle speed. Further, the road features of the forward position information can be obtained according to the high-precision map. Or the vehicle collects the features of the road ahead based on the sensors of its own vehicle; or the vehicle can also accept the features of the road ahead sent by other vehicles.

The vehicle terminal 12 further inputs the forward road characteristics into the above-mentioned road model, thereby determining the first control coefficient.

On the other hand, the vehicle terminal 12 acquires the current weather characteristics in real time, specifically through a high-precision map, or through data collected by on-board sensors, or through data provided by a weather provider. The vehicle terminal 12 inputs the weather characteristic into the above-mentioned weather model, thereby determining the second control coefficient.

According to the first control coefficient, the second control coefficient and the driving behavior characteristics, an assisted driving strategy for the behavior of the driver is obtained. For example, modeling calculation can be performed through formulas, or reference values can be obtained through statistical data, or through testing. Exemplarily, as shown in Table 5 below, assuming that the driver has the driving habit of braking frequently, the numerical values in the table represent the braking distance re-determined by the driving assistance control system for the driver, where X is based on the driver's original braking distance. The original braking distance is calculated from the driving behavior characteristics (brake pedal opening, driving speed), A(A1, A2, A3) is the first control coefficient, and B(B1, B2, B3) is the second control coefficient. The calculation method for re-determining the braking distance of the driving assistance system of the present application can be calculated in various ways. For example, the first control coefficient and the second control coefficient can be multiplied by the original braking distance.

table 5:

It should be noted that the selection of the above parameter types and calculation methods are only examples, and other suitable parameters may also be used according to specific test results, or new parameters may be redefined.

After calculating the above-mentioned value, the vehicle terminal may choose to issue a driving warning to the driver to remind the driver before a certain distance from the re-determined braking position. If the driver does not accept the advice of the driving warning, that is, the driver does not perform the braking operation at the calculated braking position, the vehicle will take over the driving and automatically perform the braking operation, thereby ensuring the safe driving of the driving.

In the embodiments of the present application, driving road conditions, driving weather, and driving behavior habits of drivers are comprehensively considered, so that personalized reminders can be provided, thereby improving driving safety.

The embodiment of the present application also provides an assisted driving system, which specifically includes a cloud server 11 , a vehicle terminal 12 and a data provider 13 .

The cloud server 11 will receive the road data, weather data and driving behavior data provided by the vehicle terminal 12 and the data provider 13, and the cloud server 11 will store and process the received data and then conduct modeling to form a road model, weather model and Modeling of driving behavior. During driving, the vehicle terminal 12 collects various information (similar to the working principle of the vehicle terminal 12 in FIG. 3 , and will not be repeated here), and uploads the location information and weather information of the vehicle terminal to the cloud server 11 in real time. The cloud server 11 obtains an assisted driving strategy for the driver's behavioral habits according to the road characteristics ahead, the current weather characteristics, and the driving behavior data. The cloud server 11 issues the assisted driving strategy to the vehicle terminal 12 . Other data acquisition, modeling, calculation processes, and technical effects of the cloud server 11 are the same as the principles described in the corresponding parts of FIG. 3 , and will not be repeated here.

FIG. 4 is an application schematic diagram of a cloud-side command-assisted driving control method provided by an embodiment of the present application.

The in-vehicle computer system 112 may also receive information from or transfer information to other computer systems. Alternatively, sensor data collected from the sensor system 104 of the vehicle 100 may be transferred to another computer for processing of the data. As shown in FIG. 5, data from computer system 112 may be transmitted via a network to cloud-side computer 720 for further processing. Networks and intermediate nodes may include various configurations and protocols, including the Internet, the World Wide Web, Intranets, Virtual Private Networks, Wide Area Networks, Local Area Networks, private networks using one or more of the company's proprietary communication protocols, Ethernet, WiFi and HTTP, and various combinations of the foregoing. Such communications may be by any device capable of transferring data to and from other computers, such as modems and wireless interfaces.

In one example, computer 720 may include a server having multiple computers, such as a load balancing server farm, that exchange information with different nodes of the network for the purpose of receiving, processing, and transmitting data from computer system 112. The server may be configured similarly to computer system 110 , with processor 730 , memory 740 , instructions 750 , and data 760 .

The data 760 may include multiple sets of driving parameter values. The server 720 may receive, monitor, store, update, and transmit various information related to roads, weather, and driver behavior. The server 720 will combine the road characteristics ahead and the current weather characteristics. , driving behavior characteristics, and obtain the assisted driving strategy for the driver's behavior habits. The assisted driving strategy is issued to the vehicle terminal, and the vehicle terminal will execute the assisted driving strategy. Assisted driving strategies include warning the driver or adopting driving takeover when the driver does not adopt the warning strategy.

The assisted driving control method provided by the embodiments of the present application comprehensively considers road conditions, driving weather, and driving behavior habits of drivers, so that personalized reminders can be provided, thereby improving driving safety.

5 is a schematic flowchart of a control method for assisted driving provided by an embodiment of the present application. The method can be applied to the above-mentioned assisted driving system, and the method includes the following steps:

S01. Obtain a road model, a weather model, and a driving behavior model, wherein the road model includes road features corresponding to road positions, the weather model includes weather features that affect driving, and the driving behavior model includes the driver's driving behavior Behavioral features are used to characterize the driver's driving habits, especially bad driving habits.

First, the vehicle terminal 12 acquires the road model, weather model and driving behavior model from the cloud. The road model includes road features corresponding to the road location, and the road location can use geographic information to represent its longitude and latitude, and the road features include at least one of: road type, road material, road gradient, or road curvature. The weather model reflects the degree of influence of weather conditions on road driving, the weather model includes weather features, and the weather features include at least one of light intensity, rainfall, snowfall, and wind power.

The driving behavior features included in the driving behavior model reflect the driver's driving habits, such as the steering wheel angle when the driver is accustomed to turning, the driver's braking distance, etc. The driver's behavior features include driving speed, centripetal acceleration, accelerator pedal At least one of opening, brake pedal opening, steering wheel angle, and braking distance.

Specifically, the driver can download and update the model from the cloud before the vehicle is turned on or at a fixed period, or set a display on the vehicle terminal to perform the relevant interface, and the driver can select from the display to start the update program. Optionally, the display can be Touch the monitor. Alternatively, related buttons and buttons can also be set, and the driver can download or update the function model by clicking the buttons and buttons.

It should be noted that step S01 is not a step that must be performed in the assisted driving control method of the present application, and the driver may choose not to perform this step by making settings and choosing to perform this step.

S02. Determine the road features ahead.

Wherein, the vehicle terminal 12 may obtain the road characteristics of the journey ahead of the vehicle through a navigation map, such as a high-precision map, in the navigation mode. Alternatively, the features of the road ahead can be calculated from the information collected by the self-vehicle sensors. Alternatively, it can also accept notification information sent by other vehicles, roadside, or the cloud to determine the road characteristics ahead.

Further, determining the front road feature may further include: determining the front road position; and determining the front road feature based on the front road position. Specifically, other vehicles, the roadside, and the cloud can send corresponding road features to the current vehicle according to the road location information where the current vehicle is located. Further, the following methods can be used to determine the road position:

Method 1, the vehicle terminal 12 can obtain the position information of the road on which the vehicle is currently traveling through the global positioning system 122 shown in FIG. 1, for example, the global positioning system 122 can be a GPS system, a Beidou system or other positioning systems, to determine the position of the road ahead;

Method 2: Determine the position of the road ahead by judging whether the itinerary is a conventional route. Specifically, the date, time, and road location of the current itinerary can be compared with the historical itinerary. If the date, time, and road location of the current itinerary are the same as the historical itinerary, it is determined as a conventional route. For example, a commute route, or a route to pick up family members.

The third method is to collect the position of the vehicle through another vehicle or a roadside camera, so as to determine the road position in front of the vehicle.

S03. Determine a first control coefficient corresponding to the front road feature based on a road model, where the road model represents the correspondence between different road features and the first control coefficient.

The vehicle terminal inputs the road feature information determined in the previous step into the road model, so as to obtain the first control coefficient corresponding to the front road feature.

For example, the value can be obtained through Table 2, which is not repeated here.

S04. Determine the current weather feature.

Wherein, the current weather feature may be determined by the weather information obtained by the automatic wiper sensor of the vehicle or a nearby vehicle terminal, the visual camera, or by the road location information in the above steps through the weather service provider.

Weather characteristics mainly include: rainfall, snowfall size and light intensity. Among them, the amount of rainfall and snowfall will affect the friction coefficient of the road surface, which will have a greater impact on the braking and cornering functions; too strong or too dark light will affect the recognition accuracy of the visual sensor, thereby affecting the assisted driving function.

S05. Determine a second control coefficient corresponding to the current weather feature based on a weather model, where the weather model represents the correspondence between different weather features and the second control coefficient.

The weather feature obtained in the previous step is input into the weather model, so that the second control coefficient corresponding to the weather feature can be determined.

For example, the value can be obtained through Table 3, which is not repeated here.

S06. Execute assisted driving based on the first control coefficient, the second control coefficient and the driving behavior feature.

Wherein, the assisted driving is performed based on the first control coefficient, the second control coefficient and the driving behavior characteristic, so that the occurrence time or the occurrence place of the driving action for the driver can be obtained. For example, modeling calculation can be performed through formulas, or reference values can be obtained through statistical data, or through testing. For example, as shown in Table 5 above, details are not repeated here.

The driving behavior features are the driving parameters that the driver is accustomed to adopt when controlling the driving of the vehicle, as shown in Table 4.

The assisted driving system will warn the driver before reaching the re-determined position, such as prompting the driver to start decelerating operations and a specific speed reference value, if the driver does not start to decelerate at the determined position, or has not reached the Speed reference, the vehicle will take over to control the vehicle. Thus, the driving safety is improved.

The technical solution provided by this application comprehensively considers the weather, roads and the driver's personal driving habits, and provides targeted warnings or takeovers to the driver during the driving process, thereby ensuring the safety of the driver's life.

Further, the road feature includes at least one of road type, road material, road gradient, or road curvature.

Further, the weather feature includes at least one of light intensity, rainfall, snowfall, or wind.

Further, the driving behavior characteristics include: at least one of driving speed, centripetal acceleration, accelerator pedal opening, or brake pedal opening

FIG. 6 is a schematic diagram of a scene of assisted driving in a vehicle navigation mode. Suppose there are two drivers, driver A operates car A, and driver B operates car B. In daily driving, vehicles A and B collect the driving behavior data of drivers A and B respectively and combine the driving behavior data of A and B. Uploaded to the cloud server, the cloud server models the driving behaviors of A and B respectively, and judges that user A's driving habits are often sharp turns. The process and method for establishing the road model and the weather model by the cloud server are the same as the description part corresponding to FIG. 3 , and will not be repeated here.

When drivers A and B enter the vehicle to prepare for a driving trip, the above road model, weather model, and driving behavior model selection can be downloaded or updated after starting the vehicle, or the vehicle can also be automatically updated at a certain frequency and within a fixed period.

After drivers A and B input the travel destination in the navigation software, the assisted driving system of the vehicle terminal can determine the road location of the travel. The vehicle terminal can obtain the current weather conditions through the method described in the corresponding description of FIG. 3 . If the travel time is long, the weather conditions may change during the entire driving time. When the weather conditions change is detected, the control method for assisted driving of the present application can be recalculated in real time, or the weather information provided by the supplier can be used. Weather forecast function, and the situation of the weather forecast corresponds to the road position of the entire road section of the trip.

The assisted driving system calculates the above-mentioned road location information and the weather conditions in combination with the driving behavior model, and obtains the time or location information that needs to alert the driver or perform assisted driving. For example, modeling calculation can be performed through formulas, or reference values can be obtained through statistical data, or through testing.

For a driver who uses a navigation map, such as a high-precision map, the time or location information of the assisted driving calculated above can be associated with the navigation map. When the driver is driving, the navigation system combines the high-precision map for driving. staff to remind.

Or before the trip begins, the driver can check the time or location of the assisted driving, thereby enhancing safety awareness.

For example, for driver A, due to his driving habit of turning sharply, when he encounters a turning situation, the navigation system will not only prompt "350 meters ahead will turn right" as shown in Figure 6, but also Remind the driver to "reduce the speed to 30km/h", while the navigation system for driver B will only routinely remind "350 meters ahead to turn right" when it encounters a corner.

FIG. 7 is a schematic diagram of a scenario of a vehicle in a non-navigation mode. When driver A starts the vehicle, the assisted driving system detects that the driver is not using the navigation system, and the assisted driving system will count the date, time, and location of the current itinerary, and then determine whether the itinerary is the driver's regular route. , if it is a regular route of driver A, such as a commuting route, the assisted driving system can extract the location information of each road based on historical data, and then combine weather information and driving behavior data to predict the road section of the entire trip, and calculate the driver. The time or location information for A to warn or assist driving. Before the start of the trip, the driver can check the time or location of the assisted driving, thereby enhancing safety awareness.

Or, when driver A is driving to a road section that needs to be reminded, such as when he is about to turn in Figure 7, the assisted driving system will give a warning to driver A: 350 meters ahead is about to turn right, please reduce the speed to 30km/h.

In the above technical solution, the road position is determined by navigation or conventional route judgment, and the road characteristics of the corresponding trip are further obtained, so that the position information or time information that needs to be alerted for the entire trip can be calculated in advance. On the one hand, the driver can know the road driving in advance. On the other hand, it also relieves the pressure on vehicle computing resources for real-time computing.

The embodiments of the present application also provide a control device for assisting driving, which may be located at the vehicle end or in the cloud. As shown in FIG. 8 , the device specifically includes: an acquisition module 101 for acquiring a road model, a weather model, and a driving behavior model, wherein the road model includes road features corresponding to road positions, and the weather model includes the generated data for driving. Influenced weather characteristics, the driving behavior model includes the driving behavior characteristics of the driver;

a road feature determination module 102, configured to determine the road feature ahead;

a first coefficient determination module 103, configured to determine a first control coefficient corresponding to the front road feature based on a road model, the road model representing the correspondence between different road features and the first control coefficient;

a weather feature determination module 104, configured to determine the current weather feature;

The second coefficient determination module 105 is configured to determine a second control coefficient corresponding to the current weather feature based on a weather model, where the weather model represents the correspondence between different weather features and the second control coefficient;

An execution module 106, configured to execute assisted driving based on the first control coefficient, the second control coefficient and the driving behavior feature.

Further, the assisted driving includes a driving warning or a driving takeover.

Further, the road feature includes at least one of road type, road material, road gradient, or road curvature.

Further, the weather feature includes at least one of light intensity, rainfall, snowfall, or wind.

Further, the driving behavior characteristics include: at least one of a driving vehicle speed, a centripetal acceleration, an accelerator pedal opening, or a brake pedal opening.

Further, the road feature determination module is specifically configured to: determine the position of the road ahead; and determine the feature of the road ahead based on the position of the front road.

Further, the road feature determination module is specifically configured to: determine the position of the road ahead according to the navigation information; or determine the position of the road ahead by prejudging whether the itinerary is a conventional route.

The above-mentioned control device for assisting driving provided in this embodiment can be used to implement the above-mentioned control device for assisting driving, and can be used to implement the technical solutions of the above-mentioned method embodiments. The implementation principles and technical effects thereof are similar. Reference may be made to the corresponding descriptions in the method embodiments, which will not be repeated here.

Embodiments of the present application further provide a chip, including a processor and an interface, where the interface is used to read the processor-executable instructions from an external memory, and the processor is used to execute possible implementations provided by the foregoing method embodiments The control method for assisted driving provided by the method.

Embodiments of the present application further provide a vehicle, where the vehicle is configured to execute the control method provided by the above method embodiments, which may realize the provided driving assistance.

The present application also provides a server, which is configured to execute the control method provided by the above method embodiments that may realize the provided driving assistance.

It should be noted that, the above-mentioned computer-readable storage medium, electronic device, computer program product, chip, vehicle, and server can all be implemented by the corresponding methods provided above. Therefore, for the beneficial effects that can be achieved, reference may be made to the beneficial effects of the corresponding methods provided above, which will not be repeated here.

In some embodiments, the disclosed methods may be implemented as computer program instructions encoded in a machine-readable format on a computer-readable storage medium or on other non-transitory media or articles of manufacture. 9 schematically illustrates a conceptual partial view of an example computer program product including a computer program for executing a computer process on a computing device, arranged in accordance with at least some embodiments presented herein. In one embodiment, example computer program product 600 is provided using signal bearing medium 601 . The signal bearing medium 601 may include one or more program instructions 602 that, when executed by one or more processors, may provide the functions, or portions thereof, described above with respect to FIGS. 3-7 . Thus, for example, with reference to the embodiment shown in FIG. 5 , one or more features of steps S01 - 06 may be undertaken by one or more instructions associated with the signal bearing medium 601 . Additionally, program instructions 602 in FIG. 9 also describe example instructions.

In some examples, the signal bearing medium 601 may include a computer-readable medium 603, such as, but not limited to, a hard drive, a compact disc (CD), a digital video disc (DVD), a digital tape, a memory, a read only memory (Read) -Only Memory, ROM) or random access memory (Random Access Memory, RAM) and so on. In some implementations, the signal bearing medium 601 may include a computer recordable medium 604, such as, but not limited to, memory, read/write (R/W) CDs, R/W DVDs, and the like. In some embodiments, signal bearing medium 601 may include communication medium 605, such as, but not limited to, digital and/or analog communication media (eg, fiber optic cables, waveguides, wired communication links, wireless communication links, etc.). Thus, for example, the signal bearing medium 601 may be conveyed by a wireless form of communication medium 605 (eg, a wireless communication medium conforming to the IEEE 802.11 standard or other transmission protocol). The one or more program instructions 602 may be, for example, computer-executable instructions or logic-implemented instructions. In some examples, a computing device, such as the computing device described with respect to FIGS. 3-7 may be configured, in response to communication via one or more of computer readable medium 603 , computer recordable medium 604 , and/or communication medium 605 Program instructions 602 communicated to a computing device to provide various operations, functions, or actions. It should be understood that the arrangements described herein are for illustrative purposes only. Thus, those skilled in the art will understand that other arrangements and other elements (eg, machines, interfaces, functions, sequences, and groups of functions, etc.) can be used instead and that some elements may be omitted altogether depending on the desired results . Additionally, many of the described elements are functional entities that may be implemented as discrete or distributed components, or in conjunction with other components in any suitable combination and position.

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

If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or a part that contributes to the prior art or all or part of the technical solution, and the computer software product is stored in a storage inoculation , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a 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, removable hard disk, ROM, RAM, magnetic disk or optical disk and other media that can store programs.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server, or data center Transmission to another website site, computer, server, or data center is by wire (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available ring that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more of the available media integrations. The available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, solid State Disk (SSD), etc.).

The program instructions can be implemented in the form of software functional units and can be sold or used as a stand-alone product, and the memory can be any form of computer-readable storage medium. Based on this understanding, all or part of the technical solutions of the present application may be embodied in the form of software products, including several instructions to enable hundreds of millions of computer devices, specifically processors, to execute the target detection device in each embodiment of the present application. all or part of the steps. The aforementioned computer-readable storage medium includes: U disk, removable hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disks or optical disks and other programs that can store programs medium.

The electronic devices described above in this embodiment can be used to implement the technical solutions of the above method embodiments, and their implementation principles and technical effects are similar. The functions of each device can refer to the corresponding descriptions in the embodiments, which will not be repeated here. .

Finally, it should be noted that: the above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this, and any changes or replacements within the technical scope disclosed in the present application should be covered by the present application. within the scope of protection of the application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims (22)

1. A control method for assisted driving, characterized in that the method comprises:

   determine the characteristics of the road ahead;

   determining the first control coefficient corresponding to the front road feature based on the road model, the road model representing the correspondence between different road features and different first control coefficients;

   determine current weather characteristics;

   determining the second control coefficient corresponding to the current weather feature based on the weather model, the weather model representing the correspondence between different weather features and different second control coefficients;

   Assisted driving is performed based on the first control coefficient, the second control coefficient, and the driving behavior feature.
2. The method of claim 1, wherein the driving assistance comprises driving warning or driving takeover.
3. The method according to claim 1 or 2, wherein the road feature comprises at least one of road type, road material, road gradient, or road curvature.
4. The method according to any one of claims 1-3, wherein the weather characteristic includes at least one of light intensity, rainfall, snowfall, or wind.
5. The method according to any one of claims 1 to 4, wherein the driving behavior characteristic includes at least one of a driving vehicle speed, a centripetal acceleration, an accelerator pedal opening, or a brake pedal opening.
6. The method according to any one of claims 1 to 5, wherein the determining of the road ahead feature comprises: determining a front road position; and determining the front road feature based on the front road position.
7. The method according to any one of claims 1-6, wherein the determining the position of the road ahead comprises: determining the position of the road ahead according to navigation information; or determining the position of the road ahead by predicting whether the travel is a conventional route.
8. The method according to any one of claims 1-7, wherein the driving behavior feature is used to characterize the driving habits of the driver.
9. A control device for assisting driving, characterized in that the device comprises:

   a road feature determination module for determining the road features ahead;

   a first coefficient determination module, configured to determine a first control coefficient corresponding to the front road feature based on a road model, the road model representing the correspondence between different road features and different first control coefficients;

   a weather feature determination module for determining the current weather feature;

   A second coefficient determination module, configured to determine a second control coefficient corresponding to the current weather feature based on a weather model, the weather model representing the correspondence between different weather features and different second control coefficients;

   An execution module, configured to execute assisted driving based on the first control coefficient, the second control coefficient and the driving behavior feature.
10. The device according to claim 9, wherein the driving assistance comprises a driving warning or a driving takeover.
11. The apparatus according to claim 9 or 10, wherein the road feature includes at least one of road type, road material, road gradient, or road curvature.
12. The apparatus according to any one of claims 9-11, wherein the weather characteristic includes at least one of light intensity, rainfall, snowfall, or wind.
13. The device according to any one of claims 9 to 12, wherein the driving behavior characteristic includes at least one of a driving speed, a centripetal acceleration, an accelerator pedal opening, or a brake pedal opening.
14. The device according to any one of claims 9 to 13, wherein the road feature determination module is specifically configured to: determine the position of the road ahead; and determine the feature of the road ahead based on the position of the road ahead.
15. The device according to any one of claims 9-14, wherein the road feature determination module is specifically configured to: determine the position of the road ahead according to navigation information; or determine the road ahead by prejudging whether the itinerary is a conventional route road location.
16. The device according to any one of claims 9-15, wherein the driving behavior feature is used to characterize the driving habits of the driver.
17. A computer-readable storage medium, characterized in that, the storage medium stores a computer program, and the computer program is used to execute the control method for assisting driving according to any one of the preceding claims 1-8.
18. An electronic device, characterized in that the electronic device comprises:

    processor;

    memory for storing computer-executable instructions;

    The processor is configured to execute the computer-executable instructions to drive the electronic device to implement the control method for assisted driving according to any one of the preceding claims 1-8.
19. A computer program product is characterized in that, when it is run on a computer, it causes the computer to execute the control method for assisted driving according to any one of the preceding claims 1-8.
20. A chip, characterized in that it includes a processor and an interface, the interface is used to read the processor-executable instructions from an external memory, and the processor is used to execute any one of the above claims 1-8. The control method for assisted driving described above.
21. A vehicle, characterized in that, the vehicle includes the control device for assisting driving according to any one of the above claims 9-16, or the electronic device according to the above claim 18.
22. A server, characterized in that, the server comprises the control device for assisting driving according to any one of the above claims 9-16, or comprises the electronic device according to the above claim 18.

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