Automatic driving system of vehicle and automatic driving method of vehicle

Abstract

The invention discloses an automatic driving system of a vehicle and an automatic driving method of the vehicle, and relates to the technical field of vehicles. The automatic driving system comprises a road condition prediction module, an energy efficiency optimization module and a safety guarantee module. The road condition prediction module is used for determining road condition prediction information according to the vehicle driving information, the vehicle driving information is used for indicating weather information and traffic information of each road section, and the road condition prediction information is used for predicting the road condition of each road section; the energy efficiency optimization module is used for determining a vehicle driving route and a vehicle driving mode according to the vehicle position information and the road condition prediction information; the safety guarantee module is used for adjusting an executing mechanism of the vehicle according to the vehicle environment information, and the vehicle is used for driving according to the adjusted executing mechanism and the vehicle driving route and the vehicle driving mode. The vehicle driving route and the vehicle driving mode are obtained by predicting the road condition, the unnecessary driving distance in the vehicle driving process is reduced, and the energy consumption is reduced.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to an automatic driving system for a vehicle and an automatic driving method for a vehicle. Background Art

[0002] With the rapid development of autonomous driving technology, improving the energy efficiency of autonomous vehicles, reducing the impact of vehicles on the environment, and enhancing driving safety have become research hotspots.

[0003] In related technologies, traditional autonomous driving systems mainly rely on sensor data for real-time decision-making. However, in complex and changeable traffic environments, traditional autonomous driving systems usually travel along a predetermined route. When traffic jams occur, they will choose to follow the vehicle step by step. When the vehicle cannot move forward due to force majeure during the driving route, the system will re-plan the driving route when the vehicle reaches a point where it cannot move forward.

[0004] However, the above-mentioned autonomous driving system lacks accurate prediction of future road conditions and cannot flexibly handle emergencies on a given route, which can easily lead to an increase in vehicle energy consumption during driving. Summary of the invention

[0005] The embodiment of the present application provides an automatic driving system for a vehicle and an automatic driving method for a vehicle. The technical solution provided by the embodiment of the present application is as follows:

[0006] According to one aspect of an embodiment of the present application, there is provided an automatic driving system for a vehicle, the automatic driving system comprising a road condition prediction module, an energy efficiency optimization module and a safety assurance module;

[0007] The road condition prediction module is used to determine road condition prediction information according to vehicle driving information, wherein the vehicle driving information is used to indicate weather information and traffic information of each road section, and the road condition prediction information is used to predict the road condition of each road section;

[0008] The energy efficiency optimization module is used to determine the vehicle driving route and vehicle driving mode according to the vehicle position information and the road condition prediction information, wherein the vehicle position information is used to indicate the starting position and the end position of the vehicle driving;

[0009] The safety assurance module is used to adjust the actuator of the vehicle according to vehicle environment information, and the vehicle environment information is used to indicate the driving environment of the vehicle. The vehicle is used to drive according to the vehicle driving route and the vehicle driving mode based on the adjusted actuator.

[0010] According to one aspect of an embodiment of the present application, a method for automatic driving of a vehicle is provided, the method comprising:

[0011] Acquire vehicle location information and vehicle travel information, wherein the vehicle location information is used to indicate the starting position and the ending position of the vehicle travel, and the vehicle travel information is used to indicate weather information and traffic information of each road section;

[0012] Determining road condition prediction information according to the vehicle driving information, wherein the road condition prediction information is used to predict the road condition of each road section;

[0013] Determining a vehicle driving route and a vehicle driving mode according to the vehicle position information and the road condition prediction information;

[0014] Acquiring vehicle environment information, where the vehicle environment information is used to indicate a driving environment of the vehicle;

[0015] The actuator of the vehicle is adjusted according to the vehicle environment information, and the vehicle is used to travel according to the vehicle driving route and the vehicle driving mode based on the adjusted actuator.

[0016] The technical solution provided in the embodiments of the present application can bring the following beneficial effects:

[0017] By collecting vehicle driving information in real time during driving, the road conditions are predicted, so that the vehicle can avoid congested sections according to the starting and ending positions of the vehicle, dynamically plan the vehicle route, reduce unnecessary driving distance during driving, and shorten the vehicle's driving time. At the same time, according to the road condition prediction information, the optimal driving mode is matched for different sections of the vehicle to reduce energy consumption during driving and optimize energy utilization. The vehicle environment information is collected in real time during driving, and the vehicle's actuator is adjusted so that the vehicle can respond to different driving environments based on the adjusted actuator to ensure driving safety and achieve precise control of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic diagram of the structure of an automatic driving system for a vehicle provided by an embodiment of the present application;

[0019] Figure 2 This is a flowchart of an automatic driving method for a vehicle provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0021] Please refer to Figure 1 , which shows a schematic diagram of the structure of an automatic driving system for a vehicle provided in one embodiment of the present application.

[0022] The automatic driving system provided in this application can be applied to fuel vehicles or electric vehicles, and this application does not limit it. A fuel vehicle is a vehicle that uses fuel as a power source and an internal combustion engine to drive the wheels. An electric vehicle is a vehicle that uses an onboard power supply as a power source and uses an electric motor to drive the wheels. An electric vehicle can be a pure electric vehicle (Battery Electric Vehicle, BEV), a hybrid electric vehicle (Hybrid Electric Vehicle, HEV), or a fuel cell vehicle (Fuel Cell Electric Vehicle, FCEV).

[0023] The automatic driving system is an intelligent car system that realizes unmanned driving through the on-board computer system. It uses advanced communication technology, computer technology, network technology and control technology to control the vehicle in real time and continuously, so that the server can automatically and safely operate the vehicle without any human active operation. The automatic driving system is a fully automated and highly centralized vehicle operation system, which has the functions of automatic vehicle wake-up, start and sleep, automatic parking, automatic cleaning, automatic driving, automatic parking, automatic door opening and closing, automatic fault recovery, etc. The realization of fully automatic operation can save energy and optimize the reasonable matching of system energy consumption and speed.

[0024] The automatic driving system includes a road condition prediction module 10 , an energy efficiency optimization module 20 and a safety assurance module 30 .

[0025] The road condition prediction module 10 is an integrated controller. The energy efficiency optimization module 20 and the road condition prediction module 10 are connected by hard wires, and the energy efficiency optimization module 20 and the road condition prediction module 10 exchange data through the Ethernet protocol. The energy efficiency optimization module 20 and the safety assurance module 30 are connected by hard wires, and the energy efficiency optimization module 20 and the safety assurance module 30 exchange data through the Ethernet protocol.

[0026] The road condition prediction module 10 is used to determine road condition prediction information according to vehicle driving information. The vehicle driving information is used to indicate weather information and traffic information of each road section. The road condition prediction information is used to predict the road condition of each road section.

[0027] The vehicle driving information is information obtained in real time during the driving process of the vehicle. The vehicle driving information obtained at different times may be the same or different. The road condition prediction module 10 is used to update the road condition prediction information in real time according to the vehicle driving information obtained in real time. The road condition prediction information obtained at different times may be the same or different.

[0028] Weather information is used to indicate the weather conditions in the area where the vehicle is currently located. For example, weather information can be used to indicate the weather conditions in the city where the vehicle is currently located, and the weather information obtained in real time is used to indicate the weather conditions in the city where the vehicle is currently located at different times. Weather information can also be used to indicate the weather conditions in a certain area of ​​the city where the vehicle is currently located, and the weather information obtained in real time can be the weather conditions in different areas of the city where the vehicle is currently located.

[0029] Traffic information is used to indicate the traffic conditions in the area where the vehicle is currently located, including real-time traffic conditions and historical traffic conditions. For example, traffic information can be used to indicate the real-time traffic conditions and historical traffic conditions in the city where the vehicle is currently located, or can be used to indicate the real-time traffic conditions and historical traffic conditions in a certain area of ​​the city where the vehicle is currently located.

[0030] The road condition prediction information is used to predict the road condition of each road section. Specifically, the road condition prediction information is used to indicate the traffic information of each road section in the future time period. The length of the future time period is not limited in this application. For example, the future time period can be any time period such as 10 minutes, 1 hour, 2 hours, 1 day, etc. Moreover, the road section predicted by the road condition prediction information is determined from the weather information and the traffic information, and is a road section included in both the weather information and the traffic information. For example, the weather information is the weather information for road section 1, road section 2 and road section 3, and the traffic information is the traffic information for road section 1 and road section 2, then the road condition prediction information is used to predict the road condition of road section 1 and road section 2.

[0031] The energy efficiency optimization module 20 is used to determine the vehicle's driving route and vehicle driving mode according to the vehicle's position information and road condition prediction information. The vehicle's position information is used to indicate the starting position and the end position of the vehicle's driving.

[0032] The starting position of the vehicle refers to the current position of the vehicle, and the end position of the vehicle refers to the target position set by the user before the vehicle travels. During the driving process of the vehicle, the vehicle position information changes in real time, so it is necessary to obtain the vehicle position information in real time during the driving process of the vehicle. The energy efficiency optimization module 20 is used to update the vehicle driving route and vehicle driving mode in real time according to the real-time obtained vehicle position information and road condition prediction information.

[0033] The vehicle driving route is the optimal driving route dynamically planned by the energy efficiency optimization module 20 through computer technology. The vehicle driving route is used to avoid road sections with traffic congestion to reduce unnecessary driving distance and driving time.

[0034] The vehicle driving mode includes one of the following: sports mode (SPORT), economic mode (ECO) and standard mode (NORMAL). The vehicle driving mode is the optimal driving mode for the vehicle based on the vehicle's driving route. The vehicle driving mode is used to optimize energy utilization, reduce energy consumption, and improve the vehicle's energy utilization efficiency and vehicle driving performance.

[0035] In some embodiments, different sections of the vehicle's driving route can be matched with different vehicle driving modes, so that the vehicle can achieve the minimum energy consumption when the driving state of each section is normal. For example, if the vehicle's driving route includes section 1, section 2, and section 3, section 1 can be matched with the sports mode, section 2 can be matched with the economy mode, and section 3 can be matched with the standard mode.

[0036] The safety assurance module 30 is used to adjust the actuators of the vehicle according to the vehicle environment information. The vehicle environment information is used to indicate the driving environment of the vehicle. The vehicle is used to drive according to the vehicle driving route and vehicle driving mode according to the adjusted actuators.

[0037] The vehicle environment information is the environment information around the vehicle acquired in real time during the driving process, and is used to indicate the driving environment the vehicle is in. The vehicle environment information acquired at different times is different.

[0038] The safety assurance module 30 is used to adjust the vehicle's actuators in real time according to the vehicle environment information obtained in real time, so that the vehicle can travel according to the adjusted actuators, the vehicle's route and the vehicle's driving mode, so that the vehicle can cope with various complex road conditions and emergencies, ensure driving safety, and achieve precise control of the vehicle.

[0039] The technical solution provided in the embodiment of the present application predicts the road conditions by collecting the vehicle driving information in real time during the driving process, so that the vehicle can avoid congested sections according to the starting position and the end position of the vehicle, dynamically plan the vehicle driving route, reduce unnecessary driving distance during the vehicle driving process, and shorten the vehicle driving time. At the same time, according to the road condition prediction information, the optimal driving mode is matched for different sections of the vehicle, the energy consumption during the vehicle driving process is reduced, and the energy utilization is optimized. The vehicle environment information during the driving process of the vehicle is collected in real time, and the vehicle's actuator is adjusted, so that the vehicle can respond to different driving environments based on the adjusted actuator to ensure driving safety and achieve precise control of the vehicle.

[0040] Next, an autonomous driving solution for an autonomous driving system applied to a vehicle is introduced.

[0041] In some embodiments, the road condition prediction module 10 includes a data acquisition unit 11 and a model prediction unit 12. The data acquisition unit 11 is used to obtain vehicle driving information; the model prediction unit 12 is used to determine road condition prediction information based on the vehicle driving information through a road condition prediction model, and the road condition prediction information is used to indicate the traffic information of each road section in the future time period.

[0042] The data acquisition unit 11 and the model prediction unit 12 are connected by hard wire inside the road condition prediction module 10, and the data acquisition unit 11 and the model prediction unit 12 perform signal transmission via the CAN-FD (Controller Area Network with Flexible Data-Rate) protocol.

[0043] The data acquisition unit 11 is connected to the server through the vehicle-mounted T-Box (Telematics Box, vehicle networking system). The server obtains vehicle driving information in real time and sends the real-time acquired vehicle driving information to the data acquisition unit 11. The data acquisition unit 11 receives the vehicle driving information and converts the vehicle driving information into internal signal data of the vehicle and sends it to the model prediction unit 12.

[0044] In some embodiments, the vehicle driving information includes at least one of the following: weather forecast information, real-time traffic information, and historical traffic information. Weather forecast information includes real-time weather information, road flooding information, and disaster forecast information; real-time traffic information includes traffic control information, road congestion information, and accident vehicle information; historical traffic information includes historical traffic flow information and historical accident information.

[0045] The weather forecast information may be weather forecast information obtained based on the weather forecast of the area where the vehicle is currently located. Optionally, the real-time weather information may refer to the weather information of the area where the vehicle is currently located on the day, or may refer to the weather information of the area where the vehicle is currently located, or may refer to the weather information of the area where the vehicle is located every first time period. If the real-time weather information refers to the weather information of the area where the vehicle is located every first time period, the length of the first time period is not limited in this application. For example, the first time period may be any time period such as 10 minutes, 1 hour, 2 hours, etc. For example, the real-time weather information may refer to the weather information of the area where the vehicle is located every 1 hour.

[0046] The road section waterlogging information includes the road section real-time waterlogging information and the road section predicted waterlogging information. The road section real-time waterlogging information is used to indicate the current waterlogging situation of each road section, including the current waterlogging area of ​​each road section and the degree of waterlogging in each area. The road section predicted waterlogging information is used to indicate the future waterlogging situation of each road section, including the future waterlogging area of ​​each road section and the degree of waterlogging in each area in the future. In some embodiments, the road section predicted waterlogging information refers to the waterlogging situation of each road section every second time period. The length of the second time period is not limited in this application. For example, the second time period can be any time period such as 10 minutes, 1 hour, 2 hours, etc. Exemplarily, the road section predicted waterlogging information can refer to the waterlogging situation of each road section every 1 hour.

[0047] The disaster prediction information is used to indicate the prediction information of natural disasters in the area where the vehicle is located, including information such as the disaster start time, disaster duration, disaster end time, and disaster impact. Natural disasters include, but are not limited to, typhoons, earthquakes, mudslides, floods, etc. For example, if the disaster prediction information is prediction information for a typhoon, the disaster prediction information includes information such as the typhoon arrival time, typhoon duration, typhoon departure time, typhoon wind force corresponding to different times, and typhoon maximum wind force.

[0048] Traffic control information is used to indicate the traffic control situation of each road section, including information such as speed limit information, traffic flow limit information, license plate number limit information, and traffic police control locations. Road section congestion information is used to indicate the congestion situation of each road section, including the cause of road section congestion, road section congestion duration, road section congestion traffic volume, road section congestion distance, etc. Accident vehicle information is used to indicate the accident situation of each road section, including the location of the accident, the type of accident, the traffic volume at the accident location, the traffic control information at the accident location, and the estimated time for handling the accident.

[0049] Historical traffic information is used to indicate the traffic volume of each road section within a historical time period. In some embodiments, historical traffic information refers to the traffic volume of each road section every third time period within the historical time period. This application does not limit the length of the third time period and the length of the historical time period. For example, the third time period can be any time period such as 10 minutes, 1 hour, 2 hours, 1 day, etc., and the historical time period can be any time period such as 1 week, 1 month, 3 months, etc. For example, historical traffic information can refer to the traffic volume of each road section every 1 hour within a historical month.

[0050] The historical accident information is used to indicate the accident situation of each road section in the historical time period, including the accident location, accident type, traffic volume at the accident location, traffic control information at the accident location, accident handling time, etc. of each road section in the historical time period. In some embodiments, the historical accident information refers to the accident information of each road section every third time period in the historical time period. For example, the historical accident information may refer to the accident information of each road section every hour within a historical month.

[0051] The model prediction unit 12 obtains road condition prediction information based on the vehicle driving information through the pre-built road condition prediction model. The road condition prediction model is a high-precision model built using deep learning, machine learning and other algorithms combined with big data analysis technology to predict the traffic conditions of each road section in the future time period.

[0052] In some embodiments, the road condition prediction information includes at least one of the following: traffic flow information of each road section in a future time period; congestion information of each road section in a future time period; accident risk information of each road section in a future time period.

[0053] The traffic flow information of each road section in the future time period is used to indicate the traffic volume of each road section in the future time period. In some embodiments, the traffic flow information of each road section in the future time period refers to the traffic volume of each road section every fourth time period in the future time period. The length of the fourth time period is not limited in this application. For example, the fourth time period can be any time period such as 10 minutes, 1 hour, 2 hours, etc. Exemplarily, the traffic flow information of each road section in the future time period can refer to the traffic volume of each road section every 1 hour in the next 1 day.

[0054] The congestion information of each road section in the future time period is used to indicate the congestion situation of each road section in the future time period, including the cause of the road section congestion, the duration of the road section congestion, the road section congestion traffic volume, the road section congestion distance, and other information of each road section in the future time period. In some embodiments, the congestion information of each road section in the future time period refers to the congestion information of each road section every fourth time period in the future time period. For example, the congestion information of each road section in the future time period refers to the congestion information of each road section every 1 hour in the next 1 day.

[0055] The accident risk information of each road section in the future time period is used to indicate the accident occurrence of each road section in the future time period, including the accident location, accident type, traffic volume at the accident location, traffic control information at the accident location, accident handling time, etc. of each road section in the future time period. In some embodiments, the accident risk information of each road section in the future time period refers to the accident information of each road section every fourth time period in the future time period. For example, the accident risk information of each road section in the future time period may refer to the accident information of each road section every hour in the next month.

[0056] By collecting vehicle driving information in real time during driving, the road condition is predicted and the road condition prediction information is obtained, so that the driving route can be planned according to the road condition prediction information, congested sections can be avoided, unnecessary driving distance can be reduced during vehicle driving, and the driving time of the vehicle can be shortened.

[0057] In some embodiments, the energy efficiency optimization module 20 includes a route planning unit 21, a mode adjustment unit 22 and an energy recovery unit 23. The route planning unit 21 is used to determine the vehicle's driving route based on the vehicle's position information, road condition prediction information and vehicle status information; the mode adjustment unit 22 is used to determine the vehicle's driving mode based on the road condition prediction information, the vehicle's driving speed and vehicle status information; the energy recovery unit 23 is used to collect the vehicle's braking energy through the energy recovery system when the vehicle is in a braking state or in a downhill state, and the braking energy is used to store in the battery in the form of electrical energy for use in the vehicle's in-vehicle power-consuming devices.

[0058] The route planning unit 21 and the mode adjustment unit 22 are connected by hard wire, and the route planning unit 21 and the mode adjustment unit 22 transmit signals through the CAN-FD protocol. The route planning unit 21 and the energy recovery unit 23 are connected by hard wire, and the route planning unit 21 and the energy recovery unit 23 transmit signals through the CAN-FD protocol. The mode adjustment unit 22 and the energy recovery unit 23, and the mode adjustment unit 22 and the energy recovery unit 23 transmit signals through the CAN-FD protocol.

[0059] The route planning unit 21 determines the vehicle's driving route based on the vehicle's position information, road condition prediction information, and vehicle status information acquired in real time, taking into account the driving distance and driving time.

[0060] In some embodiments, the vehicle status information includes at least one of the following: the remaining battery power of the vehicle or the remaining fuel of the vehicle; and the engine load of the vehicle.

[0061] Optionally, if the vehicle is a fuel vehicle, the vehicle status information includes at least one of the following: the remaining amount of power of the vehicle, the engine load of the vehicle. Optionally, if the vehicle is a fuel vehicle, the vehicle status information includes at least one of the following: the remaining amount of fuel of the vehicle, the engine load of the vehicle.

[0062] Engine load refers to the load borne by the engine. It is used to indicate the load borne by the engine at a specific speed. Specifically, it refers to the ratio between the actual output torque of the engine and the maximum torque at that speed. During engine operation, when the engine load increases, the intake and injection volume need to be increased to ensure the normal operation of the engine. The load of the engine can be indicated by the opening of the throttle. Therefore, the change of engine load is actually reflected in the size of the throttle, that is, the size of the accelerator.

[0063] The mode adjustment unit 22 determines the vehicle driving mode based on the road condition prediction information, the vehicle's driving speed and the vehicle status information acquired in real time, taking into account the energy distribution of the vehicle. For example, if the remaining power of the vehicle or the remaining fuel of the vehicle is lower than a threshold, the vehicle driving mode can be an economic mode. Alternatively, if the vehicle's driving speed is lower than a speed threshold, the vehicle driving mode can be an economic mode. Alternatively, if the traffic flow information of each road section in the road condition prediction information in the future time period is greater than the traffic flow threshold, the vehicle driving mode can be an economic mode.

[0064] When the vehicle is in a braking state or in a downhill state, the energy recovery unit 23 collects the braking energy of the vehicle through the energy recovery system, and the braking energy is used to store in the battery in the form of electrical energy for use in the vehicle's on-board power-consuming devices.

[0065] The vehicle is in a braking state which refers to a state in which the brake pedal of the vehicle receives a stepping signal, and the vehicle is in a downhill state which refers to a deflection direction of the gyroscope of the vehicle which indicates that the vehicle is in a downhill state.

[0066] The energy recovery system is a system used in automobiles or rail transit that can convert the heat energy generated by vehicle braking into mechanical energy and store it in the form of electrical energy in the battery. When in use, it can quickly release the electrical energy for the vehicle's on-board power-consuming devices. Braking energy refers to the heat energy generated when the vehicle brakes, specifically the heat energy generated when the vehicle is in a braking state or in a downhill state.

[0067] A vehicle's in-vehicle power-consuming equipment refers to equipment in the vehicle that uses electrical energy to operate, including but not limited to air-conditioning systems, lighting systems, car audio, car chargers, driving recorders and other equipment.

[0068] By acquiring vehicle location information, road condition prediction information and vehicle status information in real time, the vehicle route is planned to avoid congested sections, reduce unnecessary driving distance during vehicle driving, and shorten vehicle driving time. In addition, the optimal driving mode is matched for different sections of the vehicle based on road condition prediction information, reducing energy consumption during vehicle driving and optimizing energy utilization.

[0069] In some embodiments, the safety assurance module 30 includes an environment perception unit 31 and a vehicle control unit 32. The environment perception unit 31 is used to obtain vehicle environment information, which includes three-dimensional map information, surrounding object information, and road sign information; the vehicle control unit 32 is used to adjust the vehicle's actuator according to the vehicle environment information.

[0070] The environment sensing unit 31 and the vehicle control unit 32 are connected by a hard line, and the environment sensing unit 31 and the vehicle control unit 32 transmit signals through the CAN-FD protocol.

[0071] The environment perception unit 31 obtains vehicle environment information in real time through sensors, converts the vehicle environment information into internal signal data of the vehicle, and sends it to the vehicle control unit 32 .

[0072] In some embodiments, the environment perception unit 31 includes a laser radar, a millimeter wave radar, and a camera.

[0073] Millimeter-wave radar calculates the distance of an object by emitting radio waves (i.e., millimeter waves) and calculating the time difference between the received echo and the transmitted wave. The detection range of millimeter-wave radar is usually between 0 and 200 meters, which is suitable for the detection of medium-distance objects. Lidar identifies objects and calculates distances by emitting laser pulses and measuring the reflected light signals. The detection range of lidar is longer than that of millimeter-wave radar, which can reach hundreds or even thousands of meters. Lidar is generally more accurate than millimeter-wave radar because it uses light to measure distance, while millimeter-wave radar uses radio waves, which enables lidar to more accurately detect the shape, size, and distance of objects. In addition, millimeter-wave radar has a high ability to penetrate fog, smoke, and dust, so it can still work normally in bad weather conditions, but the performance of lidar may be affected in bad weather conditions such as rain and snow.

[0074] The camera identifies objects and calculates distances by collecting and analyzing images. The advantages of a camera are its large angle detection range and high resolution. However, the camera's imaging is easily disturbed by environmental factors. For example, rain and snow may cause the lens to fog and frost, the camera's sensitivity decreases at night, and strong light may affect the imaging quality. The camera's optical stability is slightly poor, and the imaging quality needs to be optimized through continuous updates and iterations.

[0075] The environment perception unit 31 is used to obtain three-dimensional map information through a laser radar, and the three-dimensional map information is used to indicate a three-dimensional map of the environment in which the vehicle is located.

[0076] The environment perception unit 31 emits laser pulses through the laser radar and measures the reflected light signals to identify objects and calculate distances, thereby obtaining three-dimensional map information. The three-dimensional map information is used to indicate a three-dimensional map of the environment in which the vehicle is located, including identification information of each object within the detection range of the laser radar and distance information of each object. The environment perception unit 31 obtains three-dimensional map information through the laser radar, providing high-precision environment perception and distance measurement for vehicle driving, thereby ensuring driving safety.

[0077] The environment perception unit 31 is used to obtain surrounding object information through millimeter wave radar, and the surrounding object information is used to indicate other vehicles and pedestrians around the vehicle.

[0078] The environment perception unit 31 transmits radio waves through the millimeter wave radar and calculates the distance of the object based on the time difference between the received echo and the transmitted wave to obtain the surrounding object information, which includes the identification information and distance information of each object within the detection range of the millimeter wave radar, and specifically includes the identification information and distance information of other vehicles and pedestrians within the detection range of the millimeter wave radar. The environment perception unit 31 accurately detects and confirms the objects around the vehicle through the millimeter wave radar, further performs environment perception, improves the precision and accuracy of environment perception, and further ensures driving safety.

[0079] The environment perception unit 31 is used to obtain road sign information through a camera, and the road sign information is used to indicate the road signs on the road section where the vehicle is located.

[0080] The environment perception unit 31 is used to capture image information of the surrounding environment through a camera, and realize visual perception through image processing and image analysis to identify road sign information. The road sign information is mainly used to obtain the road signs on the road section where the vehicle is located, and may include road signs on the road section where the vehicle is located, other vehicles and pedestrians around the vehicle and other object information. The environment perception unit 31 confirms the road signs on the road section where the vehicle is located through a camera, and provides more accurate road restriction information and road indication information for the vehicle to ensure driving safety.

[0081] The vehicle control unit 32 adjusts the vehicle's actuators according to the vehicle environment information to achieve precise control of the vehicle. Specifically, the vehicle control unit 32 receives the vehicle environment information sent by the environment perception unit 31, and the vehicle driving route and vehicle driving mode sent by the energy efficiency optimization module 20. The vehicle control unit needs to ensure that the vehicle can accurately follow the planned vehicle driving route during driving, and can respond to various complex road conditions and emergencies on the vehicle driving route according to the vehicle environment information to ensure driving safety.

[0082] In some embodiments, the actuator of the vehicle includes at least one of the following: a steering wheel of the vehicle, an accelerator pedal of the vehicle, and a brake pedal of the vehicle.

[0083] That is, the vehicle control unit 32 adjusts actuators such as the vehicle's steering wheel, the vehicle's accelerator pedal, and the vehicle's brake pedal according to the vehicle's environmental information to achieve precise control of the vehicle.

[0084] The actuators of the vehicle are adjusted to determine the steering wheel angle of the vehicle, the opening of the accelerator pedal of the vehicle and the opening of the brake pedal of the vehicle. The opening of the accelerator pedal is used to indicate the degree of depression of the accelerator pedal, specifically the ratio of the vertical distance of the accelerator pedal from the bottom of the vehicle after adjustment to the vertical distance of the accelerator pedal from the bottom of the vehicle before adjustment. The opening of the brake pedal is used to indicate the degree of depression of the brake pedal, specifically the ratio of the vertical distance of the brake pedal from the bottom of the vehicle after adjustment to the vertical distance of the brake pedal from the bottom of the vehicle before adjustment.

[0085] The adjusted actuator is used to indicate the execution information of the actuator of the vehicle determined according to the vehicle environment information, including the steering wheel angle of the vehicle, the opening degree of the accelerator pedal of the vehicle and the opening degree of the brake pedal of the vehicle.

[0086] By collecting vehicle environmental information during driving in real time and adjusting the vehicle's actuators, the vehicle can respond to different driving environments based on the adjusted actuators, ensuring driving safety and achieving precise control of the vehicle.

[0087] The following is an embodiment of the method of the present application. For details not described in detail in the embodiment of the method of the present application, please refer to the above embodiment of the automatic driving system for the vehicle.

[0088] Please refer to Figure 2 , which shows a flow chart of an automatic driving method for a vehicle provided by an embodiment of the present application. The method is applied to the automatic driving system of the vehicle introduced above. The automatic driving system includes a road condition prediction module, an energy efficiency optimization module and a safety assurance module. The method may include at least one of the following steps 210 to 250:

[0089] Step 210, obtaining vehicle position information and vehicle travel information, the vehicle position information is used to indicate the starting position and the end position of the vehicle travel, and the vehicle travel information is used to indicate weather information and traffic information of each road section.

[0090] Exemplarily, the energy efficiency optimization module obtains vehicle location information, and the road condition prediction module obtains vehicle driving information.

[0091] In some embodiments, the energy efficiency optimization module includes a route planning unit, a mode adjustment unit and an energy recovery unit. The route planning unit obtains vehicle position information.

[0092] In some embodiments, the road condition prediction module includes a data collection unit and a model prediction unit. The data collection unit obtains vehicle driving information.

[0093] In some embodiments, the vehicle driving information includes at least one of the following: weather forecast information, real-time traffic information, and historical traffic information. Weather forecast information includes real-time weather information, road section waterlogging information, and disaster forecast information; real-time traffic information includes traffic control information, road section congestion information, and accident vehicle information; historical traffic information includes historical traffic flow information and historical accident information.

[0094] Step 220, determining road condition prediction information based on the vehicle driving information, where the road condition prediction information is used to predict the road condition of each road section.

[0095] Exemplarily, the road condition prediction module determines the road condition prediction information according to the vehicle driving information.

[0096] In some embodiments, the model prediction unit determines the road condition prediction information according to the vehicle driving information through the road condition prediction model, and the road condition prediction information includes the traffic flow information of each road section and the risk information of each road section.

[0097] In some embodiments, the road condition prediction information includes at least one of the following: traffic flow information of each road section in a future time period; congestion information of each road section in a future time period; accident risk information of each road section in a future time period.

[0098] Step 230, determining the vehicle driving route and vehicle driving mode based on the vehicle position information and the road condition prediction information.

[0099] Exemplarily, the energy efficiency optimization module determines the vehicle driving route and vehicle driving mode according to the vehicle position information and road condition prediction information.

[0100] In some embodiments, the route planning unit determines the vehicle's driving route based on the vehicle's position information, road condition prediction information and vehicle status information; the mode adjustment unit determines the vehicle's driving mode based on the road condition prediction information, the vehicle's driving speed and the vehicle status information; the energy recovery unit collects the vehicle's braking energy through the energy recovery system when the vehicle is in a braking state or in a downhill state, and the braking energy is used to store in the battery in the form of electrical energy for use in the vehicle's on-board power-consuming devices.

[0101] Step 240, obtaining vehicle environment information, where the vehicle environment information is used to indicate the driving environment of the vehicle.

[0102] Exemplarily, the safety assurance module obtains vehicle environment information.

[0103] In some embodiments, the safety assurance module includes an environment perception unit and a vehicle control unit. The environment perception unit obtains vehicle environment information, which includes three-dimensional map information, surrounding object information, and road sign information.

[0104] In some embodiments, the environment perception unit includes a laser radar, a millimeter wave radar, and a camera. The environment perception unit obtains three-dimensional map information through the laser radar, and the three-dimensional map information is used to indicate the three-dimensional map of the environment in which the vehicle is located. The environment perception unit obtains surrounding object information through the millimeter wave radar, and the surrounding object information is used to indicate other vehicles and pedestrians around the vehicle. The environment perception unit obtains road sign information through the camera, and the road sign information is used to indicate the road signs on the road section where the vehicle is located.

[0105] Step 250, adjusting the actuator of the vehicle according to the vehicle environment information, and the vehicle is used to travel according to the vehicle driving route and vehicle driving mode based on the adjusted actuator.

[0106] Exemplarily, the safety assurance module adjusts the actuators of the vehicle according to the vehicle environment information.

[0107] In some embodiments, the vehicle control unit adjusts the vehicle's actuators based on vehicle environment information.

[0108] In some embodiments, the actuator of the vehicle includes at least one of the following: a steering wheel of the vehicle, an accelerator pedal of the vehicle, and a brake pedal of the vehicle.

[0109] The technical solution provided in the embodiment of the present application predicts the road conditions by collecting the vehicle driving information in real time during the driving process, so that the vehicle can avoid congested sections according to the starting position and the end position of the vehicle, dynamically plan the vehicle driving route, reduce unnecessary driving distance during the vehicle driving process, and shorten the vehicle driving time. At the same time, according to the road condition prediction information, the optimal driving mode is matched for different sections of the vehicle, the energy consumption during the vehicle driving process is reduced, and the energy utilization is optimized. The vehicle environment information during the driving process of the vehicle is collected in real time, and the vehicle's actuator is adjusted, so that the vehicle can respond to different driving environments based on the adjusted actuator to ensure driving safety and achieve precise control of the vehicle.

[0110] It should be understood that the "multiple" mentioned in this article refers to two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. In addition, the step numbers described in this article only illustrate a possible execution sequence between the steps. In some other embodiments, the above steps may not be executed in the order of the numbers, such as two steps with different numbers are executed at the same time, or two steps with different numbers are executed in the opposite order to the diagram. The embodiments of the present application are not limited to this.

[0111] The above description is only an exemplary embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An automatic driving system for a vehicle, characterized in that: The autonomous driving system includes a road condition prediction module, an energy efficiency optimization module and a safety assurance module; The road condition prediction module is used to determine road condition prediction information according to vehicle driving information, wherein the vehicle driving information is used to indicate weather information and traffic information of each road section, and the road condition prediction information is used to predict the road condition of each road section; The energy efficiency optimization module is used to determine the vehicle driving route and vehicle driving mode according to the vehicle position information and the road condition prediction information, wherein the vehicle position information is used to indicate the starting position and the end position of the vehicle driving; The safety assurance module is used to adjust the actuator of the vehicle according to vehicle environment information, and the vehicle environment information is used to indicate the driving environment of the vehicle. The vehicle is used to drive according to the vehicle driving route and the vehicle driving mode based on the adjusted actuator.

2. The automatic driving system according to claim 1, characterized in that: The road condition prediction module includes a data acquisition unit and a model prediction unit; The data acquisition unit is used to obtain the vehicle driving information; The model prediction unit is used to determine the road condition prediction information according to the vehicle driving information through a road condition prediction model, and the road condition prediction information is used to indicate the traffic information of each road section in a future time period.

3. The automatic driving system according to claim 2, characterized in that: The vehicle driving information includes at least one of the following: Weather forecast information, including real-time weather information, road flooding information, and disaster forecast information; Real-time traffic information, including traffic control information, road congestion information and accident vehicle information; Historical traffic information, including historical traffic flow information and historical accident information.

4. The automatic driving system according to claim 2, characterized in that: The traffic condition prediction information includes at least one of the following: Traffic flow information of each road section in the future time period; congestion information of each road section in the future time period; The accident risk information of each road section in the future time period.

5. The automatic driving system according to claim 1, characterized in that: The energy efficiency optimization module includes a route planning unit, a mode adjustment unit and an energy recovery unit; The route planning unit is used to determine the vehicle driving route according to the vehicle position information, the road condition prediction information and the vehicle state information; The mode adjustment unit is used to determine the vehicle driving mode according to the road condition prediction information, the vehicle driving speed and the vehicle state information; The energy recovery unit is used to collect the braking energy of the vehicle through an energy recovery system when the vehicle is in a braking state or in a downhill state. The braking energy is used to store it in the battery in the form of electrical energy for use in the vehicle's in-vehicle power-consuming devices.

6. The automatic driving system according to claim 5, characterized in that: The vehicle status information includes at least one of the following: the remaining amount of electricity of the vehicle or the remaining amount of fuel of the vehicle; An engine load of the vehicle.

7. The automatic driving system according to claim 1, characterized in that: The safety assurance module includes an environment perception unit and a vehicle control unit; The environment perception unit is used to obtain the vehicle environment information, and the vehicle environment information includes three-dimensional map information, surrounding object information and road sign information; The vehicle control unit is used to adjust the actuator of the vehicle according to the vehicle environment information.

8. The automatic driving system according to claim 7, characterized in that: The environment perception unit includes a laser radar, a millimeter wave radar and a camera; The environment perception unit is used to obtain the three-dimensional map information through the laser radar, and the three-dimensional map information is used to indicate a three-dimensional map of the environment in which the vehicle is located; The environment perception unit is used to obtain the surrounding object information through the millimeter wave radar, and the surrounding object information is used to indicate other vehicles and pedestrians around the vehicle; The environment perception unit is used to obtain the road sign information through the camera, and the road sign information is used to indicate the road signs on the road section where the vehicle is located.

9. The automatic driving system according to claim 7, characterized in that: The actuator of the vehicle includes at least one of the following: a steering wheel of the vehicle; an accelerator pedal of the vehicle; A brake pedal for the vehicle.

10. A method for automatic driving of a vehicle, characterized in that: The method comprises: Acquire vehicle location information and vehicle travel information, wherein the vehicle location information is used to indicate the starting position and the ending position of the vehicle travel, and the vehicle travel information is used to indicate weather information and traffic information of each road section; Determining road condition prediction information according to the vehicle driving information, wherein the road condition prediction information is used to predict the road condition of each road section; Determining a vehicle driving route and a vehicle driving mode according to the vehicle position information and the road condition prediction information; Acquiring vehicle environment information, where the vehicle environment information is used to indicate a driving environment of the vehicle; The actuator of the vehicle is adjusted according to the vehicle environment information, and the vehicle is used to travel according to the vehicle driving route and the vehicle driving mode based on the adjusted actuator.

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