CN107368069B - Automatic driving control strategy generation method and device based on Internet of vehicles - Google Patents
Automatic driving control strategy generation method and device based on Internet of vehicles Download PDFInfo
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Abstract
The invention provides a method and a device for generating an automatic driving control strategy based on Internet of vehicles, wherein the method comprises the following steps: acquiring environmental information generated by a vehicle environmental sensor and active driving information of a driver through the Internet of vehicles; obtaining a vehicle driving habit model of a corresponding vehicle according to the active driving information; obtaining a regional driving habit model according to a plurality of vehicle driving habit models in a geographic region; obtaining a road condition model of a geographic area in each time period according to a plurality of pieces of environment information; generating an automatic driving control strategy of the current vehicle according to the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located and the road condition model; updating the autonomous driving control strategy into an autonomous driving control system of the current vehicle. The aim of the invention is to adapt the automatic driving control strategy to the vehicle and its driving environment, thereby improving the comfort of automatic driving.
Description
Technical Field
The invention relates to the field of vehicle control, in particular to a method and a device for generating an automatic driving control strategy based on an internet of vehicles.
Background
With the development of vehicle technology, the automatic driving automobile has been gradually produced and applied. Since the control method and the driving method of the autonomous vehicle are greatly different from those of the human-driven vehicle, the driver feels discomfort regardless of the passenger seated in the autonomous vehicle or the driver in another vehicle traveling on the road around the autonomous vehicle. In addition, since the driving behavior habits of drivers vary from country to country and from region to region, it is not possible to adapt to all driving environments using the same automatic driving control strategy.
On the other hand, modern vehicle technology has developed a vehicle networking. The car networking is a network system for collecting and sharing information of cars through 3G, 4G and mobile internet. By utilizing the Internet of vehicles and processing information, the communication between vehicles and roads, between vehicles and vehicle owners, between vehicle owners and between vehicle owners and third-party service providers can be realized, and the life of the vehicles is more intelligent.
Disclosure of Invention
The invention aims to provide a method and a device for generating an automatic driving control strategy based on an internet of vehicles, so that the automatic driving control strategy is adaptive to a vehicle and a driving environment thereof, and the comfort of automatic driving is improved.
In order to achieve the above object, the present invention provides a method for generating an automatic driving control strategy based on internet of vehicles, including:
acquiring environmental information generated by a vehicle environmental sensor and active driving information of a driver through the Internet of vehicles;
obtaining a vehicle driving habit model of a corresponding vehicle according to the active driving information; obtaining a regional driving habit model according to a plurality of vehicle driving habit models in a geographic region; obtaining a road condition model of a geographic area in each time period according to a plurality of pieces of environment information;
generating an automatic driving control strategy of the current vehicle according to the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located and the road condition model;
updating the autonomous driving control strategy into an autonomous driving control system of the current vehicle.
Preferably, in the above method, the vehicle driving habit model comprises: the vehicle speed index, the vehicle brake index, the vehicle distance index and the vehicle line-changing overtaking index of the vehicle;
the regional driving habit model comprises: the regional vehicle speed index, the regional brake index, the regional vehicle distance index and the regional line-changing overtaking index;
the road condition model comprises: the road section vehicle density index, the road section average vehicle speed index, the road section bend index, the road section pavement index, the road section accident rate index and the road section red light intersection index.
Preferably, in the above method, in the step of generating the automatic driving control strategy of the current vehicle, a weight of the vehicle driving habit model is equal to a weight of the regional driving habit model.
Preferably, in the above method, the environment information includes: peripheral vehicle information, pedestrian information, lane line information, traffic sign information, and/or traffic signal information;
the active driving information includes: accelerator pedal opening, acceleration, brake deceleration, steering wheel angle, and/or vehicle yaw angle.
In order to better achieve the above object, the present invention further provides an automatic driving control strategy generation device based on internet of vehicles, including:
an information collection unit for: acquiring environmental information generated by a vehicle environmental sensor and active driving information of a driver through the Internet of vehicles;
a model unit for: obtaining a vehicle driving habit model of a corresponding vehicle according to the active driving information; obtaining a regional driving habit model according to a plurality of vehicle driving habit models in a geographic region; obtaining a road condition model of a geographic area in each time period according to a plurality of pieces of environment information; generating an automatic driving control strategy of the current vehicle according to the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located and the road condition model;
an update unit configured to: updating the autonomous driving control strategy into an autonomous driving control system of the current vehicle.
Preferably, in the above apparatus, the vehicle driving habit model includes: the vehicle speed index, the vehicle brake index, the vehicle distance index and the vehicle line-changing overtaking index of the vehicle;
the regional driving habit model comprises: the regional vehicle speed index, the regional brake index, the regional vehicle distance index and the regional line-changing overtaking index;
the road condition model comprises: the road section vehicle density index, the road section average vehicle speed index, the road section bend index, the road section pavement index, the road section accident rate index and the road section red light intersection index.
Preferably, in the above apparatus, in the step of generating the automatic driving control strategy of the current vehicle, a weight of the vehicle driving habit model is equal to a weight of the regional driving habit model.
Preferably, in the above apparatus, the environment information includes: peripheral vehicle information, pedestrian information, lane line information, traffic sign information, and/or traffic signal information;
the active driving information includes: accelerator pedal opening, acceleration, brake deceleration, steering wheel angle, and/or vehicle yaw angle.
In the invention, the communication between vehicles and roads, between vehicles and vehicle owners, between vehicle owners and between vehicle owners and third-party service providers can be realized through the Internet of vehicles, so that the environmental information and the active driving information of a plurality of vehicles in the area can be acquired through the Internet of vehicles. The regional driving habit models are obtained according to a plurality of vehicle driving habit models in a geographic region, and each vehicle driving habit model is a model which is obtained according to the active driving information of a corresponding driver and can simulate the driving habits of a corresponding vehicle, so that the regional driving habit models can simulate the driving habits of a plurality of vehicles in a geographic region, and when the vehicle driving habit models are abundant, the regional driving habit models can simulate the driving habits of most vehicles in a geographic region. Similarly, since the traffic model is obtained according to a plurality of environmental information in a geographic area, when the environmental information is sufficient, the traffic model can simulate the traffic condition of a geographic area in each time slot. According to the driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located and the road condition model, the automatic driving control strategy which is suitable for the current vehicle and the driving environment of the current vehicle can be obtained, and the automatic driving control strategy is updated to the automatic driving control system of the current vehicle, so that the current vehicle can be controlled to automatically run. In summary, the automatic driving control strategy obtained by the method not only considers the driving habits of the vehicle owner, but also considers the driving habits of other drivers in the area where the vehicle is located and the road conditions of all time periods in the area, so that the automatic driving control strategy obtained by the method can be adapted to the vehicle and the driving environment thereof, passengers and surrounding vehicles in the automatic driving vehicle cannot feel uncomfortable, the comfort of automatic driving is improved, and the automatic driving is more intelligent.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
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Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
FIG. 1 is a method flow diagram of a method of generating a vehicle networking based autonomous driving control strategy according to one embodiment of the present invention;
FIG. 2 is an apparatus schematic diagram of an apparatus for generating an Internet of vehicles based autonomous driving control strategy according to one embodiment of the present invention;
fig. 3 is a flowchart of the operation of the internet-of-vehicles based automatic driving control strategy generation apparatus according to an embodiment of the present invention.
Detailed Description
FIG. 1 is a method flow diagram of a method for generating a vehicle networking based autonomous driving control strategy according to one embodiment of the invention. As shown in fig. 1, an embodiment of the present invention provides a method for generating an automatic driving control strategy based on internet of vehicles, which at least includes steps S102 to S108.
And S102, acquiring environmental information generated by a vehicle environment sensor and active driving information of a driver through the Internet of vehicles.
Step S104, obtaining a vehicle driving habit model of the corresponding vehicle according to the active driving information; obtaining a regional driving habit model according to a plurality of vehicle driving habit models in a geographic region; and obtaining a road condition model of a geographic area in each time segment according to the plurality of environment information.
And S106, generating an automatic driving control strategy of the current vehicle according to the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located and the road condition model.
And step S108, updating the automatic driving control strategy into the automatic driving control system of the current vehicle.
In the invention, the communication between vehicles and roads, between vehicles and vehicle owners, between vehicle owners and between vehicle owners and third-party service providers can be realized through the Internet of vehicles, so that the environmental information and the active driving information of a plurality of vehicles in the area can be acquired through the Internet of vehicles. The regional driving habit models are obtained according to a plurality of vehicle driving habit models in a geographic region, and each vehicle driving habit model is a model which is obtained according to the active driving information of a corresponding driver and can simulate the driving habits of a corresponding vehicle, so that the regional driving habit models can simulate the driving habits of a plurality of vehicles in a geographic region, and when the vehicle driving habit models are abundant, the regional driving habit models can simulate the driving habits of most vehicles in a geographic region. Similarly, since the traffic model is obtained according to a plurality of environmental information in a geographic area, when the environmental information is sufficient, the traffic model can simulate the traffic condition of a geographic area in each time slot. According to the driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located and the road condition model, the automatic driving control strategy which is suitable for the current vehicle and the driving environment of the current vehicle can be obtained, and the automatic driving control strategy is updated to the automatic driving control system of the current vehicle, so that the current vehicle can be controlled to automatically run. In summary, the automatic driving control strategy obtained by the method not only considers the driving habits of the vehicle owner, but also considers the driving habits of other drivers in the area where the vehicle is located and the road conditions of all time periods in the area, so that the automatic driving control strategy obtained by the method can be adapted to the vehicle and the driving environment thereof, passengers and surrounding vehicles in the automatic driving vehicle cannot feel uncomfortable, the comfort of automatic driving is improved, and the automatic driving is more intelligent.
In step S102, the car networking is a network system for collecting and sharing car information through 3G, 4G or mobile internet. Environmental information and active driving information of a plurality of vehicles in the area can be acquired through the Internet of vehicles. By utilizing the Internet of vehicles and processing information, the communication between vehicles and roads, between vehicles and vehicle owners, between vehicle owners and between vehicle owners and third-party service providers can be realized, and the life of the vehicles is more intelligent.
In step S102, the environment information at least includes surrounding vehicle information, pedestrian information, lane line information, traffic sign information, and/or traffic signal information, and may further include information such as a vehicle position, a vehicle speed, and a driving path, where the surrounding vehicle information may be, but is not limited to, a surrounding vehicle position and speed, and the pedestrian information may be, but is not limited to, a surrounding pedestrian position and speed. The road condition information of the current vehicle can be reflected through the environment information. The vehicle environment sensor for acquiring the environment information may be, but is not limited to, an external environment sensor such as a vehicle front radar, a front camera, a side radar, a side camera, and the like, and the environment information may also be acquired through the vehicle communication device.
The active driving information includes at least accelerator pedal information, brake pedal information, steering wheel angle information, longitudinal acceleration, lateral acceleration, wherein the accelerator pedal information may be, but is not limited to, accelerator pedal opening, the steering wheel angle information may be, but is not limited to, steering wheel angle and/or vehicle yaw angle, and the brake pedal information may be, but is not limited to, brake deceleration. The driving habit and the control mode of the current vehicle can be reflected through the active driving information. Including but not limited to, obtaining active driving information via an engine control system, a braking system, a steering system, and an inertial measurement system.
In step S104, a vehicle driving habit model of a corresponding vehicle is obtained through the active driving information, a regional driving habit model is obtained according to a plurality of vehicle driving habit models in the same region, and a road condition model of the same region in each time period is obtained according to a plurality of environmental information, wherein the vehicle driving habit model can simulate the driving habit of the current vehicle, the regional driving habit model can simulate the driving habit of most vehicles in the region where the current vehicle is located, and the road condition model can simulate the road condition in each time period in the current region.
The vehicle driving habit model at least comprises: the vehicle speed index, the vehicle brake index, the vehicle distance index and the vehicle line-changing overtaking index of the vehicle; for example, the host vehicle speed index includes: the average speed per hour is 80 kilometers, and the highest speed per hour is 180 kilometers. The vehicle distance index is as follows: and the distance between the vehicle and the front vehicle is 30 meters when the speed per hour is 80 kilometers. The distance is 1 meter from the side car during overtaking. The lane change and overtaking index of the vehicle is for example; the speed of the front vehicle is less than 50 km, and the front vehicle overtakes the vehicle in a time-varying manner.
The regional driving habit model at least comprises: the regional vehicle speed index, the regional brake index, the regional vehicle distance index and the regional line-changing overtaking index; for example, the zone vehicle speed index includes: the average speed per hour is 60 kilometers, and the maximum speed per hour is 120 kilometers. The regional vehicle distance index is: at a speed of 80 km/h, the distance from the front vehicle is 40 m. The distance between the car and the side car is 1.5 meters during overtaking. The lane change and overtaking index of the vehicle is for example; the speed of the front vehicle is less than 40 km, and the front vehicle overtakes the vehicle in a time-varying manner.
The road condition model at least comprises: the road section vehicle density index, the road section average vehicle speed index, the road section bend index, the road section pavement index, the road section accident rate index and the road section red light intersection index.
In step S106, the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located, and the road condition model are comprehensively considered, so that the automatic driving control strategy adapted to the current vehicle and the driving environment thereof can be obtained.
In step S106, in order to comprehensively consider the vehicle driving habit model and the regional driving habit model, the weight of the vehicle driving habit model is set to be equal to the weight of the regional driving habit model, for example, when the average speed per hour in the vehicle driving habit model of the host vehicle is 80 km and the average speed per hour in the regional driving habit model is 60 km, the average speed per hour in the automatic driving control strategy may be set to 70 km, when the distance between the vehicle driving habit model of the host vehicle and the side vehicle during passing is 1 m, and the distance between the regional driving habit model and the side vehicle during passing is 1.5 m, the distance between the vehicle driving habit model and the side vehicle during passing may be set to 1.25 m. Therefore, the driving habits of the vehicle owners are considered, and the driving habits of other vehicle owners in the region are also considered.
In step S108, the automatic driving control strategy obtained in step S106 is updated to the automatic driving control system of the current vehicle, so that the automatic driving of the current vehicle can be controlled. The autonomous driving control strategy may be updated in real time or periodically.
The automatic driving control strategy obtained by the method not only considers the driving habits of the vehicle owner, but also considers the driving habits of other drivers in the area where the vehicle is located and the road conditions of all time periods, so the automatic driving control strategy obtained by the method can be adaptive to the vehicle and the driving environment thereof, passengers and surrounding vehicles in the automatic driving vehicle can not feel uncomfortable, the comfort of automatic driving is improved, and the automatic driving is more intelligent.
To further explain the method for generating the automatic driving control strategy based on the internet of vehicles in fig. 1, as shown in fig. 2, correspondingly, another embodiment of the present invention further provides a device for generating an automatic driving control strategy based on an internet of vehicles, which at least includes the following units.
An information collection unit 202 for: the environmental information generated by the vehicle environmental sensor and the active driving information of the driver are collected through the Internet of vehicles.
A model unit 204 for: obtaining a vehicle driving habit model of a corresponding vehicle according to the active driving information; obtaining a regional driving habit model according to a plurality of vehicle driving habit models in a geographic region; obtaining a road condition model of a geographic area in each time period according to a plurality of pieces of environmental information; and generating an automatic driving control strategy of the current vehicle according to the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located and the road condition model.
An updating unit 206, configured to: the autonomous driving control strategy is updated into the autonomous driving control system of the current vehicle.
In the present invention, in the information collection unit 202, since the internet of vehicles can implement communication between vehicles and roads, between vehicles and vehicle owners, between vehicle owners and vehicle owners, and between vehicle owners and third-party service providers, environmental information and active driving information of a plurality of vehicles in an area can be obtained through the internet of vehicles. In the model unit 204, since the regional driving habit model is obtained according to a plurality of vehicle driving habit models in a geographic region, and each vehicle driving habit model is a model capable of simulating the driving habit of a corresponding vehicle obtained according to the active driving information of the corresponding driver, the regional driving habit model can simulate the driving habits of a plurality of vehicles in a geographic region, and when the vehicle driving habit models are sufficiently numerous, the regional driving habit model can simulate the driving habits of most vehicles in a geographic region. Similarly, since the traffic model is obtained according to a plurality of environmental information in a geographic area, when the environmental information is sufficient, the traffic model can simulate the traffic condition of a geographic area in each time slot. In the present invention, in the model unit 204, an automatic driving control strategy adapted to the current vehicle and its driving environment can be obtained according to the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located, and the road condition model. In the updating unit 206, the automatic driving control strategy is updated to the automatic driving control system of the current vehicle, that is, the current vehicle can be controlled to automatically run. In summary, the automatic driving control strategy obtained by the method not only considers the driving habits of the vehicle owner, but also considers the driving habits of other drivers in the area where the vehicle is located and the road conditions of all time periods in the area, so that the automatic driving control strategy obtained by the device can be adapted to the vehicle and the driving environment thereof, passengers and surrounding vehicles in the automatic driving vehicle cannot feel uncomfortable, the comfort of automatic driving is improved, and the automatic driving is more intelligent.
In the information collecting unit 202, the car networking is a network system for collecting and sharing car information via 3G, 4G, or mobile internet. Environmental information and active driving information of a plurality of vehicles in the area can be acquired through the Internet of vehicles. By utilizing the Internet of vehicles and processing information, the communication between vehicles and roads, between vehicles and vehicle owners, between vehicle owners and between vehicle owners and third-party service providers can be realized, and the life of the vehicles is more intelligent.
In the information collecting unit 202, the environment information at least includes surrounding vehicle information, pedestrian information, lane line information, traffic sign information and/or traffic signal information, and may further include information such as a vehicle position, a vehicle speed and a driving path, wherein the surrounding vehicle information may be, but is not limited to, a surrounding vehicle position and speed, and the pedestrian information may be, but is not limited to, a surrounding pedestrian position and speed. The road condition information of the current vehicle can be reflected through the environment information. The vehicle environment sensor for acquiring the environment information may be, but is not limited to, an external environment sensor such as a vehicle front radar, a front camera, a side radar, a side camera, and the like, and the environment information may also be acquired through the vehicle communication device.
The active driving information includes at least accelerator pedal information, brake pedal information, steering wheel angle information, longitudinal acceleration, lateral acceleration, wherein the accelerator pedal information may be, but is not limited to, accelerator pedal opening, the steering wheel angle information may be, but is not limited to, steering wheel angle and/or vehicle yaw angle, and the brake pedal information may be, but is not limited to, brake deceleration. The driving habit and the control mode of the current vehicle can be reflected through the active driving information. Including but not limited to, obtaining active driving information via an engine control system, a braking system, a steering system, and an inertial measurement system.
In the model unit 204, a driving habit model of a vehicle is obtained through active driving information, a driving habit model of an area is obtained according to a plurality of driving habit models of vehicles in the same area, and a road condition model of the same area in each time slot is obtained according to a plurality of environment information, wherein the driving habit model of the vehicle can simulate the driving habit of the current vehicle, the driving habit model of the area can simulate the driving habit of most vehicles in the area where the current vehicle is located, and the road condition model can simulate the road condition in each time slot of the current area.
The vehicle driving habit model at least comprises: the vehicle speed index, the vehicle brake index, the vehicle distance index and the vehicle line-changing overtaking index of the vehicle; for example, the host vehicle speed index includes: the average speed per hour is 80 kilometers, and the highest speed per hour is 180 kilometers. The vehicle distance index is as follows: and the distance between the vehicle and the front vehicle is 30 meters when the speed per hour is 80 kilometers. The distance is 1 meter from the side car during overtaking. The lane change and overtaking index of the vehicle is for example; the speed of the front vehicle is less than 50 km, and the front vehicle overtakes the vehicle in a time-varying manner.
The regional driving habit model at least comprises: the regional vehicle speed index, the regional brake index, the regional vehicle distance index and the regional line-changing overtaking index; for example, the zone vehicle speed index includes: the average speed per hour is 60 kilometers, and the maximum speed per hour is 120 kilometers. The regional vehicle distance index is: at a speed of 80 km/h, the distance from the front vehicle is 40 m. The distance between the car and the side car is 1.5 meters during overtaking. Regional lane change cut-in indices such as; the speed of the front vehicle is less than 40 km, and the front vehicle overtakes the vehicle in a time-varying manner.
The road condition model at least comprises: the road section vehicle density index, the road section average vehicle speed index, the road section bend index, the road section pavement index, the road section accident rate index and the road section red light intersection index.
In the model unit 204, the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located, and the road condition model are comprehensively considered, so that the automatic driving control strategy adapted to the current vehicle and the driving environment thereof can be obtained.
In the model unit 204, in order to comprehensively consider the vehicle driving habit model and the regional driving habit model, the weight of the vehicle driving habit model is set to be equal to the weight of the regional driving habit model, for example, when the average speed per hour in the vehicle driving habit model of the host vehicle is 80 km and the average speed per hour in the regional driving habit model is 60 km, the average speed per hour in the automatic driving control strategy may be set to 70 km, when the vehicle driving habit model of the host vehicle is 1 meter from the side vehicle distance in overtaking, and the regional driving habit model is 1.5 meters from the side vehicle distance in overtaking, the side vehicle distance in the automatic driving control strategy may be set to 1.25 meters. Therefore, the driving habits of the vehicle owners are considered, and the driving habits of other vehicle owners in the region are also considered.
In the updating unit 206, the automatic driving control strategy obtained in the model unit 204 is updated to the automatic driving control system of the current vehicle, i.e. the automatic driving of the current vehicle can be controlled. The autonomous driving control strategy may be updated in real time or periodically.
The automatic driving control strategy obtained by the device not only considers the driving habits of the vehicle owner, but also considers the driving habits of other drivers in the area where the vehicle is located and the road conditions of all time periods, so the automatic driving control strategy obtained by the device can be adapted to the vehicle and the driving environment thereof, passengers and surrounding vehicles in the automatic driving vehicle can not feel uncomfortable, the comfort of automatic driving is improved, and the automatic driving is more intelligent.
To further illustrate the generation apparatus in fig. 2, in another embodiment of the present invention, a work flow of the generation apparatus of the automatic driving control strategy based on the internet of vehicles is further provided, and fig. 3 is a work flow chart of the generation apparatus of the automatic driving control strategy based on the internet of vehicles according to one embodiment of the present invention. As shown in fig. 3, the work flow of the generation apparatus of the internet-of-vehicles-based automatic driving control strategy includes at least steps S302 to S306.
And S302, collecting environmental information of the vehicle and surrounding vehicles and active driving information of a driver through the Internet of vehicles.
And S304, uploading the environment information and the active driving information to a cloud server through the Internet of vehicles, and carrying out mechanical learning by the cloud server according to the environment information and the active driving information to establish an automatic driving control strategy.
And S306, the cloud server downloads the automatic driving control strategy and the related parameters to the self vehicle through the Internet of vehicles.
In the embodiment of the present invention, in step S302, the internet of vehicles can implement communication between vehicles and roads, between vehicles and vehicle owners, between vehicle owners and vehicle owners, and between vehicle owners and third-party servers, so that environmental information obtained from vehicles and surrounding vehicles and active driving information obtained from vehicles and surrounding vehicles can be obtained through the internet of vehicles.
In step S302, the external environment sensors such as the front radar, the front camera, the side radar, and the side camera may be used to collect and record the environmental information such as the position, speed, and traveling path of the host vehicle, the position and speed of the peripheral pedestrian, the lane line, the traffic sign, and the traffic signal. The engine control system, the brake system, the steering system and the inertia measurement system are utilized to collect and record active driving information such as accelerator pedal information, brake pedal information, steering wheel corner information, longitudinal acceleration, transverse acceleration and the like.
In step S304, based on the communication functions of the vehicle and road, the vehicle and vehicle owner, the vehicle owner and vehicle owner, and the vehicle owner and third-party server of the internet of vehicles, the environmental information and the active driving information collected and recorded in step S302 are uploaded to the cloud server.
In step S304, the cloud server stores the environmental information and the active driving information of different vehicles in the same area to a unified database, and learns the environmental information and the active driving information within a period of time through mechanical learning (such as Boosting, SVM, and other learning methods), thereby establishing an automatic driving control strategy.
In step S306, the cloud server downloads the automatic driving control policy and the related parameters to the vehicle via the internet of vehicles, so as to update the automatic driving control system of the vehicle, so that the automatic driving control system of the vehicle is adapted to the vehicle and the driving environment.
As can be seen from the above, the cloud server is set as the model unit in the embodiment of the present invention, and the cloud server is used to store and learn the environmental information and the active driving information of different vehicles, so as to obtain the automatic driving control strategy. Based on the characteristics of the cloud server and the communication function of the Internet of vehicles, the environmental information and the active driving information are uploaded through the Internet of vehicles, and the automatic driving control strategy is downloaded to the self vehicle through the Internet of vehicles after being established, so that the updating of the automatic driving control strategy of the self vehicle is realized.
According to the embodiment of the invention, the automatic driving control strategy can be established and updated by utilizing the environmental information and the active driving information within a period of time, and the automatic driving control strategy can be established in real time by utilizing the environmental information and the active driving information in real time, so that the automatic control system can be updated in real time.
As can be seen from the above, in the embodiment of the present invention, in the information collecting unit 202, since the internet of vehicles can implement communication between vehicles and roads, between vehicles and vehicle owners, between vehicle owners and vehicle owners, and between vehicle owners and third-party service providers, environmental information and active driving information of a plurality of vehicles in an area can be obtained through the internet of vehicles. In the model unit 204, since the regional driving habit model is obtained according to a plurality of vehicle driving habit models in a geographic region, and each vehicle driving habit model is a model capable of simulating the driving habit of a corresponding vehicle obtained according to the active driving information of the corresponding driver, the regional driving habit model can simulate the driving habits of a plurality of vehicles in a geographic region, and when the vehicle driving habit models are sufficiently numerous, the regional driving habit model can simulate the driving habits of most vehicles in a geographic region. Similarly, since the traffic model is obtained according to a plurality of environmental information in a geographic area, when the environmental information is sufficient, the traffic model can simulate the traffic condition of a geographic area in each time slot. In the present invention, in the model unit 204, an automatic driving control strategy adapted to the current vehicle and its driving environment can be obtained according to the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located, and the road condition model. In the updating unit 206, the automatic driving control strategy is updated to the automatic driving control system of the current vehicle, that is, the current vehicle can be controlled to automatically run. In summary, the automatic driving control strategy obtained by the method not only considers the driving habits of the vehicle owner, but also considers the driving habits of other drivers in the area where the vehicle is located and the road conditions of all time periods in the area, so that the automatic driving control strategy obtained by the device can be adapted to the vehicle and the driving environment thereof, passengers and surrounding vehicles in the automatic driving vehicle cannot feel uncomfortable, the comfort of automatic driving is improved, and the automatic driving is more intelligent.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (4)
1. A method for generating an automatic driving control strategy based on Internet of vehicles is characterized by comprising the following steps:
acquiring environmental information generated by a vehicle environmental sensor and active driving information of a driver through the Internet of vehicles;
obtaining a vehicle driving habit model of a corresponding vehicle according to the active driving information; obtaining a regional driving habit model according to a plurality of vehicle driving habit models in a geographic region; obtaining a road condition model of a geographic area in each time period according to a plurality of pieces of environment information;
generating an automatic driving control strategy of the current vehicle according to the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located and the road condition model;
updating the autonomous driving control strategy into an autonomous driving control system of the current vehicle;
in the step of generating the automatic driving control strategy of the current vehicle, the weight of the vehicle driving habit model is equal to the weight of the regional driving habit model;
the road condition model comprises: the road section vehicle density index, the road section average vehicle speed index, the road section bend index, the road section pavement index, the road section accident rate index and the road section red light intersection index.
2. The method of claim 1,
the environment information includes: peripheral vehicle information, pedestrian information, lane line information, traffic sign information, and/or traffic signal information;
the active driving information includes: accelerator pedal opening, acceleration, brake deceleration, steering wheel angle, and/or vehicle yaw angle.
3. An apparatus for generating an autonomous driving control strategy based on a vehicle networking, comprising:
an information collection unit for: acquiring environmental information generated by a vehicle environmental sensor and active driving information of a driver through the Internet of vehicles;
a model unit for: obtaining a vehicle driving habit model of a corresponding vehicle according to the active driving information; obtaining a regional driving habit model according to a plurality of vehicle driving habit models in a geographic region; obtaining a road condition model of a geographic area in each time period according to a plurality of pieces of environment information; generating an automatic driving control strategy of the current vehicle according to the vehicle driving habit model of the current vehicle, the regional driving habit model of the region where the current vehicle is located and the road condition model;
an update unit configured to: updating the autonomous driving control strategy into an autonomous driving control system of the current vehicle;
in the step of generating the automatic driving control strategy of the current vehicle, the weight of the vehicle driving habit model is equal to the weight of the regional driving habit model;
the road condition model comprises: the road section vehicle density index, the road section average vehicle speed index, the road section bend index, the road section pavement index, the road section accident rate index and the road section red light intersection index.
4. The generation apparatus according to claim 3,
the environment information includes: peripheral vehicle information, pedestrian information, lane line information, traffic sign information, and/or traffic signal information;
the active driving information includes: accelerator pedal opening, acceleration, brake deceleration, steering wheel angle, and/or vehicle yaw angle.
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Families Citing this family (47)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160341555A1 (en) * | 2015-05-20 | 2016-11-24 | Delphi Technologies, Inc. | System for auto-updating route-data used by a plurality of automated vehicles |
| CN104963779B (en) * | 2015-06-11 | 2017-06-16 | 奇瑞汽车股份有限公司 | Vehicle drive control apparatus |
| CN105446338B (en) * | 2015-12-21 | 2017-04-05 | 福州华鹰重工机械有限公司 | Cloud aids in automatic Pilot method and system |
| CN105844913A (en) * | 2016-04-15 | 2016-08-10 | 苏州爱诺信信息科技有限公司 | Correlation analyzing method based on vehicle, road conditions and safe travel big data in network of vehicles |
| JP2017200786A (en) * | 2016-05-02 | 2017-11-09 | 本田技研工業株式会社 | Vehicle control system, vehicle control method and vehicle control program |
| CN106066644A (en) | 2016-06-17 | 2016-11-02 | 百度在线网络技术（北京）有限公司 | Set up the method for intelligent vehicle control model, intelligent vehicle control method and device |
| CN106209777A (en) * | 2016-06-24 | 2016-12-07 | 韩磊 | A kind of automatic driving car on-vehicle information interactive system and safety communicating method |
| DE102016116859A1 (en) | 2016-09-08 | 2018-03-08 | Knorr-Bremse Systeme für Nutzfahrzeuge GmbH | Sensor arrangement for an autonomously operated commercial vehicle and a method for round imaging |
| CN106347359B (en) * | 2016-09-14 | 2019-03-12 | 北京百度网讯科技有限公司 | Method and apparatus for operating automatic driving vehicle |
| US10539966B2 (en) * | 2016-09-28 | 2020-01-21 | Denso Corporation | Service cooperation system for vehicle |
| US10705536B2 (en) * | 2016-11-22 | 2020-07-07 | Baidu Usa Llc | Method and system to manage vehicle groups for autonomous vehicles |
| CN109476310B (en) * | 2016-12-30 | 2021-11-12 | 同济大学 | Automatic driving vehicle speed control method based on comfort level |
| CN108462726A (en) * | 2017-02-14 | 2018-08-28 | 广州市联奥信息科技有限公司 | Vehicle assistant drive decision system and device towards unknown situation |
| CN108508881B (en) * | 2017-02-27 | 2022-05-27 | 北京百度网讯科技有限公司 | Automatic driving control strategy adjusting method, device, equipment and storage medium |
| DE112017007596T5 (en) * | 2017-06-02 | 2020-02-20 | Honda Motor Co., Ltd. | Strategy generator and vehicle |
| CN107479549A (en) * | 2017-08-14 | 2017-12-15 | 苏州世纪天成信息技术有限公司 | One kind is based on unpiloted intelligent vehicle speed management method and its system |
| WO2019037125A1 (en) * | 2017-08-25 | 2019-02-28 | 深圳市得道健康管理有限公司 | Artificial intelligence terminal and behavior control method thereof |
| WO2019037123A1 (en) * | 2017-08-25 | 2019-02-28 | 深圳市得道健康管理有限公司 | Artificial intelligence terminal and behavior control method thereof |
| CN107943057B (en) * | 2017-12-25 | 2021-06-22 | 深圳市豪位科技有限公司 | Multi-automobile interaction automatic control system |
| CN108646737A (en) * | 2018-05-03 | 2018-10-12 | 韩静超 | A kind of novel intelligent highway transport vehicle control system and its control method |
| CN108766005A (en) * | 2018-06-08 | 2018-11-06 | 北京洪泰同创信息技术有限公司 | The control method of the vehicles, apparatus and system |
| JP6914229B2 (en) * | 2018-07-09 | 2021-08-04 | 株式会社日立製作所 | Automatic driving support device and its method |
| CN110824912B (en) * | 2018-08-08 | 2021-05-18 | 华为技术有限公司 | Method and apparatus for training a control strategy model for generating an autonomous driving strategy |
| CN108827334A (en) * | 2018-08-09 | 2018-11-16 | 北京智行者科技有限公司 | A kind of automatic Pilot method |
| JP7167732B2 (en) * | 2018-11-05 | 2022-11-09 | トヨタ自動車株式会社 | map information system |
| CN109597412A (en) * | 2018-12-06 | 2019-04-09 | 江苏萝卜交通科技有限公司 | A kind of Unmanned Systems and its control method |
| CN109618136B (en) * | 2018-12-14 | 2022-01-18 | 北京汽车集团有限公司 | Vehicle management system and method |
| CN111376911A (en) * | 2018-12-29 | 2020-07-07 | 北京宝沃汽车有限公司 | Vehicle and driving style self-learning method and device thereof |
| CN109808706A (en) * | 2019-02-14 | 2019-05-28 | 上海思致汽车工程技术有限公司 | Learning type assistant driving control method, device, system and vehicle |
| CN109801534A (en) * | 2019-02-19 | 2019-05-24 | 上海思致汽车工程技术有限公司 | Driving behavior hardware-in-the-loop test system based on automatic Pilot simulator |
| CN109795506B (en) * | 2019-02-26 | 2021-04-02 | 百度在线网络技术（北京）有限公司 | Driving control method and device and vehicle |
| CN109839937B (en) * | 2019-03-12 | 2023-04-07 | 百度在线网络技术（北京）有限公司 | Method, device and computer equipment for determining automatic driving planning strategy of vehicle |
| CN110058588B (en) * | 2019-03-19 | 2021-07-02 | 驭势科技（北京）有限公司 | Method for upgrading automatic driving system, automatic driving system and vehicle-mounted equipment |
| CN110069064B (en) * | 2019-03-19 | 2021-01-29 | 驭势科技（北京）有限公司 | Method for upgrading automatic driving system, automatic driving system and vehicle-mounted equipment |
| CN111845769B (en) * | 2019-04-25 | 2022-02-22 | 广州汽车集团股份有限公司 | Vehicle driving method and device, computer equipment and vehicle |
| CN110532846B (en) * | 2019-05-21 | 2022-09-16 | 华为技术有限公司 | Automatic channel changing method, device and storage medium |
| CN110488814A (en) * | 2019-07-30 | 2019-11-22 | 深圳市前海胡桃科技有限公司 | A kind of automatic Pilot method, apparatus and terminal device |
| CN112389451A (en) * | 2019-08-14 | 2021-02-23 | 大众汽车（中国）投资有限公司 | Method, device, medium, and vehicle for providing a personalized driving experience |
| CN110989569B (en) * | 2019-11-20 | 2022-06-14 | 华为技术有限公司 | Vehicle running control method and related equipment |
| CN110901402B (en) * | 2019-12-23 | 2021-02-09 | 南昌工程学院 | Intelligent electric automobile brake control method |
| CN111798686A (en) * | 2020-07-02 | 2020-10-20 | 重庆金康动力新能源有限公司 | Method and system for outputting exclusive road condition driving mode according to driving preference of driver |
| CN112109715B (en) * | 2020-09-14 | 2022-06-14 | 华人运通(上海)云计算科技有限公司 | Method, device, medium and system for generating vehicle power output strategy |
| CN112141102B (en) * | 2020-09-24 | 2022-02-15 | 阿波罗智能技术(北京)有限公司 | Cruise control method, cruise control device, cruise control apparatus, vehicle, and cruise control medium |
| CN113071505B (en) * | 2021-04-16 | 2024-02-13 | 阿波罗智联(北京)科技有限公司 | Method, device and equipment for determining driving behavior habit and controlling vehicle running |
| CN113903102B (en) * | 2021-10-29 | 2023-11-17 | 广汽埃安新能源汽车有限公司 | Adjustment information acquisition method, adjustment device, electronic equipment and medium |
| CN114056352A (en) * | 2021-12-24 | 2022-02-18 | 上海海积信息科技股份有限公司 | Automatic driving control device and vehicle |
| CN115056798B (en) * | 2022-05-30 | 2024-04-09 | 天津大学 | Automatic driving vehicle lane change behavior vehicle-road collaborative decision algorithm based on Bayesian game |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103295424A (en) * | 2013-06-28 | 2013-09-11 | 公安部第三研究所 | Automobile active safety system based on video recognition and vehicle ad-hoc network |
| WO2013180206A1 (en) * | 2012-05-30 | 2013-12-05 | 日立オートモティブシステムズ株式会社 | Vehicle control device |
| WO2014149376A2 (en) * | 2013-03-15 | 2014-09-25 | Abalta Technologies, Inc. | Vehicle range projection |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE60121963T2 (en) * | 2001-10-15 | 2007-01-18 | Ford Global Technologies, LLC, Dearborn | Method and device for controlling a vehicle |
| DE10334620B4 (en) * | 2003-07-30 | 2023-07-06 | Robert Bosch Gmbh | Generation of traffic information by interpreting traffic sign scenarios and navigation information in a vehicle |
| JP4367431B2 (en) * | 2005-10-26 | 2009-11-18 | トヨタ自動車株式会社 | Vehicle driving support system |
| US20110320113A1 (en) * | 2010-06-25 | 2011-12-29 | Gm Global Technology Operations, Inc. | Generating driving route traces in a navigation system using a probability model |
| DE102011121948A1 (en) * | 2011-12-22 | 2013-06-27 | Gm Global Technology Operations, Llc | Perspective on actions of an autonomous driving system |
| CN102589557B (en) * | 2012-01-13 | 2014-12-03 | 吉林大学 | Intersection map matching method based on driver behavior characteristics and logit model |
| CN102665294B (en) * | 2012-04-25 | 2014-09-03 | 武汉大学 | Vehicular sensor networks (VSN) event region detection method based on Dempster-Shafer (D-S) evidence theory |
| US9085303B2 (en) * | 2012-11-15 | 2015-07-21 | Sri International | Vehicle personal assistant |
| CN103496366B (en) * | 2013-09-09 | 2016-02-24 | 北京航空航天大学 | A kind of initiative lane change collision avoidance control method based on collaborative truck and device |
| CN103531042B (en) * | 2013-10-25 | 2015-08-19 | 吉林大学 | Based on the vehicle rear-end collision method for early warning of driver's type |
| CN103646298B (en) * | 2013-12-13 | 2018-01-02 | 中国科学院深圳先进技术研究院 | A kind of automatic Pilot method and system |
| CN103996312B (en) * | 2014-05-23 | 2015-12-09 | 北京理工大学 | There is the pilotless automobile control system that social action is mutual |
| CN103996298B (en) * | 2014-06-09 | 2019-05-24 | 百度在线网络技术（北京）有限公司 | A kind of driver behavior modeling method and device |
| CN104064050B (en) * | 2014-06-30 | 2015-12-30 | 科大讯飞股份有限公司 | Automated driving system and method |
-
2014
- 2014-11-25 CN CN201710418934.0A patent/CN107368069B/en active Active
- 2014-11-25 CN CN201410686677.5A patent/CN104391504B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013180206A1 (en) * | 2012-05-30 | 2013-12-05 | 日立オートモティブシステムズ株式会社 | Vehicle control device |
| WO2014149376A2 (en) * | 2013-03-15 | 2014-09-25 | Abalta Technologies, Inc. | Vehicle range projection |
| CN103295424A (en) * | 2013-06-28 | 2013-09-11 | 公安部第三研究所 | Automobile active safety system based on video recognition and vehicle ad-hoc network |
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