WO2021119964A1 - 用于智能网联车辆的控制系统及控制方法 - Google Patents

用于智能网联车辆的控制系统及控制方法 Download PDF

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Publication number
WO2021119964A1
WO2021119964A1 PCT/CN2019/125779 CN2019125779W WO2021119964A1 WO 2021119964 A1 WO2021119964 A1 WO 2021119964A1 CN 2019125779 W CN2019125779 W CN 2019125779W WO 2021119964 A1 WO2021119964 A1 WO 2021119964A1
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Prior art keywords
information
vehicle
module
planning
control
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PCT/CN2019/125779
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English (en)
French (fr)
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张玉新
陈建成
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驭势科技（北京）有限公司
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Priority to JP2022536846A priority Critical patent/JP7450982B2/ja
Priority to KR1020227023685A priority patent/KR20220114016A/ko
Priority to EP19956596.1A priority patent/EP4074562A4/en
Priority to US17/785,935 priority patent/US20230025222A1/en
Priority to CN201980003925.2A priority patent/CN113272195A/zh
Priority to PCT/CN2019/125779 priority patent/WO2021119964A1/zh
Publication of WO2021119964A1 publication Critical patent/WO2021119964A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
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    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
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    • B60—VEHICLES IN GENERAL
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    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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Definitions

  • the invention relates to the technical field of intelligent vehicles, in particular to a control system and a control method for intelligent networked vehicles.
  • intelligent networked vehicles With the continuous development of intelligent networked vehicle technology, intelligent networked vehicles have gradually changed from intelligent assisted driving to intelligent independent driving. Moreover, in the process of continuous intelligentization of intelligent networked vehicles, there are many differences between them and traditional vehicles. For example, the high-level intelligent networked vehicle has completely separated from the driver's control, and it needs to be independently judged and degraded when it fails. It cannot be ignored that there is a big difference between the driving styles of intelligent networked vehicles and human drivers, and a high degree of intelligence is accompanied by a higher driving risk. Therefore, the safety of high-level intelligent networked vehicles has become the key to whether they can land.
  • control system of most intelligent networked vehicles is still based on the development of the control system of traditional vehicles. Because the control system architecture is too simple and the redundant control is too rigid, it requires the driver or the inspector to assist in control or even take over, resulting in the intelligent body in the driving process Inability to make good self-determination, supervision, fault diagnosis and avoid driving risks.
  • the present invention provides a control system and control method for intelligent networked vehicles to solve the problem that the existing technology cannot meet the safety requirements of intelligent networked vehicles due to the simple structure of the control system and the excessively rigid redundant control. The problem.
  • the first aspect of the present invention provides a control system for an intelligent networked vehicle, the control system including:
  • Sensor group used to obtain sensor information
  • a sensing and positioning module which is used to obtain sensing information and positioning information based on the sensing information
  • the planning control module is used to determine vehicle planning control information based on the perception information and positioning information, the vehicle state assessment result, and the risk assessment result, where the vehicle state assessment result is generated by the function assessment module, and the risk assessment result Is generated by the risk assessment module;
  • a safety control module configured to determine vehicle safety control information based on the perception information and positioning information, the vehicle state assessment result, and the risk assessment result;
  • the function evaluation module is used to determine the vehicle state evaluation result based on the state information of the sensor group, the perception positioning module, the planning control module, the safety control module, and the execution module;
  • a risk assessment module configured to obtain a risk assessment result based on the sensor information, the positioning information, the status and planning information of the planning control module, the status of the safety control module, and the safety planning information;
  • Logical arbitration module configured to arbitrate the vehicle planning control information and the vehicle safety control information based on the state of the function evaluation module and the risk evaluation module, the vehicle state evaluation result, and the risk evaluation result Vehicle execution information;
  • the execution module is used to control the driving of the vehicle based on the execution information of the vehicle.
  • the second aspect of the present invention provides a control method for an intelligent networked vehicle.
  • the method is based on the control method proposed by the control system provided in the embodiment of the present invention, which includes:
  • Determining vehicle planning control information according to the perception information, positioning information, vehicle state assessment results, and risk assessment results;
  • Determining vehicle safety control information according to the perception information, positioning information, vehicle state assessment results, and risk assessment results;
  • a third aspect of the present invention provides an in-vehicle device, including: a processor, a memory, and a communication interface, the communication interface is data connected to the processor and the memory;
  • the processor is used to execute the steps of the control method provided in the embodiment of the present invention by calling the program or instruction stored in the memory.
  • the fourth aspect of the present invention provides a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium stores a program or instruction, and the program or instruction causes a computer to execute the The steps of the control method.
  • the planning control module determines vehicle planning control information based on perception information and positioning information
  • the safety control module determines vehicle safety control information based on the perception information and positioning information
  • the function evaluation module determines the vehicle According to the status assessment result
  • the risk assessment module determines the risk assessment result.
  • the logical arbitration module arbitrates the vehicle planning control information and vehicle safety control information based on the working status of the function assessment module and the working status of the risk assessment module, and determines the vehicle execution information, so that Vehicle execution information is more accurate, thereby reducing the driving risk of intelligent networked vehicles.
  • Figure 1 is an overall architecture diagram of an intelligent networked vehicle provided by an embodiment of the present invention.
  • Fig. 2 is an exemplary block diagram of a control system for an intelligent networked vehicle provided by an embodiment of the present invention
  • FIG. 3 is a flowchart of a control method for an intelligent networked vehicle in this embodiment
  • FIG. 4 is a schematic structural diagram of a vehicle-mounted device provided by this embodiment.
  • this embodiment provides a control system and control method for the intelligent networked vehicle, and the vehicle control command is combined
  • the status assessment results, risk assessment results, vehicle planning control information and vehicle safety control information are determined, which improves the accuracy of vehicle control commands, thereby improving the safety of intelligent networked vehicles.
  • FIG. 1 is an overall architecture diagram of an intelligent networked vehicle provided by this embodiment.
  • the intelligent networked vehicle includes a sensor group 10, an intelligent driving system 20, a vehicle execution system 30 and a cloud server 40, and the intelligent driving system 20 and the cloud server 40 can communicate.
  • the sensor group 10 is used to obtain sensor information.
  • the sensor group includes but is not limited to at least one of a camera, a lidar, a millimeter wave radar, a global positioning system (Global Positioning System, GPS), a pressure sensor, an IMU, an angle sensor, and a speed sensor.
  • GPS Global Positioning System
  • the intelligent driving system is used to receive the sensor information of the sensor group and generate execution information based on the sensor information.
  • the vehicle execution system is used to receive execution information and control the vehicle to travel according to the execution information.
  • the vehicle execution system includes, but is not limited to, a steering system, a braking system, and a drive system.
  • the steering system, braking system, and drive system are mature systems in the vehicle field and will not be repeated here.
  • the cloud server communicates with the intelligent driving system for overall coordination and management of intelligent networked vehicles.
  • the cloud server can be used to interact with one or more intelligent networked vehicles, coordinate the scheduling of multiple intelligent networked vehicles, and so on.
  • the vehicle control system 20 and the cloud server perform wireless communication through wireless communication networks (including but not limited to wireless communication networks such as GPRS network, Zigbee network, Wifi network, 3G network, 4G network, 5G network, etc.).
  • the cloud server is a cloud server established by a vehicle service provider to provide cloud storage and cloud computing functions.
  • the vehicle file is created in the cloud server.
  • various information uploaded by the vehicle control system 20 is stored in the vehicle file.
  • the cloud server can synchronize the driving data generated by the vehicle in real time.
  • the cloud server may include a data warehouse and a data processing platform, wherein the data warehouse stores the vehicle-side files created by the cloud server.
  • the data warehouse may uniformly collect data from various source business systems into the data warehouse, and process the data on the data processing platform for use on the vehicle side.
  • the cloud server may be a server or a server group.
  • Server groups can be centralized or distributed. Distributed server is conducive to task allocation and optimization among multiple distributed servers, and overcomes the shortcomings of traditional centralized server resource shortage and response bottleneck.
  • the cloud server may be local or remote.
  • the cloud server can be used to obtain information about a road side unit (RSU: Road Side Unit) and an intelligent networked vehicle, and can send information to the intelligent networked vehicle.
  • RSU Road Side Unit
  • the cloud server may send the detection information corresponding to the intelligent networked vehicle in the road monitoring unit to the intelligent networked vehicle according to the information of the intelligent networked vehicle.
  • the intelligent networked vehicle may also include a vehicle CAN bus, which connects the vehicle control system 20 and the vehicle execution system 30.
  • vehicle CAN bus connects the vehicle control system 20 and the vehicle execution system 30.
  • the information interaction between the intelligent driving system 10 and the underlying execution system of the vehicle is transmitted through the vehicle CAN bus.
  • the intelligent networked vehicle can be controlled by the driver in a manual driving mode, and can also be controlled by the vehicle control system 20 in an unmanned manner.
  • the driver drives the vehicle by operating a device that controls the traveling of the vehicle.
  • the devices that control the traveling of the vehicle include, but are not limited to, a brake pedal, a steering wheel, and an accelerator pedal, for example.
  • the device for controlling the driving of the vehicle can directly operate the execution system at the bottom of the vehicle to control the driving of the vehicle.
  • the intelligent networked vehicle may also be an unmanned vehicle, and the driving control of the vehicle is controlled by the intelligent control system 20 outputting control instructions and executed by the vehicle execution system 30.
  • Fig. 2 is an exemplary block diagram of a control system for an intelligent networked vehicle provided by an embodiment of the present invention.
  • the control system can implement part of the functions of the intelligent control system 20 in FIG. 1 for controlling the driving of the intelligent networked vehicle.
  • control system for intelligent networked vehicles can be divided into a sensor receiving module 201, a perception positioning module 202, a planning control module 203, a safety control module 204, a function assessment module 205, a risk assessment module 206, and logic
  • the sensor receiving module 201 is used to receive the sensor information of the sensor group, and transmit the sensor information to the sensor positioning module 202, the planning control module 203, the safety control module 204, the function evaluation module 205, and the risk evaluation module 206.
  • the sensor group includes but is not limited to one or more of camera, lidar, millimeter wave radar, pressure sensor, IMU, angle sensor, speed sensor, etc.;
  • the sensor information includes environmental information and vehicle status information .
  • the environmental information includes obstacles, pedestrians, surrounding vehicles, drivable areas, road markings, and so on.
  • Vehicle status information includes vehicle speed, front wheel deflection angle, acceleration, deceleration, steering wheel angle, brake, throttle and other status.
  • the sensor group can also monitor its own state information, and send its own state information to the function evaluation module 205 through the sensor receiving module 201.
  • the self-state information includes the working state of each sensor in the sensor group, and the like.
  • the sensing and positioning module 202 is configured to determine sensing information and positioning information based on the sensing information.
  • the sensing and positioning module 202 further perceives its own state, and transmits the state information to the planning control module 203, the safety control module 204, the function evaluation module 205, and the risk evaluation module 206.
  • the sensing and positioning module 202 includes a sensing unit 2021 and a positioning unit 2022.
  • the sensing unit 2021 obtains the sensing information.
  • the sensing unit 2021 is configured to sense the state of the vehicle itself and the environment outside the vehicle based on the sensing information, and obtain the sensing information.
  • the perception information includes the vehicle's own state information, such as vehicle state information such as vehicle speed, vehicle acceleration, and hardware working status in the vehicle.
  • the perception information also includes information about the environment outside the vehicle, such as information about the vehicle's driveable area, obstacles, pedestrians around the vehicle, and other vehicles.
  • the positioning unit 2022 is configured to obtain position information of the vehicle based on the sensor information acquisition, and obtain positioning information. In some embodiments, the positioning unit 2022 obtains the position information of the vehicle based on GPS, IMU, identification positioning module, and the like. In some embodiments, the positioning information can also be positioned by visual sensors, lidar, etc., for example, obtained by means of V-SLAM, Lidar-SLAM, and the like.
  • the planning control module 203 determines vehicle planning control information based on the perception information and positioning information. Wherein, the vehicle planning control information is generated based on driving comfort, timeliness, and applicability. In some embodiments, the planning control module may further combine at least one of V2X data, high-precision maps and other data to perform path planning and decision-making. In some embodiments, the planning control module further receives vehicle state assessment results and/or risk assessment results to determine vehicle planning control information, and determines vehicle planning based on the perception information, positioning information, vehicle state assessment results, and risk assessment results. Control information.
  • the vehicle planning control information includes control information such as vehicle speed, front wheel deflection angle, acceleration, deceleration, steering wheel angle, braking, and accelerator.
  • the planning control module 203 includes a planning unit 2031 and a planning motion control unit 2032.
  • the planning unit 2031 is used to generate planning information.
  • the planning unit generates planning information based on the sensing positioning information generated by the sensing module and the positioning module.
  • the planning unit may also combine at least one of V2X data, high-precision maps and other data to generate planning information.
  • the planning information includes but is not limited to: desired path, behavior (for example, including but not limited to following, overtaking, stopping, detouring, etc.), vehicle heading, vehicle speed, desired acceleration of the vehicle, desired Steering wheel angle, etc.
  • the planning unit receives planning information from a vehicle state assessment result and/or a risk assessment result.
  • the planning unit performs planning based on passenger comfort, where the passenger comfort includes driving comfort, driving timeliness, driving suitability, and the like.
  • the planning unit transmits the generated plans and decisions to the planning motion control unit.
  • the planning unit 2031 transmits the obtained planning information and the performance of the planning unit 2031 to the planning motion control unit 2032.
  • the planning motion control unit 2032 is configured to determine vehicle planning control information based on the planning information.
  • the vehicle planning control information refers to execution information of the underlying control system of the vehicle.
  • the planning motion control unit issues vehicle control information so that the bottom-level execution system of the vehicle controls the vehicle to travel along a desired path, for example, controls the vehicle laterally and longitudinally by controlling the steering wheel, brakes, and accelerator. For example, it is planned that the maximum acceleration does not exceed 5m/S 2 and the maximum steering angle does not exceed 15°.
  • the safety control module 204 is configured to generate vehicle safety control information based on the perception information and positioning information. Wherein, the vehicle safety control information is generated based on driving safety, stability and collision consequences. In some embodiments, the safety control module further determines the vehicle safety control information based on the vehicle state evaluation result and the risk evaluation result, that is, the vehicle safety control information is determined based on the perception information and positioning information, the vehicle state evaluation result, and the risk evaluation result, which is a smart network Linked vehicles provide high-guarantee control decisions.
  • the safety control module 204 includes a planning unit 2041 and a safety behavior control unit 2042.
  • the planning unit 2041 is used to generate safety planning information.
  • the planning unit 2041 generates safety planning information based on the sensing positioning information generated by the sensing module and the positioning module.
  • the planning unit 2041 may also combine at least one of V2X data, high-precision maps and other data to generate safety planning information.
  • the safety planning information includes, but is not limited to: behaviors (including but not limited to following, overtaking, stopping, detouring, etc.), vehicle heading, vehicle speed, desired acceleration of the vehicle, desired steering wheel angle Wait.
  • the safety planning unit receives safety planning information from a vehicle state assessment result and/or a risk assessment result.
  • the safety planning unit performs driving planning for the vehicle based on driving safety, stability and collision consequences.
  • the safety planning unit transmits the generated plans and decisions to the planning motion control unit.
  • the safety behavior control unit 2042 is configured to determine vehicle safety control information based on the safety planning information.
  • the safety planning control information refers to the execution information of the vehicle's underlying control system.
  • the safety behavior control unit issues vehicle control information so that the bottom-level execution system of the vehicle controls the vehicle to travel along a desired path, for example, controls the vehicle laterally and longitudinally by controlling the steering wheel, brakes, and accelerator.
  • the vehicle safety planning information considers, but is not limited to, safety factors such as vehicle slip rate, yaw angle, and roll angle. For example, the slip rate of the vehicle should not exceed 20%, and the yaw angle and roll angle of the vehicle should be kept within a safe range.
  • the function evaluation module 205 is used to generate vehicle state evaluation results.
  • the function evaluation module 205 monitors the working status of the sensor group 201, the perception positioning module 202, the planning control module 203, the safety control module 204, and the execution module 208 in real time, determines the first monitoring result, and evaluates the above functions based on the first monitoring result.
  • the function of the module is evaluated, and the result of the vehicle state evaluation is obtained.
  • the first monitoring result includes but is not limited to software and hardware failure monitoring and functional failure monitoring results.
  • the function evaluation module 205 monitors the status of the sensor group 201, the perception positioning module 202, the planning control module 203, the safety control module 204, and the execution module 208 in real time to obtain the first monitoring result, and then according to the first monitoring As a result, the severity of the damage of the above-mentioned modules was evaluated in a graded manner, and the vehicle state evaluation result was obtained.
  • the first monitoring result includes, but is not limited to, software and hardware failure monitoring and functional failure monitoring results.
  • the function evaluation module 205 includes a function monitoring unit 2051 and a function evaluation unit 2052.
  • the function monitoring unit is used to generate the first monitoring information.
  • the first monitoring information includes software and hardware failure monitoring information and function failure detection information for the functional module.
  • the functional modules include a sensor group 201, a perception positioning module 202, a planning control module 203, a safety control module 204, a function evaluation module 205, and an execution module 208.
  • the function monitoring unit includes a fault monitoring subunit 301 and a function monitoring subunit 302.
  • the fault monitoring subunit 301 is used for real-time monitoring of the sensor group 201, the perception positioning module 202, the planning control module 203, The status of the safety control module 204, the function evaluation module 205, and the execution module 208 obtain corresponding fault monitoring information.
  • the function monitoring subunit 302 is used to monitor the status of the sensor group 201, the sensing positioning module 202, the planning control module 203, the safety control module 204, the function evaluation module 205, and the execution module 208 in real time to determine whether it fails.
  • the function evaluation unit 2052 is used to determine the evaluation result of the vehicle state. In some embodiments, the function evaluation unit 2052 is configured to evaluate the safety of the vehicle itself according to the first monitoring result, and determine the vehicle state evaluation result. In some embodiments, the function evaluation unit 2052 sends the vehicle state evaluation result to the planning control module 203 and the safety control module 204. In some embodiments, when the vehicle state evaluation result determined by the function evaluation unit 2052 changes, the planning control module 203 modifies the vehicle planning control information according to the vehicle state evaluation result. In some embodiments, the safety control module 204 modifies the vehicle safety control information according to the result of the vehicle state evaluation.
  • the risk assessment module 206 is used to determine the risk assessment result.
  • the risk assessment module 206 is configured to obtain a risk assessment result based on the sensing information and the positioning information, the status and planning information of the planning control module, the status of the safety control module, and the safety planning information.
  • the risk assessment module 206 includes a risk monitoring unit and a risk assessment unit.
  • the risk monitoring unit determines risk monitoring information based on the sensing information and positioning information.
  • the risk monitoring information includes, but is not limited to, collision monitoring information, feasible region monitoring information, and the like.
  • the risk assessment module may use sensors that are independent of the sensor positioning module, so as to ensure the independence of risk assessment.
  • the risk monitoring unit After determining the risk monitoring information, the risk monitoring unit sends the risk monitoring information to the risk assessment unit.
  • the risk monitoring unit includes a feasible region monitoring subunit 401 and a collision monitoring subunit 402.
  • the feasible region monitoring subunit 401 is used to monitor the environment in the feasible region to obtain feasible region monitoring information and status information of the feasible region monitoring subunit.
  • the collision monitoring subunit 402 is used to monitor the environment around the vehicle body to obtain collision monitoring information and the working status of the collision monitoring subunit.
  • the collision monitoring information can be obtained based on sensing information, positioning information, and real-time monitoring of the surrounding environment.
  • the collision monitoring information is directly obtained through independent sensors.
  • the feasible zone monitoring information includes but is not limited to factors such as weather, speed, pedestrians/animals, etc.
  • the risk assessment unit 2062 is configured to determine a risk assessment result according to the risk monitoring information, the vehicle planning control information, and the vehicle safety control information. In some embodiments, the risk assessment unit further determines the risk assessment result according to the working status of the risk monitoring unit, the planning unit, and the safety planning unit.
  • the risk assessment unit 2402 may also evaluate the overall risk in the vehicle in combination with information such as vehicle speed, driving style, weather, and road condition complexity, and determine the risk assessment result.
  • the risk assessment unit 2062 sends the risk assessment result to the planning unit 2031, the safety planning unit 2041, and the logical arbitration module 207.
  • the planning control module 203 modifies the vehicle planning control information according to the risk assessment result.
  • the safety control module 204 modifies the vehicle safety control information according to the risk assessment result. For example, when the risk assessment result is a level 2 risk level, the minimum speed needs to be limited to meet timeliness.
  • the planning control module 203 and the safety control module 204 can correspondingly modify the vehicle planning control information and safety control information, limit the minimum speed to 90km/h, and correspondingly modify the vehicle planning control information and vehicle safety control information based on the minimum speed.
  • the planning control module 203 and the safety control module 204 may also limit the maximum steering angle, for example, limit the maximum steering angle to 15°, and modify the vehicle planning control information and the vehicle safety control information based on the maximum steering angle.
  • the logic arbitration module 207 is used to determine vehicle execution information. Wherein, the logical arbitration module determines vehicle execution information based on the vehicle planning control information and safety control information. In some embodiments, the logic arbitration module further determines the vehicle execution information according to the working status of the function evaluation module and the working status of the risk evaluation module.
  • the logic arbitration module 207 can determine that the planning control information is execution information based on the risk assessment result that the risk level is low; when the risk level is high, it can determine that the safety control information is execution information.
  • the risk level threatens the safety of the vehicle or the personal safety of the passengers, you can choose to slow down as soon as possible or stop as soon as possible.
  • the vehicle can be controlled to decelerate or stop.
  • the logic arbitration module 207 may select the vehicle planning control information. In some embodiments, when the risk level of the intelligent networked vehicle is high, for example, the risk level is 4, the logic arbitration module 207 may select the vehicle safety control information. In some embodiments, when the intelligent networked vehicle has a higher risk, such as the risk level is above 5, the logic arbitration module 207 may choose to temporarily maintain the speed of the vehicle and seek an opportunity to stop temporarily; or it may choose to reduce the speed of the vehicle and seek the opportunity temporary parking.
  • the logic arbitration module 207 may choose to decelerate at the maximum deceleration, such as decelerating at a deceleration of 12.5 m/S 2 .
  • the execution module 208 is used to control the driving of the vehicle based on the vehicle execution information.
  • the execution module 208 receives the vehicle execution information sent by the logic arbitration module 207, and controls the vehicle to travel based on the vehicle execution information.
  • the execution module 208 includes, but is not limited to, the chassis, steering system, power system, braking system, and other vehicle hardware.
  • the execution module parses the vehicle execution information, and respectively sends matching execution signals to each hardware of the vehicle. The execution module can also monitor its own working status and send the working status to the function evaluation module.
  • modules involved in this embodiment are all logical modules.
  • a logical unit can be a physical unit, a part of a physical unit, or multiple physical units.
  • this embodiment does not introduce units that are not closely related to solving the technical problems proposed by the present invention, but this does not indicate that there are no other units in this embodiment.
  • FIG. 3 is a flowchart of a control method for an intelligent networked vehicle provided by this embodiment.
  • the main body of execution of the control method is the control system provided in this embodiment, and the specific structure of the control system will not be repeated here.
  • the control method provided in this embodiment can be applied to intelligent networked autonomous vehicles.
  • control method for the intelligent networked vehicle may include the following steps:
  • Sensing information is obtained by the sensor group and sent to the intelligent control system through the sensor receiving module.
  • Sensing information includes environmental information and vehicle status information.
  • the environmental information includes obstacles, pedestrians, surrounding vehicles, drivable areas, road markings, and so on.
  • Vehicle status information includes vehicle speed, front wheel deflection angle, acceleration, deceleration, steering wheel angle, brake, throttle and other status.
  • the sensor group also monitors its own state information, and transmits its own state information to the intelligent control system through the sensor receiving module.
  • its own status information includes the working status of each sensor in the sensor group.
  • the perception information includes the vehicle's own state information, such as vehicle driving speed, vehicle acceleration, and vehicle hardware working status in the vehicle.
  • the perception information also includes information about the environment outside the vehicle, such as information about the vehicle's driveable area, obstacles, pedestrians around the vehicle, and other vehicles.
  • the positioning information is based on GPS, IMU, identification positioning module, etc. to obtain the position information of the vehicle.
  • the positioning information can also be positioned by visual sensors, lidar, etc., for example, obtained by means of V-SLAM, Lidar-SLAM, and the like.
  • the vehicle planning control information includes control information such as the vehicle speed, front wheel deflection angle, acceleration, deceleration, steering wheel angle, brake, and accelerator.
  • the vehicle planning control information is determined according to the perception information, positioning information, vehicle state evaluation result, and risk evaluation result.
  • receiving the perception information, positioning information, vehicle state assessment results, and risk assessment results determining planning information according to the perception information, positioning information, vehicle state assessment results, and risk assessment results; analyzing the planning information to obtain Vehicle planning control information.
  • the safety planning information is information generated based on the perception information and positioning information.
  • the security planning information is information generated based on perception information and positioning information, and further combined with at least one of V2X data, high-precision maps and other data.
  • the vehicle safety control information is determined based on the perception information, positioning information, vehicle state evaluation result, and risk evaluation result.
  • receiving the perception information, positioning information, vehicle state assessment results, and risk assessment results determining safety planning information according to the perception information, positioning information, vehicle state assessment results, and risk assessment results; performing safety planning information on the safety planning information Analyze and obtain vehicle safety control information.
  • the risk assessment result is determined according to the sensing information, the positioning information, the working status and planning information of the planning control module, the working status of the safety control module and the safety planning information.
  • the risk assessment module monitors the driving environment of the intelligent networked vehicle, and compares the sensor information, the positioning information, the status and planning information of the planning control module, the status of the safety control module and the safety planning information to the The risk of intelligent networked vehicles is evaluated.
  • the risk assessment result includes one to six levels, and different levels indicate different degrees of risk.
  • the risk assessment result is determined by the following steps: receiving the sensing information and the positioning information, and determining risk monitoring information according to the sensing information and the positioning information; according to the risk monitoring information and Vehicle planning control information and vehicle safety control information determine the risk assessment result. Specifically, receiving the status and planning information of the planning control module, as well as the status and safety planning information of the safety control module; monitoring the environment in the feasible domain to obtain feasible domain monitoring information and status information; monitoring the environment around the vehicle body to obtain collision monitoring information and status information ; According to the sensor information, positioning information, planning control module status and planning information, safety control module status and safety planning information, feasible domain monitoring information and status information, collision monitoring information and status information to obtain risk assessment results.
  • At least one of factors such as weather conditions, vehicle speed, pedestrians/animals, etc. may be considered.
  • at least one of factors such as the current speed of the vehicle, driving style, planning information, safety planning information, risk monitoring information, and feasible region monitoring information may be comprehensively considered.
  • the risk assessment result is transmitted to the safety control module, the planning control module, and the logic arbitration module.
  • the state information of the sensor group, the perception positioning module, the planning control module, the safety control module, and the execution module is received; according to the state information, the sensor group, the perception positioning module, the planning control module, The working state of the safety control module and the execution module; the vehicle state evaluation result is determined according to the working state of the sensor group, the perception positioning module, the planning control module, the safety control module, the function evaluation module and the execution module.
  • the vehicle planning control information and the vehicle safety control information are obtained through arbitration according to the working state of the function evaluation module, the working state of the risk evaluation module, the vehicle state evaluation result, and the risk evaluation result. Vehicle execution information.
  • the vehicle planning control information and the vehicle safety control information are arbitrated according to the risk level and the function damage level in the risk assessment result, the status of the risk assessment module and the function assessment module, that is, the logical arbitration module determines that the vehicle execution information is the vehicle plan Control information or vehicle safety control information.
  • the execution module executes the execution information arbitrated by the logic arbitration module.
  • the perception and positioning module obtains perception information and positioning information according to the sensor information; the planning control module obtains the vehicle according to the perception information, positioning information, risk assessment results, and vehicle status assessment results Planning control information; the safety control module obtains vehicle safety control information according to the perception information, positioning information, risk assessment results, and vehicle status assessment results.
  • the function assessment module obtains the vehicle status assessment results according to the status of each unit, and the risk assessment module monitors according to the feasible domain Information, collision monitoring information, combined with planning control module status, planning information, safety control module status, and safety planning information to obtain risk assessment results; logical arbitration module arbitrates vehicle planning control information and vehicles based on risk assessment results, vehicle status assessment results Security control information, and send the arbitration result to the execution module for execution.
  • the decision execution information under different driving conditions is obtained from the perspectives of performance and safety at the same time. It not only considers the failure and failure of each unit and module, but also considers the driving decision risk, and reduces Response time.
  • the logical arbitration module performs arbitration based on the risk assessment results and performance assessment results, so that the vehicle execution information is more accurate, thereby reducing the driving risk of the intelligent networked vehicle.
  • FIG. 4 is a schematic structural diagram of a vehicle-mounted device provided by this embodiment.
  • the self-driving vehicle includes in-vehicle equipment, and the in-vehicle equipment includes at least one processor 401, at least one memory 402, and at least one communication interface 403.
  • the processor 401 and the memory 402 are coupled together through the bus system 404.
  • the communication interface 403 is used for information transmission with external devices.
  • the bus system 404 is used for connection and communication between various components including the processor 401 and the memory 402.
  • the bus system 404 also includes a power bus, a control bus, and a status signal bus. However, for ease of description, various buses are marked as the bus system 404 in FIG. 4.
  • the memory 402 in this embodiment may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the memory 402 stores the following elements, executable modules or data structures, or a subset of them, or an extended set of them: operating systems and applications.
  • the operating system includes various system programs, such as a framework layer, a core library layer, and a driver layer, which are used to implement various basic services and process hardware-based tasks.
  • Application programs including various application programs, such as Media Player, Browser, etc., are used to implement various application services.
  • the program for implementing the control method for the intelligent networked vehicle provided by the embodiments of the present disclosure may be included in the application program.
  • the processor 401 calls a program or instruction stored in the memory 402, specifically, a program or instruction stored in an application program, and the processor 401 is used to execute the intelligent networked vehicle provided by the embodiment of the present disclosure.
  • the control system and control method of the intelligent networked vehicle provided in this embodiment may be applied to the processor 401 or implemented by the processor 401.
  • the processor 401 may be an integrated circuit chip with signal processing capability. In the implementation process, the steps of the foregoing method can be completed by an integrated logic circuit of hardware in the processor 401 or instructions in the form of software.
  • the aforementioned processor 401 may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a ready-made programmable gate array (Field Programmable Gate Array, FPGA) or other Programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the steps of the method for controlling the intelligent networked vehicle provided in this embodiment may be directly embodied as being executed and completed by a hardware decoding processor, or executed by a combination of hardware and software units in the decoding processor.
  • the software unit may be located in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers.
  • the storage medium is located in the memory 402, and the processor 401 reads the information in the memory 402 and completes the steps of the method in combination with its hardware.
  • This embodiment also proposes a non-transitory computer-readable storage medium that stores a program or instruction that causes a computer to execute, for example, the control method of an intelligent networked vehicle. In order to avoid repeating the description, I won’t repeat them here.

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Abstract

一种用于智能网联车辆的控制系统、控制方法、车载设备和存储介质，属于自动驾驶领域，用于智能网联车辆的控制系统包括：传感器组（10），用于获得传感信息（301）；感知定位模块（202），用于基于传感信息得到感知信息和定位信息（302）；规划控制模块（203），用于基于感知信息和定位信息确定车辆规划控制信息（303）；安全控制模块（204），用于基于感知信息和定位信息确定车辆安全控制信息（304）；功能评估模块（205），用于确定车辆状态评估结果；风险评估模块（206），用于确定风险评估结果；逻辑仲裁模块（207），用于对车辆规划控制信息和车辆安全控制信息进行仲裁，并确定车辆执行信息；执行模块（208），用于基于车辆执行信息控制车辆行驶（308）。用于智能网联车辆的控制方法可以降低智能网联车辆的行车风险。

Description

用于智能网联车辆的控制系统及控制方法 技术领域
本发明涉及智能车辆技术领域，具体涉及一种用于智能网联车辆的控制系统及控制方法。
背景技术
随着智能网联车技术的不断发展，智能网联车辆也逐渐从智能体辅助驾驶转变为智能体独立驾驶。而且，智能网联车辆在不断智能化的过程中，与传统车辆产生了诸多差异。如，高等级智能网联车辆已完全脱离驾驶员控制，其发生故障时需独立判断并降级等。不可忽视的是，智能网联车辆和人类驾驶员的驾驶风格存在较大的差异，高度的智能化伴随着较高的行驶风险。因此，高等级智能网联车辆的安全性成为其能否落地的关键。
大多数智能网联车辆的控制系统仍是基于传统车辆的控制系统开发，由于控制系统架构过于简单、冗余控制过于死板，需要驾驶员或监察员辅助控制甚至接管，导致智能体在行驶过程中不能良好地自我决策、监管、故障诊断以及规避行车风险。
因此，目前的控制系统的架构无法满足高等级智能网联车辆独立驾驶的需求。如何针对高等级智能网联车辆的驾驶特性及行驶条件，制定可靠的控制系统架构，已成为目前亟待解决的关键技术问题。
发明内容
为此，本发明提供一种用于智能网联车辆的控制系统及控制方法，以解决现有技术中由于控制系统架构简单、冗余控制过于死板而导致无法满足智能网联车辆的安全性要求的问题。
为了实现上述目的，本发明第一方面提供一种用于智能网联车辆的控制系统，所述控制系统包括：
传感器组，用于获得传感信息；
感知定位模块，用于基于所述传感信息得到感知信息和定位信息；
规划控制模块，用于基于所述感知信息和定位信息、车辆状态评估结果和风险评估结果确定车辆规划控制信息，其中，所述车辆状态评估结果是由功能评估模块生成的，所述风险评估结果是由风险评估模块生成的；
安全控制模块，用于基于所述感知信息和定位信息、车辆状态评估结果和风险评估结果确定车辆安全控制信息；
功能评估模块，用于基于所述传感器组、感知定位模块、规划控制模块、安全控制模块和执行模块的状态信息确定车辆状态评估结果；
风险评估模块，用于基于所述传感信息、所述定位信息、规划控制模块的状态和规划信息、安全控制模块的状态和安全规划信息获得风险评估结果；
逻辑仲裁模块，用于基于所述功能评估模块和所述风险评估模块的状态、所述车辆状态评估结果和所述风险评估结果对所述车辆规划控制信息和所述车辆安全控制信息进行仲裁获得车辆执行信息；
执行模块，用于基于所述车辆执行信息控制车辆行驶。
为了实现上述目的，本发明第二方面提供一种用于智能网联车辆的控制方法，所述方法是基于本发明实施例提供所述控制系统提出的控制方法，其包括：
获取传感信息；
根据所述传感信息得到感知信息和定位信息；
根据所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定车辆规划控制信息；
根据所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定车辆安全控制信息；
根据车辆自身的状态信息获得车辆状态评估结果；
根据所述传感信息、所述定位信息、车辆规划控制信息和车辆安全控制信息确定风险评估结果；
根据功能评估模块的工作状态、风险评估模块的工作状态对所述车辆规划控制信息和所述车辆安全控制信息进行仲裁，并确定车辆执行信息；
执行所述车辆执行信息。
为了实现上述目的，本发明第三方面提供一种车载设备，包括：处理器、存储器和通信接口，所述通信接口数据连接所述处理器和所述存储器；
所述处理器通过调用所述存储器存储的程序或指令，用于执行本发明实施例提供的控制方法的步骤。
为了实现上述目的，本发明第四方面提供一种非暂态计算机可读存储介质，所述非暂态计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行本发明实施例提供的控制方法的步骤。
本发明提供的用于智能网联车辆的控制系统，规划控制模块获基于感知信息和定位信息确定车辆规划控制信息，安全控制模块基于感知信息和定位信息确定车辆安全控制信息，功能评估模块确定车辆状态评估结果，风险评估模块确定风险评估结果，逻辑仲裁模块基于功能评估模块的工作状态和风险评估模块的工作状态，对车辆规划控制信息和车辆安全控制信息进行仲裁，并确定车辆执行信息，使得车辆执行信息更准确，从而降低智能网联车辆的行车风险。
附图说明
附图是用来提供对本发明的进一步理解，并且构成说明书的一部分，与下面的具体实施方式一起用于解释本发明，但并不构成对本发明的限制。
图1为本发明实施例提供的一种智能网联车辆的整体架构图；
图2为本发明实施例提供的一种用于智能网联车辆的控制系统的示例性框图；
图3为本实施例还提供一种用于智能网联车辆的控制方法的流程图；
图4为本实施例提供的一种车载设备的结构示意图。
具体实施方式
以下结合附图对本发明的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本发明，并不用于限制本发明。
针对智能网联车辆的安全架构仍然沿用传统车辆的安全架构导致智能网联车辆的安全性差的问题，本实施例提供一种用于智能网联车辆的控制系统和控制方法，车辆控制指令是结合状态评估结果、风险评估结果、车辆规划控制信息和车辆安全控制信息确定，提高了车辆控制指令的准确率，从而提高了智能网联车辆的安全性。
图1为本实施例提供的一种智能网联车辆的整体架构图。如图1所示，智能网联车辆包括传感器组10、智能驾驶系统20、车辆执行系统30和云端服务器40，智能驾驶系统20与云端服务器40能够进行通信。
传感器组10用于获得传感信息。传感器组包括但不限于摄像头、激光雷达、毫米波雷达、全球定位系统(Global Positioning System，GPS)、压力传感器、IMU、角度传感器和速度传感器中的至少一个。
智能驾驶系统用于接收传感器组的传感信息，并基于传感信息生成执行信息。
车辆执行系统用于接收执行信息，按照执行信息控制车辆行驶。在一些实施例中，车辆执行系统包括但不限于转向系统、制动系统和驱动系统。转向系统、制动系统和驱动系统属于车辆领域成熟系统，在此不再赘述。
云端服务器与智能驾驶系统进行通信，用于统筹协调管理智能网联车辆。在一些实施例中，云端服务器可用于与一个或多个智能网联车辆进行交互，统筹协调管理多个智能网联车辆的调度等。在一些实施例中，车辆控制系统20与云端服务器通过无线通讯网络(包括但不限于GPRS网络、Zigbee网络、Wifi网络、3G网络、4G网络、5G网络等无线通讯网络)进行无线通信。
在一些实施例中，云端服务器是由车辆服务商所建立的云端服 务器，提供云存储和云计算的功能。在一些实施例中，在云端服务器中建立车辆端档案。在一些实施例中，车辆端档案中储存车辆控制系统20上传的各种信息。在一些实施例中，云端服务器可以实时同步车辆端产生的驾驶数据。
在一些实施例中，云端服务器可包括数据仓库和数据加工平台，其中数据仓库中存储云端服务器建立的车辆端档案。在一些实施例中，数据仓库可以从各种源头业务系统中把数据统一采集到数据仓库中，并在数据加工平台进行加工，以便车辆端使用。
在一些实施例中，云端服务器可以是一个服务器，也可以是一个服务器群组。服务器群组可以是集中式的，也可以是分布式的。分布式服务器，有利于任务在多个分布式服务器进行分配与优化，克服传统集中式服务器资源紧张与响应瓶颈的缺陷。在一些实施例中，云端服务器可以是本地的或远程的。
在一些实施例中，云端服务器可用于获取道路监测单元(RSU：Road Side Unit)和智能网联车辆的信息，以及可以发送信息至智能网联车辆。在一些实施例中，云端服务器可以根据智能网联车辆的信息将道路监测单元中的与智能网联车辆相对应的检测信息发送给智能网联车辆。
在一些实施例中，智能网联车辆还可包括车辆CAN总线，车辆CAN总线连接车辆控制系统20和车辆执行系统30。智能驾驶系统10与车辆底层执行系统之间的信息交互通过车辆CAN总线进行传递。
在一些实施例中，智能网联车辆既可以通过驾驶员以人工驾驶模式控制车辆行驶，又可以通过车辆控制系统20以无人驾驶方式控制车辆行驶。在人工驾驶模式下，驾驶员通过操作控制车辆行驶的装置驾驶车辆，控制车辆行驶的装置例如包括但不限于制动踏板、方向盘和油门踏板等。控制车辆行驶的装置可直接操作车辆底层执行系统控制车辆行驶。
在一些实施例中，智能网联车辆也可以为无人车，车辆的驾驶控制由智能控制系统20输出控制指令，并由车辆执行系统30来执行。
图2为本发明实施例提供的一种用于智能网联车辆的控制系统 的示例性框图。在一些实施例中，该控制系统可以实现图1中智能控制系统20的部分功能，用于控制智能网联车辆行驶。
如图2所示，用于智能网联车辆的控制系统可划分为传感接收模块201、感知定位模块202、规划控制模块203、安全控制模块204、功能评估模块205、风险评估模块206、逻辑仲裁模块207、执行模块208以及其他一些可用于智能网联车辆的单元。
传感接收模块201用于接收传感器组的传感信息，并将所述传感信息传送给感知定位模块202、规划控制模块203、安全控制模块204、功能评估模块205、风险评估模块206模块。其中，所述传感器组包括但不限于摄像头、激光雷达、毫米波雷达、压力传感器、IMU、角度传感器、速度传感器等中的一种或多种；所述传感信息包括环境信息和车辆状态信息。其中，环境信息包括障碍物、行人、周围车辆、可行驶区域、道路标记等。车辆状态信息包括车速、前轮偏角、加速度、减速度、方向盘转角、刹车、油门等状态。
在一些实施例中，传感器组还可以监测自身状态信息，并将自身状态信息通过传感接收模块201发送给功能评估模块205。所述自身状态信息包括传感器组中各个传感器的工作状态等。
感知定位模块202，用于基于所述传感信息确定感知信息和定位信息。感知定位模块202还进一步感知自身的状态，并将状态信息传送给规划控制模块203、安全控制模块204、功能评估模块205和风险评估模块206。
在一些实施例中，感知定位模块202包括感知单元2021和定位单元2022。其中，感知单元2021获得感知信息。具体地，感知单元2021用于基于传感信息感知车辆自身状态和车外环境，得到感知信息。在一些实施例中，感知信息包括车辆自身状态信息，如车辆行驶速度、车辆加速度、车辆中硬件工作状态等车辆状态信息。在一些实施例中，感知信息还包括车外环境信息，如车辆可行驶区域、障碍物、车辆周围行人和其他车辆等信息。
在一些实施例中，定位单元2022用于基于所述传感信息获取获得车辆的位置信息，得到定位信息。在一些实施例中，定位单元2022 基于GPS、IMU、标识定位模块等获得车辆的位置信息。在一些实施例中，定位信息还可以通过视觉传感器、激光雷达等进行定位，例如通过V-SLAM、Lidar-SLAM等方式获取。
规划控制模块203基于所述感知信息和定位信息确定车辆规划控制信息。其中，所述车辆规划控制信息基于行驶舒适性、时效性以及适用性生成。在一些实施例中，规划控制模块还可以进一步结合V2X数据、高精度地图等数据中的至少一种，进行路径规划和决策。在一些实施例中，所述规划控制模块还接收车辆状态评估结果和/或风险评估结果确定车辆规划控制信息，并基于所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定车辆规划控制信息。
在一些实施例中，车辆规划控制信息包括车辆的车速、前轮偏角、加速度、减速度、方向盘转角、刹车、油门等控制信息。
在一些实施例中，规划控制模块203包括规划单元2031和规划运动控制单元2032。其中，规划单元2031用于生成规划信息。在一些实施例中，所述规划单元基于感知模块和定位模块生成的感知定位信息生成规划信息。所述规划单元还可以结合V2X数据、高精度地图等数据中的至少一种，生成规划信息。在一些实施例中，所述规划信息包括但不限于：期望路径、行为(例如包括但不限于跟车、超车、停车、绕行等)、车辆航向、车辆速度、车辆的期望加速度、期望的方向盘转角等。在一些实施例中，所述规划单元接收来自车辆状态评估结果和/或风险评估结果确定规划信息。所述规划单元基于乘客舒适度进行规划，其中，所述乘客舒适度包括行驶舒适性，行驶时效性，行驶适用性等。规划单元将生成的规划和决策传输至规划运动控制单元。
在一些实施例中，规划单元2031将获得的规划信息及规划单元2031的性能传送至规划运动控制单元2032。
规划运动控制单元2032，用于基于所述规划信息确定车辆规划控制信息。其中，所述车辆规划控制信息是指车辆底层控制系统的执行信息。在一些实施例中，规划运动控制单元下发车辆控制信息以使车辆底层执行系统控制车辆按照期望路径行驶，例如通过控制方向盘、刹车以及油门对车辆进行横向和纵向控制。例如，规划最大加速度不 超过5m/S 2，最大转向角不超过15°。
安全控制模块204用于基于所述感知信息和定位信息生成车辆安全控制信息。其中，所述车辆安全控制信息是基于行驶安全性、稳定性及碰撞后果生成。在一些实施例中，安全控制模块进一步基于车辆状态评估结果和风险评估结果确定车辆安全控制信息，即基于感知信息和定位信息、车辆状态评估结果和风险评估结果确定车辆安全控制信息，为智联网联车辆提供高保障的控制决策。
在一些实施例中，安全控制模块204包括规划单元2041和安全行为控制单元2042。其中，规划单元2041用于生成安全规划信息。在一些实施例中，所述规划单元2041基于感知模块和定位模块生成的感知定位信息生成安全规划信息。所述规划单元2041还可以结合V2X数据、高精度地图等数据中的至少一种，生成安全规划信息。在一些实施例中，所述安全规划信息包括但不限于：行为(例如包括但不限于跟车、超车、停车、绕行等)、车辆航向、车辆速度、车辆的期望加速度、期望的方向盘转角等。在一些实施例中，所述安全规划单元接收来自车辆状态评估结果和/或风险评估结果确定安全规划信息。所述安全规划单元基于行驶安全性、稳定性及碰撞后果对车辆进行行驶规划。安全规划单元将生成的规划和决策传输至规划运动控制单元。
安全行为控制单元2042，用于对基于所述安全规划信息确定车辆安全控制信息。其中，安全规划控制信息是指车辆底层控制系统的执行信息。在一些实施例中，安全行为控制单元下发车辆控制信息以使车辆底层执行系统控制车辆按照期望路径行驶，例如通过控制方向盘、刹车以及油门对车辆进行横向和纵向控制。在一些实施例中，在生成车辆安全控制信息时，车辆安全规划信息考虑但不限于车辆滑移率、横摆角、侧倾角等安全因素。例如将车辆滑移率不超过20％，车辆横摆角和侧倾角保持在安全范围内。
功能评估模块205用于生成车辆状态评估结果。其中功能评估模块205实时监测所述传感器组201、感知定位模块202、规划控制模块203、安全控制模块204和执行模块208的工作状态，确定第一监测结果，并基于第一监测结果对上述功能模块的功能进行评估，获 得车辆状态评估结果。其中，第一监测结果包括但不限于软件、硬件的故障监测和功能失效监测结果。
在一些实施例中，功能评估模块205实时监测所述传感器组201、感知定位模块202、规划控制模块203、安全控制模块204和执行模块208的状态，获得第一监测结果，再根据第一监测结果上述模块损坏的严重性进行分级评估，获得车辆状态评估结果。在一些实施例中，第一监测结果包括但不限于软、硬件的故障监测和功能失效监测结果。
在一些实施例中，功能评估模块205包括功能监测单元2051和功能评估单元2052。其中，功能监测单元用于生成第一监测信息。所述第一监测信息包括对于功能模块的软硬件故障监测信息和功能失效检测信息。其中，所述功能模块包括传感器组201、感知定位模块202、规划控制模块203、安全控制模块204、功能评估模块205和执行模块208。
在一些实施例中，功能监测单元包括故障监测子单元301和功能监测子单元302，其中，故障监测子单元301，用于实时监测所述传感器组201、感知定位模块202、规划控制模块203、安全控制模块204、功能评估模块205和执行模块208的状态，获得对应的故障监测信息。功能监测子单元302，用于实时监测所述传感器组201、感知定位模块202、规划控制模块203、安全控制模块204、功能评估模块205和执行模块208的状态，以判断是否失效。
功能评估单元2052，用于确定车辆状态评估结果。在一些实施例中，功能评估单元2052，用于根据所述第一监测结果对所述车辆自身的安全性进行评价，确定车辆状态评估结果。在一些实施例中，功能评估单元2052将车辆状态评估结果发送至规划控制模块203和安全控制模块204。在一些实施例中，当功能评估单元2052确定的车辆状态评估结果发送变化时，规划控制模块203依据车辆状态评估结果修改车辆规划控制信息。在一些实施例中，安全控制模块204依据车辆状态评估结果修改车辆安全控制信息。
风险评估模块206用于确定风险评估结果。在一些实施例中，风险评估模块206用于基于所述传感信息和所述定位信息、规划控 制模块的状态和规划信息、安全控制模块的状态和安全规划信息获得风险评估结果。
在一些实施例中，风险评估模块206包括风险监测单元和风险评估单元。在一些实施例中，所述风险监测单元基于所述传感信息和定位信息确定风险监测信息。所述风险监测信息包括但不限于碰撞监测信息、可行域监测信息等。在一些实施例中，所述风险评估模块可以采用与所述感知定位模块相互独立的传感器，从而保证风险评估的独立性。
所述风险监测单元确定风险监测信息后，将风险监测信息发送至风险评估单元。
风险监测单元包括可行域监测子单元401和碰撞监测子单元402。可行域监测子单元401，用于监测可行域内的环境获得可行域监测信息和可行域监测子单元的状态信息。在一些实施例中，碰撞监测子单元402，用于监测车身周围的环境获得碰撞监测信息和碰撞监测子单元的工作状态。在一些实施例中，碰撞监测信息可以根据传感信息、定位信息和实时监测周围环境获得。在一些实施例中，碰撞监测信息通过独立的传感器直接获得。在一些实施例中，可行域监测信息包括但不限于天气、速度、行人/动物等因素。
风险评估单元2062，用于根据所述风险监测信息、所述车辆规划控制信息及所述车辆安全控制信息确定风险评估结果。在一些实施例中，风险评估单元进一步根据风险监测单元、规划单元、安全规划单元的工作状态确定风险评估结果。
在一些实施例中，风险评估单元2402还可以结合车辆速度、驾驶风格、天气、路况复杂度等信息对车辆中整体风险进行评估，确定风险评估结果。在一些实施例中，风险评估单元2062将所述风险评估结果发送至规划单元2031、安全规划单元2041和逻辑仲裁模块207。其中，规划控制模块203根据风险评估结果修改车辆规划控制信息。安全控制模块204依据风险评估结果修改车辆安全控制信息。例如，当风险评估结果为2级风险等级时，需要限定最低时速，以满足时效性。规划控制模块203和安全控制模块204可以对应地修改 车辆规划控制信息和安全控制信息，将最低时速限定在90km/h，并基于该最低时速对应地修改车辆规划控制信息和车辆安全控制信息。
在一些实施例中，规划控制模块203和安全控制模块204还可以限定最大转向角度，如将最大转向角度限定为15°，并基于该最大转向角度修改修改车辆规划控制信息和车辆安全控制信息。
逻辑仲裁模块207用于确定车辆执行信息。其中，所述逻辑仲裁模块基于所述车辆规划控制信息和安全控制信息确定车辆执行信息。在一些实施例中，逻辑仲裁模块进一步根据所述功能评估模块的工作状态和所述风险评估模块的工作状态确定车辆执行信息。
例如，逻辑仲裁模块207基于风险评估结果中风险等级较低，逻辑仲裁模块可确定规划控制信息为执行信息；当风险等级较高时，可确定安全控制信息为执行信息。当所述风险等级威胁车辆安全或乘客人身安全时，可选择尽快减速或尽快停车。基于功能评估结果中存在功能模块障碍或失效时，可控制车辆减速或停车。
在一些实施例中，在智能网联车辆风险等级较低时，如风险等级低于2级，逻辑仲裁模块207可以选择车辆规划控制信息。在一些实施例中，在智能网联车辆风险等级较高时，如风险等级为4级，逻辑仲裁模块207可以选择车辆安全控制信息。在一些实施例中，在智能网联车辆存在更高风险时，如风险等级在5级以上，逻辑仲裁模块207可以选择暂时保持车速，并寻求时机临时停车；或者可以选择降低车速，并寻求时机临时停车。在一些实施例中，在智能网联车辆存在更高风险时，如风险等级在5级以上，逻辑仲裁模块207可以选择以最大减速度减速，如以12.5m/S 2的减速度减速。
执行模块208，用于基于所述车辆执行信息控制车辆行驶。在一些实施例中，执行模块208接收逻辑仲裁模块207发出的车辆执行信息，并基于车辆执行信息控制车辆行驶。在一些实施例中，执行模块208包括但不限于底盘、转向系统、动力系统、制动系统及其车辆其他硬件。在一些实施例中，执行模块解析所述车辆执行信息，并分别向车辆各硬件发送匹配的执行信号。执行模块还可以监测自身工作状态，并发送工作状态至功能评估模块。
需要说明的是，本实施方式中所涉及到的各模块均为逻辑模块，在实际应用中，一个逻辑单元可以是一个物理单元，也可以是一个物理单元的一部分，还可以以多个物理单元的组合实现。此外，为了突出本发明的创新部分，本实施方式中并没有将与解决本发明所提出的技术问题关系不太密切的单元引入，但这并不表明本实施方式中不存在其它的单元。
图3为本实施例还提供一种用于智能网联车辆的控制方法的流程图。该控制方法的执行主体是本实施例提供的控制系统，控制系统的具体结构在此不再赘述。在一些实施例中，本实施例提供的控制方法可应用于智能网联自动驾驶车辆。
如图3所示，用于智能网联车辆的控制方法可包括以下步骤：
301，获取传感信息。
传感信息是由传感器组获得，并经传感接收模块传送至智能控制系统。传感信息包括环境信息和车辆状态信息。其中，环境信息包括障碍物、行人、周围车辆、可行驶区域、道路标记等。车辆状态信息包括车速、前轮偏角、加速度、减速度、方向盘转角、刹车、油门等状态。
在一些实施例中，传感器组还监测自身状态信息，并将自身状态信息通过传感接收模块传送至智能控制系统。其中，自身状态信息包括传感器组中各个传感器的工作状态等。
302，基于所述传感信息获得感知信息和定位信息。
其中，感知信息包括车辆自身状态信息，如车辆行驶速度、车辆加速度、车辆中硬件工作状态等车辆状态信息。在一些实施例中，感知信息还包括车外环境信息，如车辆可行驶区域、障碍物、车辆周围行人和其他车辆等信息。
定位信息基于GPS、IMU、标识定位模块等获得车辆的位置信息。在一些实施例中，定位信息还可以通过视觉传感器、激光雷达等进行定位，例如通过V-SLAM、Lidar-SLAM等方式获取。
303，基于感知信息和定位信息确定车辆规划控制信息。
其中，车辆规划控制信息包括车辆的车速、前轮偏角、加速度、 减速度、方向盘转角、刹车、油门等控制信息。
在一些实施例中，根据所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定车辆规划控制信息。
具体地，接收所述感知信息、定位信息、车辆状态评估结果和风险评估结果；根据所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定规划信息；对所述规划信息进行解析获得车辆规划控制信息。
304，基于感知信息和定位信息确定车辆安全控制信息。
其中，安全规划信息是基于所述感知信息和定位信息生成的信息。安全规划信息是根据感知信息和定位信息、并进一步结合V2X数据、高精度地图等数据中的至少一种生成的信息。
在一些实施例中，根据所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定车辆安全控制信息。
具体地，接收所述感知信息、定位信息、车辆状态评估结果和风险评估结果；根据所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定安全规划信息；对所述安全规划信息进行解析获得车辆安全控制信息。
305，基于所述传感信息、所述定位信息、车辆规划控制信息、和车辆安全控制信息确定风险评估结果。
在一些实施例中，根据所述传感信息、所述定位信息、规划控制模块的工作状态和规划信息、安全控制模块的工作状态和安全规划信息确定风险评估结果。在一些实施例中，风险评估模块对智能网联车辆的行驶环境进行监测，并根据传感信息、所述定位信息、规划控制模块的状态和规划信息、安全控制模块的状态和安全规划信息对智能网联车辆的风险性进行评估。在一些实施例中，风险评估结果包括一到六个等级，不同的等级表示风险程度不同。
在一些实施例中，风险评估结果通过以下步骤确定：接收所述传感信息和所述定位信息，并依据所述传感信息和所述定位信息确定风险监测信息；根据所述风险监测信息和车辆规划控制信息、车辆安全控制信息确定风险评估结果。具体地，接收规划控制模块的状态和规 划信息、以及安全控制模块的状态和安全规划信息；监测可行域内的环境获得可行域监测信息和状态信息；监测车身周围的环境获得碰撞监测信息和状态信息；根据所述传感信息、定位信息、规划控制模块的状态和规划信息、安全控制模块的状态和安全规划信息、可行域监测信息和状态信息、碰撞监测信息和状态信息获得风险评估结果。
在一些实施例中，在获得可行域监测信息时，可以考虑天气状况、车辆速度、行人/动物等因素中的至少一种。在一些实施例中，在获得风险评估结果时，可以综合考虑车辆当前速度、驾驶风格、规划信息、安全规划信息、风险监测信息、可行域监测信息等因素中的至少一种。
在一些实施例中，风险评估结果被传送至安全控制模块、规划控制模块和逻辑仲裁模块。
306，基于车辆自身的状态信息获得车辆状态评估结果。
在一些实施例中，接收所述传感器组、感知定位模块、规划控制模块、安全控制模块和执行模块的状态信息；依据所述状态信息，获得所述传感器组、感知定位模块、规划控制模块、安全控制模块和执行模块的工作状态；依据所述传感器组、感知定位模块、规划控制模块、安全控制模块、功能评估模块和执行模块的工作状态确定车辆状态评估结果。
307，基于风险评估结果、车辆状态评估结果、车辆规划控制信息、车辆安全控制信息进行仲裁，确定车辆执行信息。
在一些实施例中，根据功能评估模块的工作状态、风险评估模块的工作状态、所述车辆状态评估结果和所述风险评估结果对所述车辆规划控制信息和所述车辆安全控制信息进行仲裁获得车辆执行信息。
在一些实施例中，根据风险评估结果中的风险等级和功能损坏等级、风险评估模块和功能评估模块的状态仲裁车辆规划控制信息和车辆安全控制信息，即逻辑仲裁模块确定车辆执行信息为车辆规划控制信息或车辆安全控制信息。
308，基于所述车辆执行信息控制车辆行驶。在一些实施例中，执行模块执行逻辑仲裁模块仲裁的执行信息。
上面各种方法的步骤划分，只是为了描述清楚，实现时可以合并为一个步骤或者对某些步骤进行拆分，分解为多个步骤，只要包括相同的逻辑关系，都在本专利的保护范围内；对算法中或者流程中添加无关紧要的修改或者引入无关紧要的设计，但不改变其算法和流程的核心设计都在该专利的保护范围内。
本实施例提供的用于智能网联车辆的控制方法，感知定位模块依据传感信息获得感知信息和定位信息；规划控制模块根据感知信息、定位信息、风险评估结果、车辆状态评估结果得出车辆规划控制信息；安全控制模块根据感知信息、定位信息、风险评估结果、车辆状态评估结果得出车辆安全控制信息，功能评估模块根据各单元的状态获得车辆状态评估结果，风险评估模块根据可行域监测信息、碰撞监测信息并结合规划控制模块的状态、规划信息、安全控制模块的状态和安全规划信息得出风险评估结果；逻辑仲裁模块根据风险评估结果、车辆状态评估结果仲裁车辆规划控制信息和车辆安全控制信息，并将仲裁结果发送至执行模块执行。通过规划控制模块和安全控制模块同时从性能和安全两个角度得出不同驾驶条件下的决策执行信息，不仅考虑了各单元、模块的故障、失效等问题，还考虑了行驶决策风险，而且减少了响应时间。逻辑仲裁模块依据风险评估结果和性能评估结果进行仲裁，使得车辆执行信息更准确，从而降低智能网联车辆的行车风险。
图4为本实施例提供的一种车载设备的结构示意图。自动驾驶车辆包括车载设备，车载设备包括至少一个处理器401、至少一个存储器402和至少一个通信接口403。处理器401和存储器402通过总线系统404耦合在一起。通信接口403，用于与外部设备之间的信息传输。可理解地，总线系统404用于包括处理器401和存储器402在内的各组件之间的连接通信。总线系统404除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。但为便于说明，在图4中将各种总线都标为总线系统404。
本实施例中的存储器402可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。
在一些实施方式中，存储器402存储了如下的元素，可执行模块或者数据结构，或者他们的子集，或者他们的扩展集：操作系统和应用程序。
其中，操作系统，包含各种系统程序，例如框架层、核心库层、驱动层等，用于实现各种基础业务以及处理基于硬件的任务。应用程序，包含各种应用程序，例如媒体播放器(Media Player)、浏览器(Browser)等，用于实现各种应用业务。实现本公开实施例提供的用于智能网联车辆的控制方法的程序可以包含在应用程序中。
在本实施例中，处理器401通过调用存储器402存储的程序或指令，具体的，可以是应用程序中存储的程序或指令，处理器401用于执行本公开实施例提供的智能网联车辆的控制系统和控制方法各实施例的步骤。
本实施例提供的智能网联车辆的控制系统和控制方法可以应用于处理器401中，或者由处理器401实现。处理器401可以是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法的各步骤可以通过处理器401中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器401可以是通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
本实施例提供的智能网联车辆的控制方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件单元组合执行完成。软件单元可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器402，处理器401读取存储器402中的信息，结合其硬件完成方法的步骤。
本实施例还提出一种非暂态计算机可读存储介质，所述非暂态计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行 如智能网联车辆的控制方法各实施例的步骤，为避免重复描述，在此不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
可以理解的是，以上实施方式仅仅是为了说明本发明的原理而采用的示例性实施方式，然而本发明并不局限于此。对于本领域内的普通技术人员而言，在不脱离本发明的精神和实质的情况下，可以作出各种变型和改进，这些变型和改进也视为本发明的保护范围。

Claims (16)

  1. 一种用于智能网联车辆的控制系统，其特征在于，所述控制系统包括传感器组、感知定位模块、规划控制模块、安全控制模块、功能评估模块、风险评估模块、逻辑仲裁模块及执行模块；其中，传感器组用于获得传感信息；
    感知定位模块，用于基于所述传感信息得到感知信息和定位信息；
    规划控制模块，用于基于所述感知信息和定位信息确定车辆规划控制信息；
    安全控制模块，用于基于所述感知信息和定位信息确定车辆安全控制信息；
    功能评估模块，用于确定车辆状态评估结果；
    风险评估模块，用于确定风险评估结果；
    逻辑仲裁模块，用于基于所述功能评估模块的工作状态和所述风险评估模块的工作状态对所述车辆规划控制信息和所述车辆安全控制信息进行仲裁，并确定车辆执行信息；
    执行模块，用于基于所述车辆执行信息控制车辆行驶。
  2. 根据权利要求1所述的控制系统，其特征在于，所述感知定位模块包括：
    感知单元，用于基于所述传感信息感知车辆自身状态和车外环境，得到所述感知信息；
    定位单元，用于基于所述传感信息获取车辆的位置信息，得到所述定位信息。
  3. 根据权利要求1所述的控制系统，其特征在于，包括：
    所述规划控制模块基于行驶舒适性、时效性、适用性对车辆进行行驶规划；
    所述安全控制模块基于行驶安全性、稳定性及碰撞后果对车辆 进行行驶规划。
  4. 根据权利要求1所述的控制系统，其特征在于，所述规划控制模块包括规划单元和规划运动控制单元，其中，
    所述规划单元用于根据所述感知信息、所述定位信息、所述车辆状态评估结果及所述风险评估结果确定规划信息；
    规划运动控制单元，用于基于所述规划信息确定车辆规划控制信息。
  5. 根据权利要求1所述的控制系统，其特征在于，所述安全控制模块包括：安全规划单元和安全行为控制单元，其中
    安全规划单元用于根据所述感知信息、所述定位信息、所述车辆状态评估结果及所述风险评估结果确定安全规划信息；
    安全行为控制单元，用于对基于所述安全规划信息确定车辆安全控制信息。
  6. 根据权利要求1所述的控制系统，其特征在于，所述功能评估模块包括功能监测单元和功能评估单元，其中，
    功能监测单元，用于实时监测所述传感器组、感知定位模块、规划控制模块、安全控制模块、功能评估模块及执行模块的工作状态；
    功能评估单元，用于基于各个模块的工作状态确定车辆状态评估结果。
  7. 根据权利要求6所述的控制系统，其特征在于，所述功能监测单元包括：
    故障监测子单元，用于监测所述传感器组、感知定位模块、规划控制模块、安全控制模块、功能评估模块及执行模块是否发生故障；
    功能监测子单元，用于监测所述传感器组、感知定位模块、规划控制模块、安全控制模块、功能评估模块及执行模块是否失效。
  8. 根据权利要求1所述的控制系统，其特征在于，所述风险评估模块包括风险监测单元和风险评估单元，其中，
    风险监测单元，用于根据所述传感信息和所述定位信息确定风险监测信息；
    风险评估单元，用于根据所述风险监测信息、所述车辆规划控制信息及所述车辆安全控制信息确定风险评估结果。
  9. 根据权利要求8所述的控制系统，其特征在于，所述风险监测单元包括：
    可行域监测子单元，用于监测可行域内的环境获得可行域监测信息和可行域监测子单元的状态信息；
    碰撞监测子单元，用于监测车身周围的环境获得碰撞监测信息和碰撞监测子单元的状态信息。
  10. 一种用于智能网联车辆的控制方法，其特征在于，所述方法是基于所述权利要求1-9任意一项所述控制系统的控制方法，其包括：
    获取传感信息；
    基于所述传感信息得到感知信息和定位信息；
    基于所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定车辆规划控制信息；
    基于所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定车辆安全控制信息；
    基于车辆自身的状态信息获得车辆状态评估结果；
    基于所述传感信息、所述定位信息、车辆规划控制信息、和车辆安全控制信息确定风险评估结果；
    基于风险评估结果、车辆状态评估结果、车辆规划控制信息、车辆安全控制信息进行仲裁，确定车辆执行信息；
    基于所述车辆执行信息控制车辆行驶。
  11. 根据权利要求10所述的控制方法，其特征在于，所述根据所述感知信息和定位信息确定车辆规划控制信息，包括：
    接收所述感知信息、定位信息、车辆状态评估结果和风险评估结果；
    根据所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定规划信息；
    对所述规划信息进行解析获得车辆规划控制信息。
  12. 根据权利要求10所述的控制方法，其特征在于，所述根据所述感知信息和定位信息确定车辆安全控制信息，包括：
    接收所述感知信息、定位信息、车辆状态评估结果和风险评估结果；
    根据所述感知信息、定位信息、车辆状态评估结果和风险评估结果确定安全规划信息；
    对所述安全规划信息进行解析获得车辆安全控制信息。
  13. 根据权利要求10所述的控制方法，其特征在于，所述根据车辆自身的状态信息获得车辆状态评估结果，包括：
    接收所述传感器组、感知定位模块、规划控制模块、安全控制模块和执行模块的状态信息；
    依据所述状态信息，获得所述传感器组、感知定位模块、规划控制模块、安全控制模块和执行模块的工作状态；
    依据所述传感器组、感知定位模块、规划控制模块、安全控制模块、功能评估模块和执行模块的工作状态确定车辆状态评估结果。
  14. 根据权利要求10所述的控制方法，其特征在于，所述根据所述传感信息、所述定位信息、规划控制模块的状态和规划信息、安全控制模块的状态和安全规划信息确定风险评估结果，包括：
    接收所述传感信息和所述定位信息，并依据所述传感信息和所述定位信息确定风险监测信息；
    根据所述风险监测信息和车辆规划控制信息、车辆安全控制信息确定风险评估结果。
  15. 一种车载设备，其特征在于，包括：处理器、存储器和通信接口，所述通信接口数据连接所述处理器和所述存储器；
    所述处理器通过调用所述存储器存储的程序或指令，用于执行如权利要求10至14任一项所述方法的步骤。
  16. 一种非暂态计算机可读存储介质，其特征在于，所述非暂态计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行如权利要求10至15任一项所述方法的步骤。
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