WO2020187254A1 - 自动驾驶车辆的纵向控制方法*** - Google Patents

自动驾驶车辆的纵向控制方法*** Download PDF

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Publication number
WO2020187254A1
WO2020187254A1 PCT/CN2020/079953 CN2020079953W WO2020187254A1 WO 2020187254 A1 WO2020187254 A1 WO 2020187254A1 CN 2020079953 W CN2020079953 W CN 2020079953W WO 2020187254 A1 WO2020187254 A1 WO 2020187254A1
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vehicle
speed
acceleration
mode
distance
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PCT/CN2020/079953
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English (en)
French (fr)
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张凯
和林
甄龙豹
葛建勇
王天培
魏松波
常仕伟
张健
关欣
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长城汽车股份有限公司
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Priority to EP20774774.2A priority Critical patent/EP3939844A4/en
Publication of WO2020187254A1 publication Critical patent/WO2020187254A1/zh

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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
    • 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
    • B60W30/14—Adaptive cruise control
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
    • B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
    • 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
    • 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
    • B60W30/14—Adaptive cruise control
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
    • 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
    • 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
    • B60W30/14—Adaptive cruise control
    • B60W30/143—Speed control
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/107—Longitudinal acceleration
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/802—Longitudinal distance
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/804—Relative longitudinal speed
    • 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
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed
    • 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
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed
    • B60W2720/106—Longitudinal acceleration

Definitions

  • the invention relates to the field of intelligent transportation, in particular to a longitudinal control method and system of an automatic driving vehicle.
  • Autonomous driving vehicles realize automated driving through intelligent driving systems, including various sensor systems installed around the body to perceive external environmental information and vehicle information, and then fusion and decision-making on the input information (corresponding fusion system And decision-making system), according to different driving conditions to plan a safe route that can be driven, and through the control system to monitor and control the safe driving of the vehicle in real time to ensure the highly automated driving of the vehicle.
  • the control system is the core part of the intelligent driving system, and its performance directly determines the safety and automation standards of the vehicle. Therefore, it has always been the key and difficult point for various companies to develop and overcome.
  • the control system is divided into two parts: the lateral control system and the longitudinal control system.
  • the lateral control system mainly realizes real-time steering control of the autonomous vehicle through a series of control algorithms, so that the vehicle can maintain lanes and automatically change lanes according to a known planned driving route. Road, dynamic obstacle avoidance, U-turn and turning, etc.
  • the longitudinal control system mainly controls the acceleration and deceleration of the vehicle, so that the self-driving vehicle can move longitudinally at a certain safe driving speed, and realize automatic start and stop, follow and cruise, etc. .
  • the entire control system can realize automatic control of the steering and speed of the vehicle at the same time.
  • the longitudinal motion is divided into three types according to the motion state: cruise, follow, and AEB (Autonomous Emergency Braking).
  • Cruising means that there is no preceding vehicle within the action distance (this action distance is recorded as ActDis_m, which refers to the minimum distance threshold for the automatic driving vehicle to switch from cruise to following, and is related to the speed of the vehicle and the speed of the preceding vehicle).
  • Driving at the highest speed. Following means that there is the vehicle ahead within the operating distance of the lane, and the vehicle follows the vehicle ahead when not changing lanes.
  • AEB refers to the behavior of a rear-end collision or collision that may endanger the driver, passengers and pedestrians when the driving environment around the vehicle changes, and the AEB state will brake at a large deceleration to avoid or slow down The occurrence of a car accident.
  • the current longitudinal control scheme does not consider the various different working conditions of the vehicle in each state, and only provides a single control scheme for following or cruise, which cannot achieve a better control effect.
  • the present invention aims to propose a longitudinal control method for an autonomous vehicle to at least partially solve the above technical problems.
  • a longitudinal control method of an automatic driving vehicle includes:
  • the longitudinal mode including cruise mode, follow mode and automatic emergency braking AEB mode;
  • the autonomous vehicle When the autonomous vehicle is in the cruise mode, execute: obtain the current vehicle speed when the vehicle is in the cruise mode, and calculate the relative speed difference between the current vehicle speed and the highest vehicle speed at which the vehicle can travel; correct the The relative speed difference is such that the change range of the relative speed difference in the control period is within a preset range; the acceleration of the vehicle is calculated according to the corrected relative speed difference; and the acceleration of the vehicle is adjusted based on the acceleration of the vehicle.
  • Vehicle speed in cruise mode When the autonomous vehicle is in the cruise mode.
  • the correspondence between different operating conditions and different control algorithms of the autonomous vehicle in the following mode includes any one or more of the following: the speed of the preceding vehicle is less than the speed of the own vehicle and the preceding vehicle is relative to the own vehicle The first operating condition where the actual distance between the two vehicles is less than the expected distance, and the first control algorithm for controlling the vehicle to decelerate at the first acceleration in the first operating condition; the speed of the preceding vehicle is less than the speed of the vehicle and the preceding vehicle The second operating condition where the actual distance between the two vehicles relative to the own vehicle is greater than the desired distance, and the second control algorithm used to control the vehicle to decelerate at the second acceleration in the second operating condition; the speed of the preceding vehicle is greater than that of the own vehicle The third operating condition where the speed and the actual distance between the preceding vehicle and the host vehicle is greater than the expected distance, and the third control algorithm for controlling the host vehicle to accelerate at the third acceleration in the third operating condition; The fourth operating condition where the speed is greater than the speed of the own vehicle and the actual distance between the preced
  • the first control algorithm further includes calculating the first acceleration a1 using the following formula:
  • AEBDis_m (VehSpd_kph-FroVehSpd_kph)*TTC/3.6
  • VehSpd_kph is the speed of the vehicle
  • FroVehSpd_kph is the speed of the preceding vehicle
  • ExpDis_m is the expected distance
  • RelaDis_m is the actual distance between the two vehicles
  • SfDis_m is the safety distance
  • AEBDis_m is the braking distance
  • TTC is the collision time
  • C is a constant; wherein, the safety distance refers to the minimum distance required to maintain between the two vehicles when the speed of the vehicle and the preceding vehicle are the same; wherein, the braking distance refers to the automatic driving vehicle from following mode The distance threshold between the two workshops when switching to the emergency braking AEB mode.
  • the second control algorithm further includes calculating the second acceleration a2 using the following formula:
  • VehSpd_kph is the speed of the own vehicle
  • FroVehSpd_kph is the speed of the preceding vehicle
  • ExpDis_m is the expected distance
  • RelaDis_m is the actual distance between the two vehicles
  • K 1 is a constant used to compensate for the delay effect of the control algorithm.
  • the third control algorithm further includes calculating the third acceleration a3 using the following formula:
  • C31-C34 indicate different acceleration states
  • k31-k34 indicate acceleration values corresponding to different acceleration states
  • C41-C44 represent different deceleration states
  • k41-k44 represent the acceleration values corresponding to different deceleration states.
  • the fifth control algorithm further includes calculating the fifth acceleration a5 by using the following formula:
  • VehSpd_kph is the speed of the own vehicle
  • FroVehSpd_kph is the speed of the preceding vehicle
  • k p is the proportional coefficient
  • the calculating the acceleration of the own vehicle includes: calculating the acceleration a of the own vehicle using the following formula,
  • TopSpd_kph represents the maximum vehicle speed
  • VehSpd_kph represents the current vehicle speed
  • Kp is a proportional parameter of P control.
  • proportional parameter Kp is determined by the following formula:
  • K 0 is the first parameter of the optimal ride experience corresponding to the vehicle acceleration condition determined in the actual vehicle test
  • K 1 is the second parameter of the optimal ride experience corresponding to the vehicle deceleration condition determined during the actual vehicle test parameter.
  • the longitudinal control method of the autonomous vehicle further includes: when the autonomous vehicle is in the AEB mode, controlling the autonomous vehicle to brake at a braking deceleration of 0.7g-0.9g, wherein g is the acceleration due to gravity.
  • the longitudinal control method of the self-driving vehicle of the present invention has the following advantages: the longitudinal control method of the present invention can match various following conditions according to the speed of the preceding vehicle, the speed of the vehicle and the actual distance between the two vehicles According to the following control algorithm, the following control algorithm corresponding to the operating condition is executed when the operating condition is satisfied, so that the vehicle can follow better; in addition, the longitudinal control method of the autonomous vehicle according to the present invention optimizes cruise control. Therefore, the present invention improves the efficiency, safety and comfort of the entire longitudinal control algorithm.
  • Another object of the present invention is to provide a machine-readable storage medium and a processor to at least partially solve the above technical problems.
  • a machine-readable storage medium has instructions stored on the machine-readable storage medium, and the instructions are used to make a machine execute the aforementioned longitudinal control method for an autonomous vehicle.
  • a processor for running a program which is used to execute when the program is running: the longitudinal control method of an automatic driving vehicle as described above.
  • Another object of the present invention is to provide a longitudinal control system for an autonomous vehicle to at least partially solve the above technical problems.
  • a longitudinal control system for an automatic driving vehicle includes: a positioning unit for acquiring current position information of the automatic driving vehicle; a map unit, signally connected to the positioning unit, for outputting all information according to the current position information Map information around the autonomous vehicle; a detection unit for detecting obstacle information around the autonomous vehicle; the above-mentioned machine-readable storage medium; and a processor, which is signally connected to the map unit and the detection unit , Used to obtain forward target information according to the map information and the obstacle information, and execute the instructions stored in the machine-readable storage medium in combination with the forward target information.
  • a longitudinal control system of an automatic driving vehicle includes: a positioning unit for acquiring current position information of the automatic driving vehicle; a map unit for signal connection with the positioning unit for Output map information around the autonomous vehicle according to the current location information; a detection unit for detecting obstacle information around the autonomous vehicle; and the above-mentioned processor, interacting with the map unit and the detection
  • the unit signal connection is used to obtain front target information according to the map information and the obstacle information, and to perform longitudinal control of the vehicle in combination with the front target information.
  • the machine-readable storage medium, the processor, and the longitudinal control system have the same advantages as the foregoing longitudinal control method over the prior art, and will not be repeated here.
  • FIG. 1 is a schematic flowchart of a longitudinal control method for an autonomous vehicle according to an embodiment of the present invention
  • Figure 2 is a schematic diagram of the flow of follow-up control in an embodiment of the present invention
  • Figure 3 is a schematic diagram of five working conditions included in the following mode in an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of the flow of cruise control in an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of switching in a portrait mode according to an embodiment of the present invention.
  • Figure 6 is a schematic structural diagram of a longitudinal control system for an autonomous vehicle according to an embodiment of the present invention.
  • Fig. 7 is a communication schematic diagram of an example longitudinal control system of an automatic driving vehicle according to an embodiment of the present invention.
  • the cruise mode the follow mode
  • the AEB mode the three modes involved in the longitudinal control of the automatic driving vehicle, namely, the cruise mode, the follow mode, and the AEB mode.
  • it may also include a start-stop mode, which means that the autonomous vehicle can accurately control the vehicle to park to a preset location (such as a toll station, service area, etc.), but the start-stop mode can be implemented in the follow mode.
  • the start-stop mode can be implemented in the follow mode.
  • Function it can be subordinate to the follow mode, which is not described separately in the embodiment of the present invention.
  • Cruise mode refers to when the autonomous vehicle (hereinafter also referred to as the own vehicle) is in a cruising state with no preceding vehicle within the operating distance (ActDis_m), the maximum vehicle speed that the autonomous vehicle can travel is adjusted. When the vehicle speed is lower than the maximum vehicle speed, The autonomous vehicle accelerates, and vice versa.
  • Follow mode means that when the autonomous vehicle is in the following state where the vehicle ahead is within the operating distance of its own lane, and the vehicle follows the movement of the vehicle before it is not changing lanes, the speed of the vehicle is adjusted to ensure safe driving. Keep a certain safe distance and relative speed between it and the preceding vehicle, and maintain a stable state of following the preceding vehicle.
  • the AEB mode means that when the AEB mode signal is issued by the longitudinal decision, the self-driving vehicle brakes at a great deceleration.
  • Fig. 1 is a schematic flowchart of a longitudinal control method of an autonomous vehicle according to an embodiment of the present invention. As shown in Figure 1, the vertical control method may include the following steps:
  • Step S110 Detect the longitudinal mode that the autonomous vehicle is currently in.
  • the longitudinal mode includes the above cruise mode, follow mode and AEB mode.
  • Step S120 when the autonomous vehicle is in the following mode, perform following control.
  • FIG. 2 is a schematic flow chart of following control in an embodiment of the present invention. As shown in Figure 2, following control can include the following steps:
  • Step S121 Detect the current operating condition of the autonomous vehicle in the follow mode.
  • Step S122 according to the pre-configured correspondence between different operating conditions and different control algorithms of the autonomous vehicle in the following mode, matching the control algorithm corresponding to the current operating condition, wherein the control algorithm It is used to control the speed change of the autonomous vehicle under corresponding working conditions.
  • Step S123 Control the autonomous vehicle to follow the movement according to the matched control algorithm.
  • the vehicle is divided into five operating conditions according to the operating conditions of the vehicle in the following mode, and different operating conditions are configured to have different control algorithms.
  • Figure 3 is a schematic diagram of five working conditions included in the following mode in the embodiment of the present invention, where ⁇ V represents the speed difference of the vehicle relative to the preceding vehicle, and ⁇ D represents the actual distance between the vehicle and the preceding vehicle relative to the expected The distance difference (the distance difference shows the relative distance between the two workshops), where the expected distance is the driving distance that the host vehicle expects to maintain with the preceding vehicle during the course of following the preceding vehicle.
  • the vehicle in front is slow and relatively close.
  • the slow vehicle in front means that the speed of the vehicle in front is less than the speed of the vehicle in front
  • the close distance means that the actual distance between the vehicle and the vehicle in front is less than the expected distance.
  • the vehicle ahead is fast and relatively far away.
  • the fast vehicle ahead means that the speed of the vehicle ahead is greater than the speed of the vehicle.
  • the correspondence between different operating conditions and different control algorithms of the autonomous vehicle in the following mode includes any one or more of the following.
  • the first control algorithm is used to control the vehicle to decelerate at the first acceleration in the first working condition, that is, when the vehicle is in the first working condition, the vehicle should be adjusted to decelerate.
  • acceleration is not limited to indicating a vehicle acceleration scene, but can also indicate a vehicle deceleration scene, and both "decelerating by acceleration” and “deceleration” can indicate a vehicle deceleration scene.
  • the specific process of calculating the first acceleration is given.
  • the safety distance SfDis_m refers to the current When the speed of the vehicle and the preceding vehicle are the same, the minimum distance between the two vehicles is required to be maintained.
  • the braking distance AEBDis_m refers to the distance threshold between the two vehicles when the autonomous vehicle switches from the following mode to the AEB mode.
  • the braking distance AEBDis_m needs to be calculated by calculating TTC (Time to Collision).
  • TTC Time to Collision
  • VehSpd_kph is the speed of the vehicle
  • FroVehSpd_kph is the speed of the preceding vehicle
  • Relax_m is the actual distance between the two vehicles.
  • braking distance AEBDis_m is calculated using the following formula:
  • AEBDis_m (VehSpd_kph-FroVehSpd_kph)*TTC/3.6 (2)
  • the embodiment of the present invention uses the following equation to calculate the first acceleration a1:
  • ExpDis_m is the expected distance
  • the first acceleration a1 can be calculated in three cases, and the range of the first acceleration a1 can be determined as a1 ⁇ [-8,1].
  • the range of the first acceleration a1 can be determined as a1 ⁇ [-8,1].
  • the second control algorithm is used to control the vehicle to decelerate at the second acceleration in the second operating condition, that is, when the vehicle is in the second operating condition, the vehicle should also be adjusted to decelerate.
  • the second acceleration a2 is calculated using the following formula:
  • K 1 is a constant used to compensate the delay effect of the control algorithm. Specifically, when the longitudinal control (Vehicle Longitudinal Control, VLC) system of an autonomous vehicle performs longitudinal control of the vehicle, there will be a delayed response, and K 1 is used to compensate for the delayed response of the VLC system.
  • VLC Vehicle Longitudinal Control
  • the third control algorithm is used to control the vehicle to accelerate at the third acceleration under the third operating condition, that is, when the vehicle is in the third operating condition, the vehicle should be adjusted to accelerate.
  • the third control algorithm further includes calculating the third acceleration a3 using the following formula:
  • C31-C34 represent different acceleration states
  • k31-k34 represent the acceleration values corresponding to different acceleration states.
  • the acceleration state of the third working condition can be divided into four sub-states C31-C34, and different sub-states correspond to corresponding accelerations, namely k31-k34.
  • the range of the third acceleration a3 is a3 ⁇ [0, 0.8].
  • the fourth control algorithm is used to control the vehicle to decelerate at the fourth acceleration in the fourth operating condition, that is, when the vehicle is in the fourth operating condition, the vehicle should be adjusted to decelerate.
  • the fourth control algorithm further includes calculating the fourth acceleration a4 using the following formula:
  • C41-C44 represent different deceleration states
  • k41-k44 represent the acceleration values corresponding to different deceleration states
  • the specific process of calculating the fourth acceleration is given, that is, the fourth acceleration a4 is calculated using the following formula:
  • the range of the fourth acceleration a4 is a4 ⁇ [-0.8, 1].
  • the speed difference between the speed of the preceding vehicle and the speed of the own vehicle is within the set speed threshold, and the actual distance between the preceding vehicle and the own vehicle is also within the set distance threshold. Therefore, the vehicle follows the vehicle ahead stably at approximately uniform speed.
  • the fifth control algorithm is used to control the vehicle to follow the preceding vehicle stably under the fifth operating condition.
  • the fifth algorithm may consider controlling the vehicle to follow the preceding vehicle stably with the fifth acceleration a5, and the following formula may be used to calculate the fifth algorithm: Acceleration a5:
  • VehSpd_kph is the speed of the own vehicle
  • FroVehSpd_kph is the speed of the preceding vehicle
  • k p is the proportional coefficient
  • the control algorithm under the above five working conditions can be matched according to the speed of the preceding vehicle, the speed of the vehicle and the actual distance between the two vehicles, and the control algorithm corresponding to the working condition is executed for which working condition is satisfied.
  • the control effect can improve the efficiency, safety and comfort of the control algorithm.
  • Step S130 when the autonomous vehicle is in the cruise mode, perform cruise control.
  • FIG. 4 is a schematic flow chart of cruise control in an embodiment of the present invention. As shown in Figure 4, cruise control can include the following steps:
  • Step S131 Acquire the current vehicle speed when the vehicle is in the cruise mode, and calculate the relative speed difference between the current vehicle speed and the highest vehicle speed at which the vehicle can travel.
  • the current vehicle speed can be obtained from the vehicle's own vehicle sensor (such as an inertial navigation device), or can be obtained through the vehicle's ECU (Electronic Control Unit, electronic control unit) in the CAN bus.
  • vehicle's own vehicle sensor such as an inertial navigation device
  • ECU Electronic Control Unit, electronic control unit
  • the maximum speed that the own vehicle can travel comes from the maximum speed management module in the ADS of the autonomous vehicle.
  • the maximum speed management unit belongs to the decision-making system of autonomous vehicles. It is used to obtain the maximum travel speed that the vehicle can travel corresponding to driving scene information, driving environment information, and road condition information to form a set of maximum speeds, and make decisions based on actual conditions.
  • the corresponding reasonable maximum vehicle speed is used as the maximum vehicle speed at which the vehicle can travel.
  • the following specifically introduces the functions of the maximum speed management unit for driving scenes, driving environment and road conditions.
  • the maximum vehicle speed is affected by the following conditions: 1ADS presets the maximum vehicle speed V1; 2The driving area in front of the automatic driving vehicle, different maximum speed V2 is set for different widths; 3The automatic driving vehicle is currently driving in adjacent lanes ( Pedestrians in the setting range of non-emergency lanes, vehicles slow down, set the maximum speed V3; 4The distance between the autonomous vehicle and the exit of the expressway (ramp exit distance, toll gate distance, etc.) is set according to different distances.
  • Maximum speed limit value V4 5When the automatic driving vehicle is at a set distance from the converging point, it will start to decelerate to the maximum speed V5; 6Autonomous driving vehicle under certain working conditions (such as: parallel flow, diversion, high-speed departure, fork junction Etc.), it is necessary to forcibly cut into adjacent lanes, and set the maximum speed of the autonomous vehicle V6 according to the vehicle speed in the left and right areas; 7 During the lane change process of the autonomous vehicle, set the maximum speed V7 according to the target directly in front of the autonomous vehicle; 8 ) There are vehicles driving side by side in autonomous vehicles, and the maximum speed is V8 when the autonomous vehicles pass adjacent lanes.
  • the maximum speed is affected by the following conditions: 1Different roads and road sections will have different road speed limits. According to high-precision maps (HDM) and visual sensors, the maximum speed limit V9 is output; 2Automatic driving vehicles Driving environment factors (degree of light and darkness, fog, rain, snow, hail, etc.), according to different environmental conditions, limit the maximum speed of autonomous vehicles V10.
  • HDM high-precision maps
  • driving vehicles Driving environment factors (degree of light and darkness, fog, rain, snow, hail, etc.), according to different environmental conditions, limit the maximum speed of autonomous vehicles V10.
  • the maximum speed is affected by the following conditions: 1The curvature of the road, according to different road completeness, limits the maximum speed of the vehicle V11; 2The road surface roughness, according to the dynamic information of the vehicle (such as: vertical acceleration, lateral force, Gradient, etc.) limit the maximum vehicle speed V12; 3Road adhesion coefficient, according to the self-driving vehicle's own sensor to detect the current road surface adhesion coefficient, limit the maximum vehicle speed V13.
  • the maximum speed set ⁇ V1,...,V13 ⁇ is obtained, and the appropriate speed V0 is selected according to the actual situation as the maximum vehicle speed V allowed when the autonomous vehicle is driving on the current road.
  • V0 is caused by environmental factors (rain, snow, fog, etc.) or road adhesion coefficient
  • the maximum vehicle speed that the vehicle can travel determined by the embodiment of the present invention has stronger practicability, covers more scenes, is more in line with people’s driving habits, and avoids automatic driving vehicles from driving at too high speeds. In the event of violations or collisions, vehicle skidding and other accidents, the driving safety of autonomous vehicles and the safety of drivers and riding comfort are guaranteed.
  • step S132 the relative speed difference is corrected so that the variation range of the relative speed difference in the control period is within a preset range.
  • the relative speed difference is corrected by configuring a ratio limiting module.
  • the rate limiter module is, for example, the rate limiter module in simulink.
  • the purpose of configuring the ratio limiter module is to correct the change in the maximum vehicle speed that the vehicle can travel, resulting in a relatively large change in the relative speed difference within one operating cycle, which affects the comfort of the vehicle's cruise control. The introduction of this ratio limiting module will prevent this from happening. After simulation and real-vehicle tests, the ratio limiting module can greatly improve the cruise control effect.
  • the parameter settings in the ratio limiter module are also calibrated after actual vehicle testing.
  • Step S133 Calculate the acceleration of the vehicle based on the corrected relative speed difference.
  • the vehicle acceleration a is calculated using the following formula,
  • TopSpd_kph represents the maximum vehicle speed
  • VehSpd_kph represents the current vehicle speed
  • Kp is a proportional parameter of P control.
  • P control refers to P control in classical PID (Proportion Integration Differentiation, proportional-integral-derivative) control.
  • Kp is determined by the following formula:
  • K 0 is the first parameter of the optimal ride experience corresponding to the vehicle acceleration condition determined in the actual vehicle test
  • K 1 is the second parameter of the optimal ride experience corresponding to the vehicle deceleration condition determined during the actual vehicle test Parameters
  • the K 0 and K 1 are the calibration values determined through the actual vehicle test.
  • Step S134 Adjust the vehicle speed of the vehicle in the cruise mode based on the acceleration of the vehicle.
  • step S134 controls the acceleration and deceleration of the vehicle to adjust the vehicle speed of the vehicle in the cruise mode.
  • the acceleration of the own vehicle calculated in step S133 may still be unsatisfactory, for example, it is larger than the acceleration emitted by the actual driver in a certain speed range under the same conditions.
  • this step S134 is configured to limit the acceleration of the host vehicle, and then adjust the speed of the host vehicle in the cruise mode based on the acceleration of the host vehicle after the limit correction. According to this, the acceleration value used for vehicle speed control is more suitable through acceleration limiting.
  • the upper limit value Up of the acceleration limit is determined by the look-up table method, and the maximum acceleration performed by the vehicle is limited according to the vehicle speed, that is, when the acceleration a issued by the own vehicle is greater than the upper limit value Up, according to the upper limit
  • b1 to b6 are constants that increase and less than 1, for example, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, and a 1 to a 8 are successively Reduced setting value.
  • the embodiment of the present invention optimizes the comfort of control based on the realization of cruise control.
  • the test data is analyzed through real vehicle and simulation tests, which has a better control effect and improves the efficiency of the cruise control algorithm and the entire longitudinal control algorithm. Sex, safety and comfort.
  • the longitudinal control method further includes: executing the AEB control method when the autonomous vehicle is in the AEB mode.
  • the AEB control method includes: when the longitudinal decision signal is issued in the AEB mode, the autonomous vehicle brakes at a maximum deceleration, regardless of the comfort requirements of the vehicle braking process, the braking deceleration is requested to be 0.7 Between -0.9g (where g is the acceleration due to gravity), for example, the deceleration is -8m/s 2 .
  • Fig. 5 is a schematic diagram of the longitudinal mode switching of an embodiment of the present invention.
  • the program before the intelligent driving system of an autonomous vehicle is turned on, the program is in the default mode (that is, the standby state, the program does not control the vehicle). Enter the corresponding operating modes from the default state according to environmental information, namely cruise mode, AEB mode and follow mode. After driving in the vehicle, usually switch from cruise mode to AEB mode or follow mode, follow mode, cruise mode and AEB mode Can switch between each other, cruise mode, AEB mode and follow mode can also switch between default modes.
  • the longitudinal control method of an autonomous vehicle executes the corresponding control algorithm according to the instructions issued by the longitudinal decision, thereby controlling the acceleration and deceleration of the vehicle, and then adjusting the speed of the vehicle. And AEB mode control.
  • the machine-readable storage medium includes, but is not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), Read memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (Flash Memory) or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disc (DVD) ) Or other optical storage, magnetic cassette tape, magnetic tape magnetic disk storage or other magnetic storage devices and other media that can store program codes.
  • PRAM phase change memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • RAM random access memory
  • ROM Read memory
  • EEPROM electrically erasable programmable read-only memory
  • flash Memory Flash Memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disc
  • Another embodiment of the present invention also provides a processor for running a program, which is used to execute when the program is run: the longitudinal control method of an autonomous vehicle as in the foregoing embodiment.
  • Fig. 6 is a schematic structural diagram of a longitudinal follow-up control system of an automatic driving vehicle according to an embodiment of the present invention.
  • the longitudinal control system also includes: a positioning unit 610 for acquiring the current position information of the autonomous vehicle; a map unit 620, which is signally connected to the positioning unit 610, and is used for The location information is used to output map information around the autonomous vehicle; the detection unit 630 is used to detect obstacle information around the autonomous vehicle; and the aforementioned readable storage medium; and the processor 640.
  • the processor 640 is in signal connection with the map unit 620 and the detection unit 630, and is used to obtain the map information and the obstacle information from the collection device, and according to the map information and the obstacle information
  • the front target information is acquired, and the instructions stored in the machine-readable storage medium are executed in combination with the front target information for causing the machine to execute the foregoing longitudinal control method of the autonomous vehicle.
  • the positioning unit 610 is a self-positioning device, preferably a high-precision GPS positioning device, which is used to provide the current position information of the vehicle (including the current longitude and latitude information of the vehicle, heading angle information, etc.), and the lateral positioning deviation is within 10cm , The longitudinal positioning deviation is within 30cm.
  • the map unit 620 for example, is used to store and output high-precision map data information, and preferentially use map units with a storage space greater than 50G and a processing memory greater than 1G, and the map unit 620 outputs the self-driving vehicle in real time according to the current position information of the vehicle provided by the positioning unit 610 Different attribute information of the road provided by the high-precision map within 200m from the front and rear.
  • the detection unit 630 for example, is used to detect and extract obstacles that appear within a 360° range around the autonomous vehicle. All-weather sensor detection equipment is preferred to avoid unstable object detection due to rain, snow, fog, light, etc. The detection unit 630 is not only It is not limited to the current installation location nor the current number.
  • radar sensors lidar or millimeter wave radar equipment, etc.
  • vision sensors can be arranged in front of the vehicle.
  • Two angular radar devices are installed at the angular position to reduce the misdetection and missed detection of objects and targets through equipment redundancy.
  • the processor 640 provides forward target information according to the map information provided by the map unit 620 and the obstacle information provided by the detection unit 630, and then executes the instructions stored in the machine-readable storage medium to correspondingly implement the aforementioned autonomous vehicle Vertical control method.
  • the processor 640 may be a processor for running a program involved in the foregoing embodiment, where the program is used to execute the vehicle abnormal lane changing control method of the foregoing embodiment when the program is run.
  • the longitudinal control system of the autonomous vehicle may not additionally include a machine-readable storage medium.
  • the processor 640 may be an ECU (Electronic Control Unit, electronic control unit) of the vehicle, or an independently configured conventional controller, such as a CPU, a single-chip microcomputer, a DSP (Digital Signal Processor), and a SOC (System On a Chip, system on a chip), etc., and it is understood that these independent controllers can also be integrated into the ECU.
  • the processor 640 is preferably configured with a controller with a faster calculation speed and rich I/O port devices, and requires an input and output port that can communicate with the entire vehicle CAN, an input and output port for switching signals, and a network cable interface.
  • FIG. 7 is a communication schematic diagram of an example longitudinal control system of an automatic driving vehicle according to an embodiment of the present invention.
  • the corresponding longitudinal control system includes a self-positioning device as a positioning unit 610 and a high-precision map unit as a map unit 620, The radar device as the detection unit 630 and the ECU as the processor 640.
  • the self-positioning equipment includes GPS, high-precision wheel speed sensors and gyroscope sensors, etc., used to detect vehicle position information including the current longitude and latitude of the vehicle, heading angle information, etc.
  • the high-precision map unit includes HDmap (high-precision map) storage unit and Data calculation processing unit, used to store and appropriately process vehicle position information, etc.
  • the high-precision map unit can output map information to the ECU through UDP (User Datagram Protocol, User Datagram Protocol) communication.
  • Radar equipment includes transmitting optical system and receiving optical system, etc., the quantity and arrangement of which can be set according to the vehicle type and vehicle needs. Among them, the radar equipment can transmit the detected obstacle information to the ECU through CAN communication.
  • the ECU is configured with a CPU and has readable storage media such as ROM, RAM, Flash Memory, etc., which are stored in the algorithm program related to the above-mentioned vertical control method.

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Abstract

一种自动驾驶车辆的纵向控制方法及***，该方法包括：检测车辆当前所处的纵向模式；在处于跟随模式时，根据预配置的车辆在所述跟随模式下的不同工况与不同控制算法之间的对应关系，匹配与当前工况相对应的控制算法，并根据所匹配的控制算法控制自动驾驶车辆进行跟随运动；在处于巡航模式时，获取本车处于巡航模式时的当前车速，并计算当前车速与本车可行驶的最高车速之间的相对速度差；修正以使得相对速度差在控制周期内的变化幅度处于预设范围，根据修正后的相对速度差，计算本车加速度，并基于该本车加速度调节本车在所述巡航模式下的车速。该自动驾驶车辆的纵向控制方法及***提高了整个纵向控制算法的高效性、安全性和舒适性。

Description

自动驾驶车辆的纵向控制方法*** 技术领域
本发明涉及智能交通领域，特别涉及一种自动驾驶车辆的纵向控制方法及***。
背景技术
自动驾驶车辆是通过智能驾驶***来实现自动化行驶的，包括通过安装在车身周围的各种传感***来感知外部环境信息和车辆本身的信息，然后对输入的信息进行融合、决策(对应融合***和决策***)，按照不同的行驶工况自行规划出一条可行驶的安全路线，并通过控制***实时监测和控制车辆安全行驶，以保证车辆的高度自动化行驶。其中，控制***作为智能驾驶***的核心部分，其性能的好坏直接决定着车辆的安全行驶和自动化程度标准，因此一直以来是各个公司研发和攻克的重点和难点。控制***分为横向控制***和纵向控制***两个部分，横向控制***主要是通过一系列控制算法实现对自动驾驶车辆的实时转向控制，使车辆按照已知规划的行驶路线进行车道保持、自动换道、动态避障、掉头和转弯等，纵向控制***主要是通过对车辆加、减速度的控制，使自动驾驶车辆能够以一定的安全行驶速度进行纵向运动，实现自动起停、跟随和巡航等。通过对横纵向控制的耦合，使整个控制***能够同时对车辆的转向和速度实现自动控制。
其中，纵向运动按照运动状态分为三种：巡航、跟随、AEB(Autonomous Emergency Braking，自动紧急制动)。巡航是指在动作距离(该动作距离记为ActDis_m，是指自动驾驶车辆从巡航切到跟随的最小距离阀值，和本车速度及前车速度有关)以内没有存在前车，本车按照可行驶的最高车速行车。跟随是指本车道内动作距离内存本在前车，本车在不换道时跟随前车运动。AEB是指当本车周围的行车环境发生变化时，导致可能发生追尾或者碰撞的发生，危及驾驶员、乘客及行人的行为，AEB状态会以一个较大的减速度制动，进而避免或者减缓车祸事故的发生。
但是，目前的纵向控制方案未考虑车辆在各状态下会出现的多种不同工况，仅给出了单一的针对跟随或巡航的控制方案，无法取得较好的控制效果。
发明内容
有鉴于此，本发明旨在提出一种自动驾驶车辆的纵向控制方法，以至少部分地解决上述技术问题。
为达到上述目的，本发明的技术方案是这样实现的：
一种自动驾驶车辆的纵向控制方法，包括：
检测所述自动驾驶车辆当前所处的纵向模式，所述纵向模式包括巡航模式、跟随模式和自动紧急制动AEB模式；
在所述自动驾驶车辆处于所述跟随模式时，执行：根据预配置的所述自动驾驶车辆在所述跟随模式下的不同工况与不同控制算法之间的对应关系，匹配与所述当前工况相对应的控制算法，其中所述控制算法用于控制所述自动驾驶车辆在对应工况下的速度变化；以及根据所匹配的控制算法控制所述自动驾驶车辆进行跟随运动；
在所述自动驾驶车辆处于所述巡航模式时，执行：获取本车处于巡航模式时的当前车速，并计算所述当前车速与本车可行驶的最高车速之间的相对速度差；修正所述相对速度差，使得所述相对速度差在控制周期内的变化幅度处于预设范围；根据修正后的所述相对速度差，计算本车加速度；以及基于所述本车加速度调节本车在所述巡航模式下的车速。
进一步的，所述自动驾驶车辆在所述跟随模式下的不同工况与不同控制算法之间的对应关系包括以下任意一者或多者：前车速度小于本车速度且前车相对于本车的两车实际距离小于期望距离的第一工况，以及用于控制本车在所述第一工况下以第一加速度进行减速的第一控制算法；前车速度小于本车速度且前车相对于本车的两车实际距离大于期望距离的第二工况，以及用于控制本车在所述第二工况下以第二加速度进行减速的第二控制算法；前车速度大于本车速度且前车相对于本车的两车实际距离大于期望距离的第三工况，以及用于控制本车在所述第三工况下以第三加速度进行加速的第三控制算法；前车速度大于本车速度且前车相对于本车的两车实际距离小于期望距离的第四工况，以及用于控制本车在所述第四工况下以第四加速度进行减速的第四控制算法；以及前车速度与本车速度之间的速度差在设定的速度阈值范围内且前车相对于本车的两车实际距离在设定的距离阈值范围内的第五工况，以及用于控制本车在所述第五工况下以用于稳定跟随前车行驶的第五控制算法。
进一步的，在所述第一控制算法中，还包括采用以下公式计算所述第一加速度a1：
Figure PCTCN2020079953-appb-000001
AEBDis_m＝(VehSpd_kph-FroVehSpd_kph)*TTC/3.6
Figure PCTCN2020079953-appb-000002
式中，VehSpd_kph为所述本车速度，FroVehSpd_kph为所述前车速度，ExpDis_m为所述期望距离，RelaDis_m为所述两车实际距离，SfDis_m为安全距离，AEBDis_m为制动距离，TTC为碰撞时间，c为常数；其中，所述安全距离是指本车与前车的速度一样时，两车之间要求保持的最小距离；其中，所述制动距离是指所述自动驾驶车辆从跟随模式切换到紧急制动AEB模式时两车间的距离阈值。
进一步的，在所述第二控制算法中，还包括采用以下公式计算所述第二加速度a2：
Figure PCTCN2020079953-appb-000003
式中，VehSpd_kph为所述本车速度，FroVehSpd_kph为所述前车速度，ExpDis_m为所述期望距离，RelaDis_m为所述两车实际距离，K 1为用于补偿控制算法的延迟影响的常数。
进一步的，在所述第三控制算法中，还包括采用以下公式计算所述第三加速度a3：
Figure PCTCN2020079953-appb-000004
式中，C31-C34表示不同的加速状态，k31-k34表示不同加速状态对应的加速度值；和/或在所述第四控制算法中，还包括采用以下公式计算所述第四加速度a4：
Figure PCTCN2020079953-appb-000005
式中，C41-C44表示不同的减速状态，k41-k44表示不同减速状态对应的加速度值。
进一步的，在所述第五控制算法中，还包括采用以下公式计算所述第五加速度a5：
a5＝(FroVehSpd_kph-VehSpd_kph)*k p
其中，VehSpd_kph为所述本车速度，FroVehSpd_kph为所述前车速度，k p为比例系数。
进一步的，所述计算本车加速度包括：采用以下公式计算本车加速度a，
a＝(TopSpd_kph-VehSpd_kph)*Kp
其中，TopSpd_kph表示所述最高车速，VehSpd_kph表示所述当前车速，Kp为P控制的比例参数。
其中，通过下式确定所述比例参数Kp：
Figure PCTCN2020079953-appb-000006
其中，K 0是实车测试中确定的对应于车辆加速工况的最优乘坐体验的第一参数，K 1是实车测试中确定的对应于车辆减速工况的最优乘坐体验的第二参数。
进一步的，所述自动驾驶车辆的纵向控制方法还包括：在所述自动驾驶车辆处于所述AEB模式时，控制所述自动驾驶车辆以0.7g-0.9g的制动减速度进行制动，其中g为重力加速度。
相对于现有技术，本发明所述的自动驾驶车辆的纵向控制方法具有以下优势：本发明所述纵向控制方法根据前车速度、本车速度及两车实际距离可匹配多种跟随工况下的跟随控制算法，满足哪种工况就执行该工况对应的跟随控制算法，使得车辆能够较好地进行跟随；另外，本发明所述的自动驾驶车辆的纵向控制方法优化了巡航控制。因此，本发明提高了整个纵向控制算法的高效性、安全性和舒适性。
本发明的另一目的在于提出一种机器可读存储介质和处理器，以至少部分地解决上述技术问题。
为达到上述目的，本发明的技术方案是这样实现的：
一种机器可读存储介质，该机器可读存储介质上存储有指令，该指令用于使得机器执行上述的自动驾驶车辆的纵向控制方法。
一种处理器，用于运行程序，所述程序被运行时用于执行：如上述的自动驾驶车辆的纵向控制方法。本发明的另一目的在于提出一种自动驾驶车辆的纵向控制***，以至少部分地解决上述技术问题。
为达到上述目的，本发明的技术方案是这样实现的：
一种自动驾驶车辆的纵向控制***，包括：定位单元，用于获取所述自动驾驶车辆的当前位置信息；地图单元，与所述定位单元信号连接，用于根据所述当前位置信息来输出所述自动驾驶车辆周围的地图信息；探测单元，用于探测所述自动驾驶车辆周围的障碍物信息；上述的机器可读存储介质；以及处理器，与所述地图单元及所述探测单元信号连接，用于根据所述地图信息及所述障碍物信息获取前方目标信息，并结合所述前方目标信息执行所述机器可读存储介质中存储的指令。
一种自动驾驶车辆的纵向控制***，所述自动驾驶车辆的纵向控制***包括：定位单元，用于获取所述自动驾驶车辆的当前位置信息；地图单元，与所述定位单元信号连接，用于根据所述当前位置信息来输出所述自动驾驶车辆周围的地图信息；探测单元，用于探测所述自动驾驶车辆周围的障碍物信息；以及上述的处理器，与所述地图单元 及所述探测单元信号连接，用于根据所述地图信息及所述障碍物信息获取前方目标信息，并结合所述前方目标信息对车辆进行纵向控制。
所述机器可读存储介质、所述处理器、所述纵向控制***与上述纵向控制方法相对于现有技术所具有的优势相同，在此不再赘述。
本发明的其它特征和优点将在随后的具体实施方式部分予以详细说明。
附图说明
构成本发明的一部分的附图用来提供对本发明的进一步理解，本发明的示意性实施方式及其说明用于解释本发明，并不构成对本发明的不当限定。在附图中：
图1是本发明实施例的一种自动驾驶车辆的纵向控制方法的流程示意图；
图2是本发明实施例中进行跟随控制的流程示意图
图3是本发明实施例中跟随模式所包括的五种工况的示意图；
图4是本发明实施例中进行巡航控制的流程示意图；
图5是本发明实施例的纵向模式的切换示意图；
图6是本发明实施例的一种自动驾驶车辆的纵向控制***的结构示意图；以及
图7是本发明实施例的自动的驾驶车辆的示例纵向控制***的通讯示意图。
附图标记说明：
610、定位单元；620、地图单元；630、探测单元；640、处理器。
具体实施方式
需要说明的是，在不冲突的情况下，本发明中的实施方式及实施方式中的特征可以相互组合。
下面将参考附图并结合实施方式来详细说明本发明。
在介绍本发明实施例自动驾驶车辆的纵向控制方法之前，先介绍自动驾驶车辆在纵向控制中涉及的三种模式，即巡航模式、跟随模式、AEB模式。在其他实施例中，还可以包括起停模式，其是指自动驾驶车辆能够准确控制车辆停靠至预设地点(如收费站、服务区等等)，但起停模式可在跟随模式下实现其功能，故可从属于跟随模式，本发明实施例不对其进行单独介绍。
1)巡航模式
巡航模式，是指自动驾驶车辆(以下也称为本车)处于动作距离(ActDis_m)以内没有存在前车的巡航状态时，调节自动驾驶车辆可 行驶的最高车速，当车速低于最高车速时，自动驾驶车辆加速行驶，反之减速。
2)跟随模式
跟随模式，是指自动驾驶车辆处于本车道内动作距离内存本在前车、且本车在不换道时跟随前车运动的跟随状态时，调节本车车速，在保证安全行车的前提下，使其与前车之间保持一定的安全距离和相对速度，保持稳定地跟随前车行驶的状态。
3)AEB模式
AEB模式，是指在纵向决策发出AEB模式的信号时，自动驾驶车辆以极大减速度进行制动。
图1是本发明实施例的一种自动驾驶车辆的纵向控制方法的流程示意图。如图1所示，该纵向控制方法可以包括以下步骤：
步骤S110，检测所述自动驾驶车辆当前所处的纵向模式。
其中，所述纵向模式包括如上的巡航模式、跟随模式和AEB模式。
步骤S120，在所述自动驾驶车辆处于所述跟随模式时，执行跟随控制。
其中，图2是本发明实施例中进行跟随控制的流程示意图。如图2所示，跟随控制可包括以下步骤：
步骤S121，检测所述自动驾驶车辆在跟随模式下的当前工况。
步骤S122，根据预配置的所述自动驾驶车辆在所述跟随模式下的不同工况与不同控制算法之间的对应关系，匹配与所述当前工况相对应的控制算法，其中所述控制算法用于控制所述自动驾驶车辆在对应工况下的速度变化。
步骤S123，根据所匹配的控制算法控制所述自动驾驶车辆进行跟随运动。
本发明实施例中，根据车辆在跟随模式下的运行情况，将其分为五种工况，且配置不同工况具有不同的控制算法。
图3是本发明实施例中跟随模式所包括的五种工况的示意图，其中△V表示本车相对于前车的速度差，△D表示本车与前车的两车实际距离相对于期望距离的距离差(该距离差示出两车间的相对距离)，其中，期望距离是本车在跟随前车行车过程中期望与前车之间保持的行车距离。
参考图3，对应的五种工况可简单描述如下：
1)第一工况，前车慢且相对距离近，其中前车慢是指前车速度小于本车速度，相对距离近是指本车与前车之间的两车实际距离小于期望距离。
2)第二工况，前车慢且相对距离远，其中相对距离远是指本车 与前车之间的两车实际距离大于期望距离。
3)第三工况，前车快且相对距离远，其中前车快是指前车速度大于本车速度。
4)第四工况，前车快且相对距离近。
5)第五工况，稳定跟车。
在优选的实施例中，对应上述五种工况，所述自动驾驶车辆在所述跟随模式下的不同工况与不同控制算法之间的对应关系包括以下的任意一者或多者。
一、第一工况及对应的第一控制算法
其中，第一控制算法用于控制本车在所述第一工况下以第一加速度进行减速，即是在本车处于第一工况时，应调整车辆进行减速。本领域技术人员可理解的，“加速度”不限于示意车辆加速场景，也可示意车辆减速场景，“以加速度进行减速”与“减速度”均可示意车辆减速场景。
在更为优选的实施例中，给出了计算第一加速度的具体过程，在介绍该过程之前，先引入安全距离(SfDis_m)和制动距离(AEBDis_m)的概念，该安全距离SfDis_m是指本车与前车的速度一样时，两车之间要求保持的最小距离，该制动距离AEBDis_m是指所述自动驾驶车辆从跟随模式切换到AEB模式时两车间的距离阈值。
本发明实施例中，需通过计算TTC(Time to collision，碰撞时间)来计算制动距离AEBDis_m。TTC的计算如下：
Figure PCTCN2020079953-appb-000007
式中，VehSpd_kph为本车速度，FroVehSpd_kph为前车速度，RelaDis_m为两车实际距离。
进一步地，采用下式计算制动距离AEBDis_m：
AEBDis_m＝(VehSpd_kph-FroVehSpd_kph)*TTC/3.6  (2)
进一步地，采用下式计算安全距离SfDis_m：
SfDis m＝0.8509*FroVehSpd_kph+c  (3)
式中，c为标定量，例如c＝8。
基于上述式(1)-(3)，本发明实施例采用下式计算第一加速度a1：
Figure PCTCN2020079953-appb-000008
Figure PCTCN2020079953-appb-000009
式中，ExpDis_m为期望距离。
根据式(4)，可分为三种情况计算第一加速度a1，据此可确定第一加速度a1的范围为a1∈[-8，1]，此公式(4)中：
当两车之间的距离为SfDis_m<RelaDis_m<ExpDis_m时，此时
Figure PCTCN2020079953-appb-000010
当两车之间的距离为AEBDis_m<RelaDis_m<SfDis_m时，此时
Figure PCTCN2020079953-appb-000011
当两车之间的距离为RelaDis_m<AEBDis_m时从跟随模式切换到AEB模式，执行AEB的控制，加速度为-8m/s 2。
需说明是，本发明实施例的计算公式中，加速度值带有负号“-”时，表示“以加速度进行减速”或“减速度”。
二、第二工况及对应的第二控制算法
其中，第二控制算法用于控制本车在所述第二工况下以第二加速度进行减速，即是在本车处于第二工况时，同样应调整车辆进行减速。
在更为优选的实施例中，给出了计算第二加速度的具体过程，即采用以下公式计算所述第二加速度a2：
Figure PCTCN2020079953-appb-000012
式中，K 1为用于补偿控制算法的延迟影响的常数。具体地，自动驾驶车辆的纵向控制(Vehicle Longitudinal Control，VLC)***对车辆进行纵向控制时，会有延迟响应，K 1则用于补偿VLC***的延迟响应。
三、第三工况及对应的第三控制算法
其中，第三控制算法用于控制本车在所述第三工况下以第三加速度进行加速，即是在本车处于第三工况时，应调整车辆进行加速。
在优选的实施例中，在所述第三控制算法中，还包括采用以下公式计算所述第三加速度a3：
Figure PCTCN2020079953-appb-000013
式中，C31-C34表示不同的加速状态，k31-k34表示不同加速状态对应的加速度值。具体地，根据前车车速、前车距离本车的距离等，可将第三工况的加速状态分为C31-C34四种子状态，不同子状态对应相应的加速度，即k31-k34。
在其他实施例中，还采用以下公式计算所述第三加速度a3：
Figure PCTCN2020079953-appb-000014
在此，K 1为常数，例如K 1＝0.3，此第三工况下，第三加速度a3的范围为a3∈[0，0.8]。
四、第四工况及对应的第四控制算法
其中，第四控制算法用于控制本车在所述第四工况下以第四加速度进行减速，即是在本车处于第四工况时，应调整车辆进行减速。
在优选的实施例中，在所述第四控制算法中，还包括采用以下公式计算所述第四加速度a4：
Figure PCTCN2020079953-appb-000015
式中，C41-C44表示不同的减速状态，k41-k44表示不同减速状态对应的加速度值
在其他实施例中，给出了计算第四加速度的具体过程，即采用以下公式计算所述第四加速度a4：
Figure PCTCN2020079953-appb-000016
在此，K 1为常数，例如K 1＝0.2，此第四工况下，第四加速度a4的范围为a4∈[-0.8，1]。
五、第五工况及第五控制算法
其中，第五工况中前车速度与本车速度之间的速度差在设定的速度阈值范围内，以及前车相对于本车的两车实际距离也在设定的距离阈值范围内，从而本车近似匀速地稳定跟随前车行车。
其中，第五控制算法用于控制本车在所述第五工况下稳定跟随前车行驶。
在其他实施例中，在对本车相对于前车的稳定跟随状态要求不高时，第五算法可考虑控制本车以第五加速度a5稳定跟随前车行驶，并且可采用以下公式计算该第五加速度a5：
a5＝(FroVehSpd_kph-VehSpd_kph)*k p
其中，VehSpd_kph为所述本车速度，FroVehSpd_kph为所述前车速度，k p为比例系数。
如此，在跟随状态下，根据前车速度、本车速度及两车实际距离可匹配以上五种工况下的控制算法，满足哪种工况就执行该工况对应的控制算法，具有较好的控制效果，能提高控制算法的高效性、安全性和舒适性。
步骤S130，在所述自动驾驶车辆处于所述巡航模式时，执行巡 航控制。
其中，图4是本发明实施例中进行巡航控制的流程示意图。如图4所示，巡航控制可包括以下步骤：
步骤S131，获取本车处于巡航模式时的当前车速，并计算所述当前车速与本车可行驶的最高车速之间的相对速度差。
其中，所述当前车速可从本车自身的车辆传感器(如惯性导航设备)获取，也可通过CAN总线中车辆的ECU(Electronic Control Unit，电子控制单元)获取。
其中，本车可行驶的最高车速来源于自动驾驶车辆的ADS中的最高车速管理模块。该最高车速管理单元属于自动驾驶车辆的决策***，其用于获取对应于驾驶场景信息、行驶环境信息以及道路条件信息的车辆可行驶的最高行驶速度以形成最高速度集合，并根据实际情况决策出相应合理的最高车速以作为在此的本车可行驶的最高车速。
下面分别针对驾驶场景、行驶环境以及道路条件来具体介绍最高车速管理单元的功能。
1)针对不同驾驶场景，最高车速受以下条件影响：①ADS预设最高车速V1；②自动驾驶车辆行驶前方可行驶区域，不同宽度设定不同最高车速V2；③自动驾驶车辆当前行驶相邻车道(非应急车道)的设定范围内存在行人，车辆降速，设定最高车速V3；④自动驾驶车辆距离高速公路出口距离(匝道出口距离、收费站距离等)，根据不同的距离设定不同的最高限速值V4；⑤自动驾驶车辆在距离汇入点设定距离时时，开始减速至最高车速V5；⑥自动驾驶车辆在特定工况下(如：并流、分流、驶离高速、岔道口等)，需要强制切入相邻车道，根据左右前方区域车辆速度，设定自动驾驶车辆最高车速V6；⑦自动驾驶车辆在换道过程中，依据自动驾驶车辆正前方目标设定最高车速V7；8)自动驾驶车辆存在并排行驶车辆，自动驾驶车辆超越相邻车道并排行驶车辆时最高车速V8。
2)针对不同行驶环境，最高车速受以下条件影响：①不同行驶道路，路段会有不同的道路限速，根据高精度地图(HDM)、视觉传感器识别输出道路最高限速V9；②自动驾驶车辆行驶环境因素(光线明暗程度、雾、雨雪、冰雹等)，根据不同环境条件限定自动驾驶车辆最高车速V10。
3)针对不同道路条件，最高车速受以下条件影响：①道路曲率，根据不同的道路完全程度，限定车辆最高车速V11；②路面不平度，根据车辆的动态信息(如:垂直加速度、横向力、坡度等)限定车辆最高车速V12；③路面附着系数，根据自动驾驶车辆自身所带传感器检测当前道路路面附着系数，限定车辆最高车速V13。
据此，得到最高速度集合{V1，……，V13}，并根据实际情况选 取合适的速度V0作为自动驾驶车辆在当前道路行驶时允许的最高车速V。另外，若V0是由于环境因素(雨雪雾等)或者路面附着系数引起，若此时车辆周围存在邻车，此时主动将V0降低10％，***最高车速V＝0.9*V0；若上述V0非环境因素(雨雪雾等)、路面附着系数引起，若此时车辆周围存在邻车，此时***最高车速V＝V8，否则V＝V0。
本发明实施例所确定的本车可行驶的最高车速，与常规方法相比具有更强的实用性，涵盖更多的场景，更符合人的驾驶习惯，避免自动驾驶车辆由于行驶速度过高而出现违章或发生碰撞、车辆打滑等事故，保障自动驾驶车辆行车安全及驾驶员安全及乘坐舒适性。
步骤S132，修正所述相对速度差，使得所述相对速度差在控制周期内的变化幅度处于预设范围。
对于该步骤S132，在优选的实施例中，通过配置比率限幅模块来修正所述相对速度差。其中，该比率限幅模块例如是simulink中的rate limiter模块。配置比率限幅模块的目的是修正因本车可行驶的最高车速的变化，导致相对速度差在一个运行周期内有较大变化，影响整车巡航控制的舒适性。而引入这个比率限幅模块后会避免这种情况的发生，经仿真与实车测试验证，该比率限幅模块能使巡航控制效果有很大的改善。另外，此比率限幅模块中的参数设置也是经实车测试标定出来的。
步骤S133，根据修正后的所述相对速度差，计算本车加速度。
在优选的实施例中，采用以下公式计算本车加速度a，
a＝(TopSpd_kph-VehSpd_kph)*Kp
其中，TopSpd_kph表示所述最高车速，VehSpd_kph表示所述当前车速，Kp为P控制的比例参数。其中，P控制是指经典PID(Proportion Integration Differentiation，比例-积分-微分)控制中的P控制。
对于Kp，因巡航控制涉及到加速与减速控制，当本车车速低于最高车速时，车辆加速，反之减速，因加速和减速的执行机构的响应速度及精度不同，因此Kp也不同。优选地，通过下式确定所述比例参数Kp：
Figure PCTCN2020079953-appb-000017
其中，K 0是实车测试中确定的对应于车辆加速工况的最优乘坐体验的第一参数，K 1是实车测试中确定的对应于车辆减速工况的最优乘坐体验的第二参数，该K 0和K 1均是通过实车测试确定的标定值。
步骤S134，基于所述本车加速度调节本车在所述巡航模式下的车速。
通过步骤S131-步骤S133确定合适的加速度后，该步骤S134中 通过控制本车的加减速，进而调节本车在巡航模式下的车速。
在一些情形中，步骤S133计算出的本车加速度可能仍不太理想，例如比实际驾驶员在同样条件下在某个特定速度段下发出的加速度相比偏大。对此，在优选的实施例中，该步骤S134被配置为：对所述本车加速度进行限幅修正，再基于限幅修正后的本车加速度调节本车在所述巡航模式下的车速。据此，通过加速度限幅使得用于车速控制的加速度值更为适宜。
进一步地，在此的加速度限幅的上限值Up采用查表法来确定，根据本车车速限制车辆执行的最大加速度，即当本车发出的加速度a大于上限值Up时，按照上限值Up执行，否则执行本车发出的加速度，即a Taracce＝MIN(Up，a)。更为具体地，上限值Up通过下式确定：
Figure PCTCN2020079953-appb-000018
同理，加速度限幅的下限值采取同样的处理，b1至b6为依次增大且小于1的常数，例如分别为0.35、0.45、0.55、0.65、0.75、0.85，a 1至a 8为依次减小的设定值。
如此，本发明实施例在实现巡航控制的基础上，优化了控制的舒适性，经实车及仿真测试分析测试数据，具有较好的控制效果，提高了巡航控制算法及整个纵向控制算法的高效性、安全性和舒适性。
进一步地，该纵向控制方法还包括：在所述自动驾驶车辆处于所述AEB模式时，执行AEB控制方法。本发明实施例中，AEB控制方法包括：当纵向决策发出AEB模式的信号时，自动驾驶车辆以极大减速度进行制动，不考虑车辆制动过程的舒适性要求，请求制动减速度0.7-0.9g之间(其中g为重力加速度)，例如减速度为-8m/s 2。
图5是本发明实施例的纵向模式的切换示意图，如图5所示，自动驾驶车辆的智能驾驶***在开启前，程序处于默认模式(即待机状态，程序不控制车辆)，开启后，程序从默认状态根据环境信息等进入相应的工作模式，即巡航模式、AEB模式和跟随模式，在车辆行驶过种，一般是从巡航模式切换至AEB模式或跟随模式，跟随模式与巡航模式及AEB模式间可互相切换，巡航模式、AEB模式和跟随模式均可也默认模式相互切换。
据此，本发明实施例的自动驾驶车辆的纵向控制方法按照纵向决策发出的指令执行相就的控制算法，从而控制车辆的加速、减速，进而调节车辆的车速，实现了针对巡航模式、跟随模式和AEB模式的控制。
本发明另一实施例还提供一种机器可读存储介质，该机器可读存储介质上存储有指令，该指令用于使得机器执行上述的自动驾驶车辆的纵向控制方法。其中，所述机器可读存储介质包括但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体(Flash Memory)或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备等各种可以存储程序代码的介质。
本发明另一实施例还提供一种处理器，用于运行程序，所述程序被运行时用于执行：如上述实施例的自动驾驶车辆的纵向控制方法。
图6是本发明实施例的一种自动驾驶车辆的纵向随控制***的结构示意图。如图6所示，所述纵向控制***同样包括：定位单元610，用于获取所述自动驾驶车辆的当前位置信息；地图单元620，与所述定位单元610信号连接，用于根据所述当前位置信息来输出所述自动驾驶车辆周围的地图信息；探测单元630，用于探测所述自动驾驶车辆周围的障碍物信息；以及上述的可读存储介质；以及处理器640。该处理器640与所述地图单元620及所述探测单元630信号连接，用于从所述采集装置获取所述地图信息及所述障碍物信息，并根据所述地图信息及所述障碍物信息获取前方目标信息，并结合所述前方目标信息执行所述机器可读存储介质中存储的用于使得机器执行上述的自动驾驶车辆的纵向控制方法的指令。
对于上述纵向控制***，定位单元610为自定位设备，优选为高精度GPS定位设备，其用于提供车辆的当前位置信息(包括车辆的当前经纬度信息、航向角信息等)，横向定位偏差10cm以内，纵向定位偏差30cm以内。地图单元620例如用于存储、输出高精度地图数据信息，优先使用存储空间大于50G、处理内存大于1G的地图单元，且地图单元620根据定位单元610提供的车辆的当前位置信息实时输出自动驾驶车辆前后方200m范围内高精度地图所提供道路的不同属性信息。探测单元630例如用于探测提取自动驾驶车辆周围360°范围内出现的障碍物，优先选用全天候传感器探测设备以避免因雨、雪、雾、光照等引起物体目标探测不稳定，探测单元630不仅仅不局限于当前安装位置也不局限于当前数量，为提高物体探测准确性，可在车辆前方布置雷达传感器(激光雷达或毫米波雷达设备等)、视觉 传感器，同理可以在车辆前方两个左右角位置安装两个角雷达设备来通过设备冗余降低物体目标误检、漏检等状况。处理器640，根据地图单元620提供的地图信息以及探测单元630提供的障碍物信息来提供前方目标信息，进而执行所述机器可读存储介质中存储的指令，以对应实现上述的自动驾驶车辆的纵向控制方法。
在优选的实施例中，所述处理器640可以上述实施例中涉及的用于运行程序的处理器，其中所述程序被运行时用于执行上述实施例的车辆异常换道控制方法。此情况下，所述自动驾驶车辆的纵向控制***可以不另外包括机器可读存储介质。
其中，处理器640可以是车辆的ECU(Electronic Control Unit，电子控制单元)，也可以是独立配置的常规控制器，如CPU、单片机、DSP(Digital Signal Processor，数字信号处理器)、SOC(System On a Chip，片上***)等，且可以理解，这些独立控制器也可以集成至ECU中。处理器640优选采用运算速度较快且有着丰富的I/O口设备的控制器来进行配置，要求具有能与整车CAN通信的输入输出端口、开关信号的输入输出端口、网线接口等。
图7是本发明实施例的自动的驾驶车辆的示例纵向控制***的通讯示意图，该示例中，对应的纵向控制***包括作为定位单元610的自定位设备、作为地图单元620的高精度地图单元，作为探测单元630的雷达设备以及作为处理器640的ECU。其中，自定位设备包括GPS、高精度轮速传感器以及陀螺仪传感器等，用于探测包括车辆当前经纬度、航向角信息等的车辆位置信息；高精度地图单元包括HDmap(高精度地图)存储单元和数据运算处理单元，用于存储并适当处理车辆位置信息等以输出车辆前后方200m范围内高精度地图车道线离散点经纬度(经纬度以地心为原点)、离散点航向角(以正北方向为0°顺时针为证)、车道线类型、每一条车道宽度、车道数量、道路边界等地图信息。高精度地图单元可通过UDP(User Datagram Protocol，用户数据报协议)通讯方式将地图信息输出至ECU。雷达设备包括发射光学***及接受光学***等，其数量及布置可根据车型及车辆需要来进行设置。其中，雷达设备可通过CAN通讯方式将探测到的障碍信息传输给ECU。ECU采用CPU配置，且具有ROM、RAM、Flash Memory等可读存储介质，这些可读存储介质存储在关于上述纵向控制方法的算法程序。
需说明的是，本发明实施例涉及的机器可读存储介质、自动驾驶车辆的纵向控制***的更多实施细节及效果可参考上述关于自动驾驶车辆的纵向控制方法的实施例，在此则不再进行赘述。
以上所述仅为本发明的较佳实施方式而已，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等， 均应包含在本发明的保护范围之内。

Claims (12)

  1. 一种自动驾驶车辆的纵向控制方法，其特征在于，所述自动驾驶车辆的纵向控制方法包括：
    检测所述自动驾驶车辆当前所处的纵向模式，所述纵向模式包括巡航模式、跟随模式和自动紧急制动AEB模式；
    在所述自动驾驶车辆处于所述跟随模式时，执行：
    根据预配置的所述自动驾驶车辆在所述跟随模式下的不同工况与不同控制算法之间的对应关系，匹配与所述当前工况相对应的控制算法，其中所述控制算法用于控制所述自动驾驶车辆在对应工况下的速度变化；以及
    根据所匹配的控制算法控制所述自动驾驶车辆进行跟随运动；
    在所述自动驾驶车辆处于所述巡航模式时，执行：
    获取本车处于巡航模式时的当前车速，并计算所述当前车速与本车可行驶的最高车速之间的相对速度差；
    修正所述相对速度差，使得所述相对速度差在控制周期内的变化幅度处于预设范围；
    根据修正后的所述相对速度差，计算本车加速度；以及
    基于所述本车加速度调节本车在所述巡航模式下的车速。
  2. 根据权利要求1所述的自动驾驶车辆的纵向控制方法，其特征在于，所述自动驾驶车辆在所述跟随模式下的不同工况与不同控制算法之间的对应关系包括以下任意一者或多者：
    前车速度小于本车速度且前车相对于本车的两车实际距离小于期望距离的第一工况，以及用于控制本车在所述第一工况下以第一加速度进行减速的第一控制算法；
    前车速度小于本车速度且前车相对于本车的两车实际距离大于期望距离的第二工况，以及用于控制本车在所述第二工况下以第二加速度进行减速的第二控制算法；
    前车速度大于本车速度且前车相对于本车的两车实际距离大于 期望距离的第三工况，以及用于控制本车在所述第三工况下以第三加速度进行加速的第三控制算法；
    前车速度大于本车速度且前车相对于本车的两车实际距离小于期望距离的第四工况，以及用于控制本车在所述第四工况下以第四加速度进行减速的第四控制算法；以及
    前车速度与本车速度之间的速度差在设定的速度阈值范围内且前车相对于本车的两车实际距离在设定的距离阈值范围内的第五工况，以及用于控制本车在所述第五工况下以用于稳定跟随前车行驶的第五控制算法。
  3. 根据权利要求2所述的自动驾驶车辆的纵向控制方法，其特征在于，在所述第一控制算法中，还包括采用以下公式计算所述第一加速度a1：
    Figure PCTCN2020079953-appb-100001
    AEBDis_m＝(VehSpd_kph-FroVehSpd_kph)*TTC/3.6
    Figure PCTCN2020079953-appb-100002
    SfDis m＝0.8509*FroVehSpd_kph+c
    式中，VehSpd_kph为所述本车速度，FroVehSpd_kph为所述前车速度，ExpDis_m为所述期望距离，RelaDis_m为所述两车实际距离，SfDis_m为安全距离，AEBDis_m为制动距离，TTC为碰撞时间，c为常数；
    其中，所述安全距离是指本车与前车的速度一样时，两车之间要求保持的最小距离；
    其中，所述制动距离是指所述自动驾驶车辆从跟随模式切换到紧急制动AEB模式时两车间的距离阈值。
  4. 根据权利要求2所述的自动驾驶车辆的纵向控制方法，其特征在于，在所述第二控制算法中，还包括采用以下公式计算所述第二加速度a2：
    Figure PCTCN2020079953-appb-100003
    式中，VehSpd_kph为所述本车速度，FroVehSpd_kph为所述前车速度，ExpDis_m为所述期望距离，RelaDis_m为所述两车实际距离，K 1为用于补偿控制算法的延迟影响的常数。
  5. 根据权利要求2所述的自动驾驶车辆的纵向控制方法，其特征在于，在所述第三控制算法中，还包括采用以下公式计算所述第三加速度a3：
    Figure PCTCN2020079953-appb-100004
    式中，C31-C34表示不同的加速状态，k31-k34表示不同加速状态对应的加速度值；和/或
    在所述第四控制算法中，还包括采用以下公式计算所述第四加速度a4：
    Figure PCTCN2020079953-appb-100005
    式中，C41-C44表示不同的减速状态，k41-k44表示不同减速状态对应的加速度值。
  6. 根据权利要求2所述的自动驾驶车辆的纵向控制方法，其特征在于，在所述第五控制算法中，还包括采用以下公式计算所述第五加速度a5：
    a5＝(FroVehSpd_kph-VehSpd_kph)*k p
    其中，VehSpd_kph为所述本车速度，FroVehSpd_kph为所述前车速度，k p为比例系数。
  7. 根据权利要求1所述的自动驾驶车辆的纵向控制方法，其特征在于，所述计算本车加速度包括：采用以下公式计算本车加速度a，
    a＝(TopSpd_kph-VehSpd_kph)*Kp
    其中，TopSpd_kph表示所述最高车速，VehSpd_kph表示所述当前车速，Kp为P控制的比例参数；
    其中，通过下式确定所述比例参数Kp：
    Figure PCTCN2020079953-appb-100006
    其中，K 0是实车测试中确定的对应于车辆加速工况的最优乘坐体验的第一参数，K 1是实车测试中确定的对应于车辆减速工况的最优乘坐体验的第二参数。
  8. 根据权利要求1所述的自动驾驶车辆的纵向控制方法，其特征在于，所述自动驾驶车辆的纵向控制方法还包括：
    在所述自动驾驶车辆处于所述AEB模式时，控制所述自动驾驶车辆以0.7g-0.9g的制动减速度进行制动，其中g为重力加速度。
  9. 一种机器可读存储介质，该机器可读存储介质上存储有指令，该指令用于使得机器执行权利要求1至8中任意一项所述的自动驾驶车辆的纵向控制方法。
  10. 一种处理器，其特征在于，用于运行程序，所述程序被运行时用于执行：如权利要求1至8中任意一项所述的自动驾驶车辆的纵向控制方法。
  11. 一种自动驾驶车辆的纵向控制***，其特征在于，所述自动驾驶车辆的纵向控制***包括：
    定位单元，用于获取所述自动驾驶车辆的当前位置信息；
    地图单元，与所述定位单元信号连接，用于根据所述当前位置信息来输出所述自动驾驶车辆周围的地图信息；
    探测单元，用于探测所述自动驾驶车辆周围的障碍物信息；
    权利要求9中所述的机器可读存储介质；以及
    处理器，与所述地图单元及所述探测单元信号连接，用于根据所述地图信息及所述障碍物信息获取前方目标信息，并结合所述前方目标信息执行所述机器可读存储介质中存储的指令。
  12. 一种自动驾驶车辆的纵向控制***，其特征在于，所述自动驾驶车辆的纵向控制***包括：
    定位单元，用于获取所述自动驾驶车辆的当前位置信息；
    地图单元，与所述定位单元信号连接，用于根据所述当前位置信息来输出所述自动驾驶车辆周围的地图信息；
    探测单元，用于探测所述自动驾驶车辆周围的障碍物信息；以及
    权利要求10中所述的处理器，与所述地图单元及所述探测单元信号连接，用于根据所述地图信息及所述障碍物信息获取前方目标信息，并结合所述前方目标信息对车辆进行纵向控制。
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