WO2020135742A1 - Système de décision horizontale de véhicule à conduite autonome et procédé de prise de décision horizontale - Google Patents

Système de décision horizontale de véhicule à conduite autonome et procédé de prise de décision horizontale Download PDF

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WO2020135742A1
WO2020135742A1 PCT/CN2019/129284 CN2019129284W WO2020135742A1 WO 2020135742 A1 WO2020135742 A1 WO 2020135742A1 CN 2019129284 W CN2019129284 W CN 2019129284W WO 2020135742 A1 WO2020135742 A1 WO 2020135742A1
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lane
target
road
obstacle avoidance
autonomous vehicle
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PCT/CN2019/129284
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English (en)
Chinese (zh)
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张凯
葛建勇
甄龙豹
和林
常仕伟
王天培
刘宏伟
刘洪亮
卜玉帅
高健
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长城汽车股份有限公司
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Publication of WO2020135742A1 publication Critical patent/WO2020135742A1/fr

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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
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0011—Planning or execution of driving tasks involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
    • 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
    • B60W30/10—Path keeping
    • B60W30/12—Lane keeping
    • 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
    • B60W30/18—Propelling the vehicle
    • B60W30/18009—Propelling the vehicle related to particular drive situations
    • B60W30/18163—Lane change; Overtaking manoeuvres
    • 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
    • B60W40/02—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 ambient conditions
    • B60W40/06—Road conditions
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/05—Type of road, e.g. motorways, local streets, paved or unpaved roads
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/10—Number of lanes
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/53—Road markings, e.g. lane marker or crosswalk
    • 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
    • B60W2554/20—Static objects

Definitions

  • the invention relates to the technical field of automatic driving, and in particular to a lateral decision system and a lateral decision determination method of an autonomous driving vehicle.
  • Autonomous vehicle refers to an intelligent vehicle that senses the road environment through an onboard sensor system, automatically plans driving routes, and controls the vehicle to reach a predetermined destination. It relies on the Autonomous Driving System (ADS) to achieve its functions. According to the development and design process of ADS, ADS can be divided into five parts: environment awareness system, data fusion system, decision system, control system and execution system.
  • ADS Autonomous Driving System
  • the environment awareness system is used to extract the current driving environment information of vehicles, pedestrians, roads, traffic signs and other vehicles through the on-board sensing system;
  • the data fusion system is used to filter, correlate, track, filter and other data of different sensors in order to Obtain more accurate information on roads, environmental objects, etc.;
  • the decision system is used to logically determine the vehicle behavior of unmanned vehicles based on the driving status, road, and environmental information of different environmental vehicles output by the data fusion system;
  • the control system uses Based on the information output by the data fusion system and decision system, the current horizontal and vertical control changes of the vehicle are calculated and output in real time;
  • the execution system is used to replace the driver's operation of the steering wheel, acceleration and deceleration pedals of the vehicle according to the steering, acceleration and other control variables output by the control system .
  • the decision-making system judges and outputs the lateral and vertical vehicle behaviors of the autonomous vehicle based on the input environmental object targets, roads and other information.
  • the lateral vehicle behaviors include lane keeping, lane changing, abnormal lane changing, etc.
  • the performance is acceleration, deceleration, etc.
  • lane keeping, lane changing and abnormal lane changing are the main behaviors of vehicles during driving.
  • the correct control of these three behaviors by the control system plays a decisive role in driving safety. Therefore, how the decision-making system can correctly judge the lateral behavior of vehicles such as lane keeping, lane-changing, and abnormal lane-changing is an important factor to be considered when designing a vehicle decision-making system.
  • the present invention aims to propose a lateral decision-making system for autonomous vehicles to achieve correct judgment of lateral behavior of the vehicle.
  • a lateral decision-making system for an autonomous vehicle includes an evaluation unit for evaluating target lanes and lane abnormalities required by the autonomous vehicle for lateral decision-making based on road feature information and pre-selected target lines and environmental object targets And a judging unit for judging and outputting the expected lateral behavior of the self-driving vehicle based on the target lane and lane abnormalities evaluated by the evaluation unit in combination with the road feature information, wherein the expected lateral behavior includes the lane Any of keep, change lane and abnormal change lane.
  • the evaluation unit includes: a target lane management module for selecting a target lane of the self-driving vehicle according to the road feature information, wherein the principle of selecting the target lane includes following the principle of road scenes and following the attributes of lanes The principle of not selecting abnormal lanes, and the principle of selecting adjacent lanes and selecting in turn to the right when the lane is abnormal, wherein the road feature information includes the road type, road feature points and the lane attributes, and the lane attributes include A lane feature point attribute and a lane number attribute; and a lane abnormality management module, used to identify an abnormal lane based on the road characteristic information, and provide an obstacle avoidance strategy for the abnormal lane.
  • a target lane management module for selecting a target lane of the self-driving vehicle according to the road feature information, wherein the principle of selecting the target lane includes following the principle of road scenes and following the attributes of lanes The principle of not selecting abnormal lanes, and the principle of selecting adjacent lanes and selecting in turn to the right when the lane is abnormal, where
  • the target lane management module includes: a main lane target lane selection sub-module for selecting a target lane according to the selection principle when the self-driving vehicle is driving in a normal scene of the main lane, wherein the main lane Conventional scenes include acceleration lanes, normal driving lanes, and deceleration lanes, and are used to select target lanes based on changes in the number of lanes of the road ahead relative to the current road when the autonomous vehicle is driving on the special scene of the main lane.
  • the special scene of the main road includes a main road narrowing, a main road widening, a main road bifurcation and/or a tunnel; and a ramp target lane selection sub-module, which is used when the autonomous vehicle is driving on a ramp scene according to the road ahead
  • the target lane is selected with respect to the change of the number of lane attributes of the current road, where the ramp scene includes a regular ramp, a ramp narrows, a ramp widens, a ramp bifurcation, and/or a ramp intersection.
  • the lane abnormality management module includes: a lane abnormality recognition sub-module for analyzing road feature information to filter out static obstacle targets on the road ahead of the self-driving vehicle, and identify whether a lane abnormality is based on the static obstacle targets And an obstacle avoidance sub-module for guiding the autonomous vehicle to avoid obstacles when the lane is abnormal.
  • the obstacle avoidance sub-module for guiding the autonomous vehicle to avoid obstacles when the lane is abnormal includes: determining obstacle avoidance targets according to the static obstacle targets and the dynamic environmental object targets existing in a set area, and Determine the static and dynamic characteristics of the obstacle avoidance target relative to the autonomous vehicle; establish an obstacle avoidance area adapted to road characteristics based on the static and dynamic characteristics of the obstacle avoidance target; based on the static of the obstacle avoidance target Characteristics and dynamic characteristics to determine the accessibility of the obstacle avoidance area; conduct a collision risk assessment on the relevant environmental object targets during the normal lane change of the autonomous vehicle, and determine the feasibility of lane change according to the result of the collision risk assessment And according to the feasibility of the lane change and the accessibility of the obstacle avoidance area, control the autonomous vehicle to change lanes or to bypass the obstacle avoidance target in the current driving lane.
  • the obstacle avoidance sub-module is used to control the autonomous vehicle to change lanes or to bypass the obstacle avoidance target in the current driving lane according to the feasibility of the lane change and the accessibility of the obstacle avoidance area Including: if the lane change is feasible, control the autonomous vehicle to change lanes; otherwise, determine the passability of the obstacle avoidance area; if the obstacle avoidance area is accessible, determine that the autonomous vehicle is in the current driving lane Drive around the obstacle avoidance target.
  • the lateral decision-making system of the self-driving vehicle has the following advantages: it can evaluate the target lane and lane abnormalities, and accordingly make lane keeping, lane change, or abnormal lane change in accordance with the characteristics of the road Lateral decision, so that the vehicle's control system can perform adaptive lateral control based on the lateral decision to ensure the vehicle's driving safety.
  • Another object of the present invention is to propose a method for determining the lateral decision of an autonomous driving vehicle, so as to realize the correct judgment of the lateral behavior of the vehicle.
  • a method for determining a lateral decision of an autonomous vehicle includes: evaluating target lanes and lane abnormalities required by the autonomous vehicle for lateral decision based on road feature information and pre-selected target lines and environmental object targets; and combining The road characteristic information determines and outputs the expected lateral behavior of the autonomous vehicle according to the evaluated target lane and lane abnormality, wherein the expected lateral behavior includes any one of lane keeping, lane changing, and abnormal lane changing.
  • the evaluation of the target lane and the lane abnormality required by the autonomous vehicle for lateral decision-making includes: selecting the target lane of the autonomous vehicle according to the road feature information, wherein the selection principle of the target lane includes follow the principles of road scenes, the principles of lane attributes, the principle of not selecting abnormal lanes, and the principle of selecting adjacent lanes and selecting in turn to the right when lanes are abnormal, where the road feature information includes the road type, road feature points, and the A lane attribute, and the lane attribute includes a lane feature point attribute and a lane number attribute; and an abnormal lane is identified according to the road characteristic information, and an obstacle avoidance strategy for the abnormal lane is provided.
  • the selection principle of the target lane includes follow the principles of road scenes, the principles of lane attributes, the principle of not selecting abnormal lanes, and the principle of selecting adjacent lanes and selecting in turn to the right when lanes are abnormal
  • the road feature information includes the road type, road feature points, and the A lane attribute
  • the lane attribute includes a lane feature
  • the selecting the target lane of the self-driving vehicle according to the road feature information includes: selecting the target lane according to the selection principle when the self-driving vehicle is driving in a normal scene of the main lane, wherein the main lane Conventional lane scenes include acceleration lanes, normal driving lanes, and deceleration lanes; when the autonomous driving vehicle is driving on a special scene of the main lane, the target lane is selected according to the change in the number of lane attributes of the road ahead relative to the current road, where the master Special road scenes include main road narrowing, main road widening, main road bifurcation and/or tunnel; and when the autonomous vehicle is driving on a ramp scene, according to the change in the number of lanes of the road ahead relative to the current road.
  • the identifying an abnormal lane according to the road feature information and providing an obstacle avoidance strategy for the abnormal lane includes: analyzing the road feature information to screen out the static obstacle target of the road ahead of the autonomous vehicle, and based on the The static obstacle target recognizes whether the lane is abnormal; and when the lane is abnormal, guides the autonomous vehicle to avoid obstacles.
  • the guiding the autonomous vehicle to avoid obstacles includes determining an obstacle avoidance target according to the static obstacle target and the dynamic environment object target existing in a set area, and determining that the obstacle avoidance target is relative to all The static and dynamic characteristics of the self-driving vehicle; based on the static and dynamic characteristics of the obstacle avoidance target to establish an obstacle avoidance area adapted to the road characteristics; based on the static and dynamic characteristics of the obstacle avoidance target, determine the avoidance The accessibility of the obstacle area; perform a collision risk assessment on the relevant environmental object targets during the normal lane change of the autonomous vehicle, and determine the lane change feasibility based on the result of the collision risk assessment; and according to the lane change feasibility And the passability of the obstacle avoidance area, controlling the autonomous vehicle to change lanes or to bypass the obstacle avoidance target in the current driving lane.
  • controlling the autonomous vehicle to change lanes or to bypass the obstacle avoidance target in the current driving lane according to the feasibility of the lane change and the accessibility of the obstacle avoidance area includes: If feasible, control the automatic driving vehicle to change lanes, otherwise judge the passability of the obstacle avoidance area, and if the obstacle avoidance area is passable, determine that the automatic driving vehicle bypasses the avoidance in the current driving lane Obstacle goal.
  • the method for determining the lateral decision of the self-driving vehicle has the same advantages as the above-mentioned lateral decision system over the prior art, and will not be repeated here.
  • Another object of the present invention is to propose a machine-readable storage medium to realize the correct judgment of the lateral behavior of the vehicle.
  • a machine-readable storage medium having instructions stored on the machine-readable storage medium is used to cause a machine to execute the above-mentioned method for determining a lateral decision of an autonomous vehicle.
  • FIG. 1 is a schematic diagram of the region division of the vehicle environment in the vehicle body coordinate system according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a lateral decision system for an autonomous driving vehicle according to an embodiment of the present invention
  • FIG. 3 is an exemplary diagram of target lane selection in a normal driving lane in an embodiment of the present invention
  • FIG. 4(a)-FIG. 4(c) are schematic diagrams of the main road narrowing, the main road widening and the main road bifurcation in the embodiment of the present invention.
  • FIG. 5 is an exemplary diagram of lane abnormality judgment in an embodiment of the present invention.
  • FIG. 6 is an exemplary diagram of lane abnormality recognition of a multi-static obstacle lane in the current lane in an embodiment of the present invention
  • FIG. 7 is a schematic diagram of the vehicle performing obstacle avoidance in the embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a hardware layout of an automatic driving vehicle according to an embodiment of the present invention.
  • FIG. 9 is a schematic flowchart of a method for determining a lateral decision of an autonomous driving vehicle according to an embodiment of the present invention.
  • the “environmental object target” mentioned in the embodiments of the present invention may refer to any object that is moving or stationary in front of, behind, or to the side of the vehicle, for example, vehicles, people, buildings, etc.
  • the “target line” mentioned may Refers to the lane center line, dynamic target line or safety offset line required for lateral decision-making and lateral control of autonomous vehicles (hereinafter referred to as vehicles).
  • the vehicle follows the target line, and the "target lane” corresponds to the "target line”.
  • the decision system will make a decision that the vehicle is driving in the target lane.
  • the “lane anomaly” in the embodiment of the present invention is mainly a target lane anomaly, indicating that the lane is impassable due to static obstacles (such as roadblocks, road cones, and vehicles that cannot move accidents) or red lights at the tunnel entrance. happening.
  • FIG. 1 is a schematic diagram of the area division of the vehicle environment in the vehicle body coordinate system according to the embodiment of the present invention, including the front area of the vehicle, the left front area, etc., the following uses the area division of FIG. 1 to explain the environment object targets, etc. Location.
  • the lateral decision-making system includes: an evaluation unit 100 for evaluating target lanes and lane abnormalities required by the vehicle for lateral decision-making based on road feature information and pre-selected target lines and environmental object targets; and
  • the judging unit 200 is configured to judge and output the expected lateral behavior of the vehicle according to the target lane and the lane abnormality evaluated by the evaluation unit 100 in combination with the road feature information.
  • the road feature information includes a road type, a road feature point and a lane attribute, and the lane attribute includes a lane feature point attribute and a number of lane attributes.
  • the expected lateral behavior includes any one of lane keeping, lane changing and abnormal lane changing.
  • lane keeping means that the vehicle travels along the current lane
  • lane change means that the vehicle moves into the adjacent lane to the left or right.
  • abnormal lanes that is, when the lane keeping and lane changing conditions are not met in front of the lane, the vehicle enters an abnormal lane change (obstacle avoidance state).
  • the three expected horizontal behaviors will be introduced in conjunction with examples below, and will not be repeated here.
  • the evaluation unit 100 includes: a target lane management module 110 for selecting the target lane of the self-driving vehicle according to the road feature information; and a lane abnormality management module 120 for Road feature information identifies abnormal lanes and provides obstacle avoidance strategies for abnormal lanes.
  • the selection principles of the target lane include the principle of following the road scene, the principle of following the attributes of the lane, the principle of not selecting the abnormal lane, and the principle of selecting the adjacent lane and selecting in turn to the right when the lane is abnormal.
  • the principle of following the road scene means that the target lane is to be considered whether the road is the main road or the ramp
  • the principle of following the lane attribute is to consider the change of the lane type when selecting the target lane (judging by the characteristic points of the lane, such as driving in Accelerated lanes) and the number of lanes change.
  • the principle of not selecting abnormal lanes means that the abnormal lanes cannot be used as the target lanes.
  • the principle of selecting adjacent lanes and the order of selection to the right means that when the lanes are abnormal, the neighbors are preferentially selected. For lanes, if multiple lanes are abnormal, select adjacent lanes to the right in turn. It should be noted that the embodiments of the present invention are not limited to these selection principles. In the selection of the target lane, it is necessary to consider more factors in combination with the actual situation. The following will illustrate the four selection principles and some of them here by examples Other selection principles.
  • the target lane management module 110 includes: a main lane target lane selection sub-module 111 for selecting according to the selection principle when the autonomous vehicle is driving in a normal scene of the main lane
  • the target lane is also used to select the target lane according to the change of the number of lane attributes of the road ahead relative to the current road when the autonomous vehicle is driving in the special scene of the main lane; and the ramp target lane selection submodule 112 is used to When the self-driving vehicle is traveling on a ramp scene, the target lane is selected according to the change in the attribute of the number of lanes of the road ahead relative to the current road.
  • the normal scenes of the main lane include acceleration lanes, normal driving lanes, and deceleration lanes. These three lanes belong to the normal lane of the vehicle, and these three lanes can be identified by the lane attributes.
  • the acceleration lane (following the attributes of the lane).
  • the target lane should select the rightmost lane.
  • the target lane is abnormal, it should Select the adjacent lane of the original target lane, and according to the characteristics of the acceleration lane on the right side of the road (follow the road scene), try to select the target lane to the right.
  • the section of the road between the normal driving lane and the starting point of the deceleration lane is less than the warning distance, and the planned deceleration lane on the high speed is called the deceleration lane (following the attributes of the lane).
  • the target lane should be replaced with the far right lane to prepare for entering the deceleration lane and the ramp part in advance.
  • the adjacent lane of the original target lane should be selected, and as far as possible to the right, so that the vehicle can choose the appropriate time to enter the deceleration lane and the ramp part and leave the road section as soon as possible.
  • the target lane selection for normal driving lanes is described in detail below.
  • the normal driving lane here refers to the section from the vehicle exiting the acceleration lane and entering the main highway of the expressway, and away from entering the deceleration lane (following the attributes of the lane), which does not include the special scene of the main lane mentioned above.
  • FIG. 3 is an example diagram of target lane selection in a normal driving lane in an embodiment of the present invention.
  • the position of the vehicle, the original target lane position, and the position of obstacles can be adapted to the following scenarios to change, and will not be shown here one by one.
  • Various changes can be understood by those skilled in the art in conjunction with the text.
  • the principles of target lane selection for normal driving lanes are as follows:
  • Two lanes (for example, only two lanes of C3 and C4): both lanes are normal, and the right lane is the target lane; only one lane is normal (for example, C3 is normal), and the normal lane is the target lane.
  • Three lanes (for example, only three lanes of C2, C3, and C4): all three lanes are normal and the middle lane is the target lane; the middle lane is abnormal and the right lane is the target lane; only one lane is normal and the normal lane is the target lane.
  • the number of lanes is greater than three: the second lane on the left is the target lane. For example, when C1-C4 are normal, select C2 as the target lane.
  • the target lane When the target lane is abnormal, select the target lane according to the principle of gradually to the right. When the abnormality disappears, return to the original target lane. As shown in the current Figure 3, the target lane should be C2. However, there are static obstacles in C2 that cause the C2 lane to be abnormal and cannot pass through. At this time, the target lane is set to the C3 lane. When the vehicle exceeds the obstacle and the C2 lane is normal , The target lane remains the C2 lane. Similarly, if the front of the C2 and C3 lanes is also abnormally unable to pass, then the target lane is placed in the C4 lane, and when there are more lanes, the analogy is in turn.
  • the target lane selects the adjacent lane on the right and selects it to the right in turn, which is helpful for parking the autonomous vehicle in the emergency lane or off the highway more quickly when there is an abnormality in the road ahead.
  • the selection of target lanes in some special scenes can be modified to make it more in line with human driving habits, such as the following selection principles:
  • the target lane is abnormal, and the current lane of the vehicle is normal, the current lane is the target lane.
  • the lane is abnormal, and the nearest normal lane is selected as the target lane.
  • the left and right sides are the same, select the right side as the target lane.
  • the original target lane is C2.
  • C2 and C3 in front are abnormal. Therefore, the vehicle is at C2 and is closer to C1. Therefore, the target lane is placed at C1.
  • target lane selection is not limited to the number of lanes, and any principle that conforms to the above scenario can be adopted.
  • the special scene of the main road mainly includes the main road narrowing, the main road widening, the main road bifurcation (separate subgrade) and/or tunnel.
  • the principle of selecting the target lane under the scenario of narrowing the main lane is: changing the lane attributes (normal lane ⁇ narrowing lane) by 1000m (standard amount) in advance; if the original target lane is a road narrowing lane, the original target lane is set The adjacent normal lane is the target lane.
  • FIG. 4(b) is a schematic diagram of the main road widening in the embodiment of the present invention, where the main road widening refers to the automatic driving vehicle driving on the main road, the number of front lanes increases, which includes left side widening and right side widening Three cases of wide and widening on both sides.
  • the principle of selecting the target lane in the scenario of widening the main lane is: change the lane attributes (normal lane ⁇ widening lane) by 500m (calibration value) in advance; the vehicle travels along the current target lane until it enters the widening area , The number of lanes changes, re-select the target lane.
  • the current number of lanes is 2
  • the target lane is the rightmost lane
  • the vehicle travels along the current road
  • the attribute of the number of lanes where the vehicle is located changes from 2 to 3
  • the target lane is C2.
  • the number of lanes becomes 3 after the left side becomes wider and the right side becomes wider. After the two sides become wider, the number of lanes becomes 4. According to the changed number of lanes, the principles mentioned above are used again Select the target lane.
  • FIG. 4(c) is a schematic diagram of a main road bifurcation in an embodiment of the present invention, where the main road bifurcation is also called a split roadbed, and the road points in two different directions, generally accompanied by changes in the number of lane attributes.
  • the principle of selecting the target lane under the main road bifurcation scenario includes: changing the lane attributes (normal lane ⁇ main road bifurcation) by 500m (calibration value) in advance; taking the 4-lane target direction to the right (the scenario of left) (Similar to this) For example, when there is 1 lane in front of the target side, the lane is the target lane, when there are 2 lanes in front of the target side, the right lane is the target lane, and when there are 3 lanes in front of the target side, the middle lane is Target lane.
  • the selection principle of the target lane of the tunnel is the same as or similar to the normal driving lane corresponding to FIG. 3 described above, and details are not described herein again.
  • the ramp scenario includes a conventional ramp, a ramp narrows, a ramp widens, a ramp bifurcation, and/or a ramp intersection.
  • the main lanes corresponding to the above Figures 4(a)-4(c) are narrowed, the main lane is widened, and the main lane is branched for the target lanes of the ramp narrowing, ramp widening, and ramp bifurcation
  • the target lanes are selected to be the same or similar, the difference is mainly that the main lane becomes a ramp, and those skilled in the art can understand it based on the ramp road conditions, so they will not repeat them here.
  • the target lane selects the rightmost lane.
  • the target lane is selected to be close to the rightmost lane, and the principle of target lane selection follows the principle of keeping to the right as much as possible.
  • ramp merge For the intersection of ramps, or ramp merge, it means that ramps in different directions merge into one.
  • the vehicle is driving on a ramp, and the lane attribute (normal ramp ⁇ interchange ramp) is changed by 500m (calibrated value) in advance.
  • the attribute of the ramp lane number changes.
  • the vehicle runs along the current target lane and merges into the intersection ramp. After the change, re-select the target lane according to the new lane number.
  • the target lane management module 110 selects the target lanes of the main lane and the ramp according to laws and regulations on different speed limits of different lanes on the highway so that the vehicle travels at a faster speed in a predetermined direction
  • Priority driving lanes, and the priority driving lanes are planned to avoid collision hazards caused by large lateral deviations of the vehicle due to inaccurate map positioning, ensuring that the vehicle can drive at a faster speed under the premise of safety , And its target lane selection planning method conforms to people's driving habits.
  • the lane abnormality management module 120 includes: a lane abnormality recognition sub-module 121 for analyzing road feature information to filter out static obstacle targets of the road ahead of the autonomous vehicle, And identify whether the lane is abnormal based on the static obstacle target; and the obstacle avoidance sub-module 122 is used to guide the autonomous vehicle to avoid obstacles when the lane is abnormal.
  • the lane abnormality recognition sub-module 121 is also used to provide the target lane management module 110 with the identified lane abnormality information, so that the target lane management module 110 selects the target lane in combination with the lane abnormality.
  • the lane abnormality recognition sub-module 121 should include three parts: static obstacle target selection, lane abnormality judgment, and lane static multi-static obstacle lane abnormality recognition.
  • the principle of static obstacle target selection includes: extracting road feature information (number of lanes, width of each lane, etc.) of the current driving section of the vehicle, road attachment information, and environmental object target information.
  • road feature information number of lanes, width of each lane, etc.
  • environmental object target information use the nearest environmental object target from the vehicle as a reference to filter static obstacle targets (also called static obstacles) in each lane within a certain range.
  • the static obstacle targets are mainly static object targets such as road cones, roadblocks, and faulty vehicles.
  • the horizontal and vertical distance information of each static obstacle target relative to the own vehicle can be extracted for each lane.
  • FIG. 5 is an exemplary diagram of lane abnormality judgment in the embodiment of the present invention, which takes the current lane where the vehicle is located as an example, and the principle of abnormality judgment of other lanes is similar to this.
  • the driving area of the autonomous vehicle is as shown in ABCE, and the driving width D is the static target 1 and the center of the lane in the range of D2.
  • the vehicle When the vehicle is driving at the tunnel entrance, it is also necessary to identify the traffic lights of each lane of the tunnel entrance.
  • the lane When the lane is a red light, the lane is set to an abnormal lane (from the entrance to the exit is abnormal); until the autonomous vehicle is driven out
  • the system re-identifies whether the road status is a tunnel and re-identifies the traffic lights.
  • FIG. 6 is an exemplary diagram of lane abnormality recognition of a multi-static obstacle lane in the present lane in an embodiment of the present invention.
  • the obstacle avoidance sub-module 122 which is used to guide the autonomous vehicle to avoid obstacles when the lane is abnormal, it may mainly include the following steps: according to the static obstacle target and the dynamic environment objects existing in the set area
  • the target determines the obstacle avoidance target, and determines the static characteristics and dynamic characteristics of the obstacle avoidance target relative to the autonomous vehicle;
  • the obstacle avoidance area adapted to the road characteristics is established based on the static characteristics and dynamic characteristics of the obstacle avoidance target;
  • the static and dynamic characteristics of the obstacle avoidance target to determine the passability of the obstacle avoidance area; to perform collision risk assessment on relevant environmental object targets during normal lane change of the autonomous vehicle, and according to the collision risk assessment
  • the result of is to determine the feasibility of lane change; and according to the feasibility of the lane change and the accessibility of the obstacle avoidance area, control the autonomous vehicle to change lanes or to bypass the obstacle avoidance target in the current driving lane.
  • the functions implemented by the obstacle avoidance sub-module 122 mainly include the following parts.
  • Obstacle avoidance targets include static obstacles and dynamic obstacles. The selection principle is based on the target of the closest object in the area to the autonomous vehicle.
  • Static obstacles are mainly static object targets such as road cones, roadblocks, and faulty vehicles. Obstacle avoidance targets include: 1 static object targets in the front area; 2 static object targets in the left front area; 3 static object targets in the right front area; 4 left side Area static object target; 5 Static object target in the right side area.
  • Dynamic obstacles are mainly moving object targets.
  • Obstacle avoidance targets include: 1Dynamic object targets in the area in front of the front are lower than the speed of the autonomous vehicle; 2Dynamic object targets in the area in the front left are lower than the speed of the autonomous vehicle; 3The area in the front right is low Dynamic object targets for the speed of autonomous vehicles; 4 Dynamic object targets for the left side area; 5 Dynamic object targets for the right side area; 6 Dynamic object targets for the left rear area higher than the speed of the autonomous vehicle; 7 Right rear area high A dynamic object target for the speed of autonomous vehicles.
  • the traditional obstacle avoidance area establishment method usually establishes a fan-shaped area, using 1/2 of the fan-shaped angle as a deflection posture, and avoids obstacles before following.
  • This method is suitable for low-speed autonomous driving vehicles such as city/rural roads without lane lines.
  • the road characteristics should be considered in the establishment of the obstacle avoidance area, so that the obstacle avoidance behavior of the automatic driving conforms to the behavior requirements of the highway for the driver (such as driving in this lane, except overtaking There is no line pressing outside; no dragon driving; no speed, no speed, etc.).
  • FIG. 7 is a schematic diagram of obstacle avoidance of a vehicle in an embodiment of the present invention, wherein the area formed by ABCD is the obstacle avoidance area, the arc length of arcs AC and BD is equal to 200 meters, and the curvature is equal to the curvature of lane line L2, that is, AC and BD Parallel to the road, the size of the area is determined by obstacle avoidance targets G1 and G2.
  • the target G1 is a dynamic object target in the area directly in front of it.
  • the relationship between G1 and the vehicle includes the outer contour points, that is, the lateral closest point G11 and the longitudinal closest point G12.
  • the curve s1 parallel to the road is constructed by G11, and the longitudinal closest point G12 to The intersection point of the vertical line of curve s1 is G13, and G13 is used as the outer contour point of obstacle avoidance for target G1.
  • a safety distance of d2 0.3 m is added to generate a BD curve.
  • the target G2 is a static object target (roadblock) in the front left area.
  • the vehicle width W+safe distance d3 is the most passable judgment condition.
  • the width of the obstacle avoidance area is greater than (W+d3), it is automatically Obstacle avoidance can be carried out by driving the vehicle; otherwise, the autonomous vehicle re-judges other obstacle avoidance areas (such as whether obstacle avoidance areas can be generated on the right).
  • the obstacle avoidance sub-module 122 is used to control the autonomous vehicle to change lanes or bypass the current driving lane based on the feasibility of the lane change and the accessibility of the obstacle avoidance area
  • the obstacle avoidance target driving includes: if the lane change is feasible, control the automatic driving vehicle to change lanes; otherwise, judge the passability of the obstacle avoidance area; if the obstacle avoidance area is passable, determine the automatic driving The vehicle travels around the obstacle avoidance target in the current driving lane.
  • the obstacle avoidance sub-module 122 is used to identify whether a lane change is required, including the following three aspects: generation of a lane change intention (that is, collision risk assessment), lane change direction judgment, and lane change feasibility judgment.
  • the automatic driving vehicle determines whether the vehicle needs to change lanes according to the relative distance between the vehicle and the preceding vehicle and the speed tradeoff, and reduces the frequency of the lane change of the automatic driving vehicle.
  • the autonomous vehicle Assuming that the threshold for the lane change intention expectation factor is set to ⁇ , the speed of the autonomous vehicle V_auto, the target vehicle speed V_trg, the relative distance between the autonomous vehicle and the target vehicle Dis_rely, the autonomous vehicle expects a safe driving distance K*V_auto where K takes priority 0.8 .
  • the self-driving vehicle When a self-driving vehicle runs normally and a static object appears in front of the detection area, the self-driving vehicle should change lanes in advance to avoid collision with the static object in front.
  • the autonomous vehicle Assuming that the threshold of the lane change intention expectation factor is set to ⁇ s, the speed of the autonomous vehicle V_auto, the relative distance Dis_s between the autonomous vehicle and the static obstacle, the autonomous vehicle expects the safe driving distance K*V_auto, where K takes priority 1.
  • the lane-changing intention expectation factor ⁇ s K1*(Dis_s/K*V_auto), where K1 preferentially takes the value 1.
  • K1 preferentially takes the value 1.
  • TTC is the time between the collision of the autonomous vehicle and the front vehicle
  • the left-hand lane change of the self-driving vehicle is prioritized, that is, when the left-front and right-front areas meet the conditions a to f at the same time, the left lane is preferentially selected as the target lane.
  • the self-driving vehicle determines the target lane for lane change based on the above conditions a) to g).
  • Vehicles must strictly comply with road traffic regulations, such as virtual and solid lines, speed limits, lights, horns, traffic lights, and no U-turns.
  • the obstacle avoidance sub-module 122 of the embodiment of the present invention proposes an obstacle avoidance method suitable for vehicles traveling at high speeds and structured roads, which can avoid manual driving that may cause vehicle collisions due to blind spots, and its lane-changing function can improve vehicle driving efficiency and reduce drivers
  • the workload, and the involved automatic lane changing method has a wide range of application, and can be applied to automatic driving systems under curved roads with a large curvature and straight roads, especially under structured roads.
  • the lane abnormality management module 120 of the embodiment of the present invention can recognize the lane situation, and can actively guide the vehicle to avoid obstacles in advance or gradually approach the emergency lane or drive away from the highway to avoid the risk of collision of vehicles.
  • FIG. 8 is a schematic diagram of a hardware arrangement of an autonomous driving vehicle according to an embodiment of the present invention, wherein the decision-making system of the autonomous driving vehicle includes the lateral decision-making system of the foregoing embodiment.
  • control unit 1 the control unit 2, and the control unit 4 constitute an environment awareness system
  • control unit 3 constitutes a lateral decision-making system according to an embodiment of the present invention, which is part of the vehicle's decision-making system.
  • the control unit 1 provides accurate location information for autonomous vehicles, and high-precision GPS+IMU equipment is preferred, with a lateral positioning deviation within 10 cm and a longitudinal positioning deviation within 30 cm.
  • the control unit 2 is used to store and output high-precision lane lines, number of lanes, lane width and other information within 200m from the front and rear of the self-driving vehicle. It preferentially uses hardware devices with storage space greater than 50G and processing memory greater than 1G.
  • the control unit 4 is used for detecting and extracting objects and objects appearing in the range of 360° around the self-driving vehicle, and preferentially selects all-weather sensor detection equipment to avoid misdetection and missed detection of objects and objects caused by rain, snow, fog, and light.
  • the control unit 4 is not limited to the current installation location or the current number.
  • several radar sensors lidar or millimeter wave radar equipment, etc.
  • visual sensors are arranged around the vehicle body. Improve the accuracy and stability of object detection.
  • the control unit 2 obtains the accurate position information of the automatic driving vehicle provided by the control unit 1, and outputs the high-precision map data within 200m in front of and behind the automatic driving vehicle in real time after processing and calculation, including: the latitude and longitude of the discrete points of the lane line (the latitude and longitude are based on the center of the earth) , Discrete point heading angle (take the clockwise direction of 0° in the north direction as evidence), lane line type, lane width, lane number, road boundary and other information, the control unit 3 will receive the lane line offline data through the Ethernet Converted to the plane vehicle coordinate system, providing the road characteristic information required during the vehicle lane change, the control unit 4 simultaneously transmits the object information of the objects in the detection area to the control unit 3 by CAN communication, and the control unit 3 executes the above-mentioned lateral decision The function of the system.
  • the lateral decision-making system of the embodiment of the present invention can evaluate the target lane and lane anomalies, and make lateral decisions on lane keeping, lane-changing, or abnormal lane-changing in accordance with road characteristics in order to facilitate the vehicle control system.
  • An adaptive lateral control can be performed based on this lateral decision to ensure the driving safety of the vehicle.
  • FIG. 9 is a schematic flow chart of a method for determining a lateral decision of an autonomous vehicle according to an embodiment of the present invention.
  • the method for determining a lateral decision is based on the same inventive idea as the lateral decision system described above.
  • the method for determining a lateral decision of an autonomous vehicle may include the following steps S100 and S200:
  • Step S100 based on the road feature information and the pre-selected target line and environmental object target, evaluate the target lane and lane abnormality required by the autonomous vehicle for lateral decision.
  • this step S100 further includes the following sub-steps:
  • Step S110 Select a target lane of the self-driving vehicle according to the road feature information.
  • the selection principle of the target lane includes the principle of following the road scene, the principle of following the attribute of the lane, the principle of not selecting the abnormal lane, and the principle of selecting the adjacent lane and selecting in turn to the right when the lane is abnormal.
  • this step S110 specifically includes: when the self-driving vehicle is driving in a normal scene of the main road, a target lane is selected according to the selection principle, wherein the normal scene of the main road includes an acceleration lane, a normal driving lane and Deceleration lane; when the self-driving vehicle is driving in the special scene of the main road, select the target lane according to the change of the attribute of the number of lanes of the road ahead relative to the current road, wherein the special scene of the main road includes the main road narrowing and the main road Widening, main road bifurcation, and/or tunnel; and when the autonomous vehicle is driving on a ramp scene, the target lane is selected according to the change in the number of lane attributes of the road ahead with respect to the current road, where the ramp scene includes conventional Ramp, ramp narrow, ramp wide, ramp bifurcation and/or ramp intersection.
  • Step S120 identify an abnormal lane according to the road feature information, and provide an obstacle avoidance strategy for the abnormal lane.
  • this step S120 specifically includes: analyzing the road feature information to filter out the static obstacle target of the road ahead of the autonomous vehicle, and identifying whether the lane is abnormal based on the static obstacle target; and when the lane is abnormal, Guiding the autonomous vehicle to avoid obstacles.
  • the guiding the autonomous vehicle to avoid obstacles includes determining an obstacle avoidance target according to the static obstacle target and the dynamic environment object target existing in a set area, and determining that the obstacle avoidance target is relative to all The static and dynamic characteristics of the self-driving vehicle; based on the static and dynamic characteristics of the obstacle avoidance target to establish an obstacle avoidance area adapted to the road characteristics; based on the static and dynamic characteristics of the obstacle avoidance target, determine the avoidance The accessibility of the obstacle area; perform a collision risk assessment on the relevant environmental object targets during the normal lane change of the autonomous vehicle, and determine the lane change feasibility based on the result of the collision risk assessment; and according to the lane change feasibility And the passability of the obstacle avoidance area, controlling the autonomous vehicle to change lanes or to bypass the obstacle avoidance target in the current driving lane.
  • controlling the autonomous vehicle to change lanes or to bypass the obstacle avoidance target in the current driving lane according to the feasibility of the lane change and the accessibility of the obstacle avoidance area includes: If the road is feasible, the automatic driving vehicle is controlled to change lanes, otherwise the passability of the obstacle avoidance area is judged, and if the obstacle avoidance area is passable, it is determined that the automatic driving vehicle bypasses the current driving lane Obstacle avoidance target driving.
  • Step S200 Combine the road feature information, determine and output the expected lateral behavior of the autonomous vehicle according to the evaluated target lane and lane abnormality.
  • the method for determining the lateral decision of the automatic driving vehicle is the same as the specific implementation details and effects of the above-described embodiment of the lateral decision system of the automatic driving vehicle, and details are not repeated herein.
  • phase change memory abbreviation of phase change random access memory, Phase Change Random Access Memory, PRAM, also known as RCM/PCRAM
  • SRAM static random access memory
  • DRAM dynamic Random access memory
  • RAM random access memory
  • ROM read only memory
  • EEPROM electrically erasable programmable read only memory
  • flash memory Flash or other memory Technology
  • CD-ROM compact disc
  • DVD digital versatile disc

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Abstract

La présente invention porte sur un système de décision horizontale de véhicule à conduite autonome et sur un procédé de prise de décision horizontale, le système de décision horizontale comprenant : une unité d'évaluation, utilisée pour évaluer une voie cible et un état d'exception de voie requis par le véhicule à conduite autonome effectuant une décision horizontale selon des informations de caractéristique de trajet et une ligne cible présélectionnée et une cible d'objet d'environnement ; et une unité de détermination, utilisée pour intégrer des informations de caractéristique de trajet, et pour déterminer et transmettre un comportement horizontal attendu du véhicule à conduite autonome en fonction de la voie cible et de l'état d'exception de voie évalué par l'unité d'évaluation, le comportement horizontal attendu comprenant l'un quelconque parmi le maintien d'une voie, le changement de voies et le changement anormal de voies. Le système et le procédé de décision horizontale de la présente invention peuvent évaluer une voie cible et un état d'exception de voie et, selon ceci, prendre une décision horizontale correspondant à des caractéristiques de route de sorte à faciliter un système de commande de véhicule effectuant une commande horizontale appropriée sur la base de cette décision.
PCT/CN2019/129284 2018-12-29 2019-12-27 Système de décision horizontale de véhicule à conduite autonome et procédé de prise de décision horizontale WO2020135742A1 (fr)

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CN118323141B (zh) * 2024-06-12 2024-10-11 知行汽车科技(苏州)股份有限公司 一种控制车辆行驶的方法、装置、设备及介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105980811A (zh) * 2014-02-25 2016-09-28 爱信艾达株式会社 路径搜索系统、路径搜索方法、计算机程序、以及成本表的数据结构
CN107408343A (zh) * 2015-03-31 2017-11-28 爱信艾达株式会社 自动驾驶辅助系统、自动驾驶辅助方法以及计算机程序
CN107430807A (zh) * 2015-03-31 2017-12-01 爱信艾达株式会社 自动驾驶辅助系统、自动驾驶辅助方法以及计算机程序
CN107533805A (zh) * 2015-04-27 2018-01-02 爱信艾达株式会社 自动驾驶辅助系统、自动驾驶辅助方法以及计算机程序

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108431549B (zh) * 2016-01-05 2020-09-04 御眼视觉技术有限公司 具有施加的约束的经训练的系统
JP6724988B2 (ja) * 2016-07-05 2020-07-15 日産自動車株式会社 走行制御方法及び走行制御装置
JP6583185B2 (ja) * 2016-08-10 2019-10-02 トヨタ自動車株式会社 自動運転システム及び自動運転車両
CN106740835B (zh) * 2016-11-21 2019-10-22 北汽福田汽车股份有限公司 车辆自适应巡航控制方法、装置和车辆
CN106681319A (zh) * 2016-12-09 2017-05-17 重庆长安汽车股份有限公司 自动换道系统及方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105980811A (zh) * 2014-02-25 2016-09-28 爱信艾达株式会社 路径搜索系统、路径搜索方法、计算机程序、以及成本表的数据结构
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CN107430807A (zh) * 2015-03-31 2017-12-01 爱信艾达株式会社 自动驾驶辅助系统、自动驾驶辅助方法以及计算机程序
CN107533805A (zh) * 2015-04-27 2018-01-02 爱信艾达株式会社 自动驾驶辅助系统、自动驾驶辅助方法以及计算机程序

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