CN110667578B

CN110667578B - Lateral decision making system and lateral decision making determination method for automatic driving vehicle

Info

Publication number: CN110667578B
Application number: CN201811642027.5A
Authority: CN
Inventors: 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
Original assignee: Haomo Zhixing Technology Co Ltd
Current assignee: Haomo Zhixing Technology Co Ltd
Priority date: 2018-12-29
Filing date: 2018-12-29
Publication date: 2021-09-17
Anticipated expiration: 2038-12-29
Also published as: WO2020135742A1; CN110667578A

Abstract

The invention relates to the technical field of automatic driving, and provides a transverse decision making system and a transverse decision making determination method for an automatic driving vehicle. The transverse decision making system comprises: the evaluation unit is used for evaluating a target lane and lane abnormal conditions required by the automatic driving vehicle for transverse decision according to the road characteristic information, a preselected target line and an environmental object target; and the judging unit is used for judging and outputting the expected transverse behavior of the automatic driving vehicle according to the target lane and the lane abnormal condition evaluated by the evaluating unit by combining the road characteristic information, wherein the expected transverse behavior comprises any one of lane keeping, lane changing and abnormal lane changing. The invention can evaluate the target lane and the abnormal condition of the lane, and make the transverse decision according with the road characteristic according to the abnormal condition, so that the control system of the vehicle can carry out the adaptive transverse control based on the transverse decision.

Description

Lateral decision making system and lateral decision making determination method for automatic driving vehicle

Technical Field

The invention relates to the technical field of automatic driving, in particular to a transverse decision making system and a transverse decision making determination method for an automatic driving vehicle.

Background

The automatic Driving vehicle is an intelligent vehicle which senses road environment through a vehicle-mounted sensing System, automatically plans a Driving route and controls the vehicle to reach a preset destination, and the automatic Driving vehicle realizes the functions of the automatic Driving vehicle by means of an automatic Driving System (ADS for short). According to the development and design process of the ADS, the ADS can be divided into: the system comprises an environment perception system, a data fusion system, a decision-making system, a control system and an execution system.

Specifically, the environment sensing system is used for extracting current running environment information of vehicles such as vehicles, pedestrians, roads, traffic signs and the like through the vehicle-mounted sensing system; the data fusion system is used for screening, correlating, tracking, filtering and the like the data information of different sensors so as to obtain more accurate information such as a road, an environmental object target and the like; the decision system is used for logically judging and outputting the vehicle behaviors of the unmanned vehicle according to the driving states, roads, environment information and the like of the vehicles in different environments output by the data fusion system; the control system is used for calculating and outputting the transverse and longitudinal control variable quantity of the current vehicle in real time according to the information output by the data fusion system and the decision system; the execution system is used for replacing the operation processes of a steering wheel, an acceleration pedal and a deceleration pedal of the vehicle by a driver according to the control quantity of steering, acceleration and the like output by the control system.

More specifically, the decision-making system determines and outputs lateral and longitudinal vehicle behaviors of the autonomous vehicle according to the input information of the environmental object target, the road and the like, wherein the lateral vehicle behavior is expressed as lane keeping, lane changing, abnormal lane changing and the like, and the longitudinal vehicle behavior is expressed as acceleration, deceleration and the like. The lane keeping, lane changing and abnormal lane changing are main behaviors of the vehicle in running, and the control system has a significant effect on driving safety by correctly controlling the three behaviors. Therefore, how the decision-making system correctly judges the transverse behaviors of the vehicle such as lane keeping, lane changing, abnormal lane changing and the like is an important factor to be considered when the whole vehicle decision-making system is designed.

Disclosure of Invention

In view of the above, the present invention is directed to a lateral decision system for an automatic driving vehicle, so as to achieve a correct determination of a lateral behavior of the vehicle.

In order to achieve the purpose, the technical scheme of the invention is realized as follows:

a lateral decision making system for an autonomous vehicle, comprising: the evaluation unit is used for evaluating a target lane and lane abnormal conditions required by the automatic driving vehicle for transverse decision according to the road characteristic information, a preselected target line and an environmental object target; and the judging unit is used for judging and outputting the expected transverse behavior of the automatic driving vehicle according to the target lane and the lane abnormal condition evaluated by the evaluating unit by combining the road characteristic information, wherein the expected transverse behavior comprises any one of lane keeping, lane changing and abnormal lane changing.

Further, the evaluation unit includes: the target lane management module is used for selecting a target lane of the automatic driving vehicle according to the road characteristic information, wherein the selection principle of the target lane comprises a principle of following a road scene, a principle of following a lane attribute, a principle of not selecting an abnormal lane and a principle of selecting an adjacent lane and sequentially selecting the adjacent lane to the right when the lane is abnormal, the road characteristic information comprises a road type, a road characteristic point and the lane attribute, and the lane attribute comprises a lane characteristic point attribute and a lane number attribute; and the lane abnormity management module is used for identifying an abnormal lane according to the road characteristic information and providing an obstacle avoidance strategy aiming at the abnormal lane.

Further, the target lane management module includes: a main lane target lane selection submodule for selecting a target lane according to the selection principle when the autonomous vehicle is running in a main lane conventional scene, wherein the main lane conventional scene comprises an acceleration lane, a normal running lane and a deceleration lane; and selecting a target lane according to a change in lane number attribute of a road ahead with respect to a current road when the autonomous vehicle is traveling in a main lane special scene, wherein the main lane special scene includes a main lane narrowing, a main lane widening, a main lane branching and/or a tunnel; and a ramp target lane selection submodule for selecting a target lane according to a change of a lane number attribute of a road ahead relative to a current road when the autonomous vehicle runs in a ramp scene, wherein the ramp scene includes a conventional ramp, a ramp narrowing, a ramp widening, a ramp bifurcation and/or a ramp junction.

Further, the lane abnormality management module includes: a lane abnormality recognition submodule for analyzing the road characteristic information to screen out a static obstacle target of a road ahead of the autonomous vehicle, and recognizing whether a lane is abnormal based on the static obstacle target; and the obstacle avoidance submodule is used for guiding the automatic driving vehicle to avoid the obstacle when the lane is abnormal.

Further, the obstacle avoidance sub-module is configured to guide the autonomous vehicle to avoid an obstacle when the lane is abnormal, and 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 the static characteristic and the dynamic characteristic of the obstacle avoidance target relative to the automatic driving vehicle; establishing an obstacle avoidance area adaptive to road characteristics based on the static characteristics and the dynamic characteristics of the obstacle avoidance target; judging the trafficability of the obstacle avoidance area based on the static characteristic and the dynamic characteristic of the obstacle avoidance target; performing collision risk evaluation on a related environment object target when the automatic driving vehicle normally changes lanes, and determining lane changing feasibility according to a collision risk evaluation result; and controlling the automatic driving vehicle to change the lane or drive around the obstacle avoidance target in the current driving lane according to the lane changing feasibility and the trafficability of the obstacle avoidance area.

Further, the obstacle avoidance sub-module is configured to control the autonomous vehicle to change lanes or drive around the obstacle avoidance target in the current driving lane according to the lane change feasibility and the feasibility of the obstacle avoidance area, and includes: if the lane changing is feasible, controlling the automatic driving vehicle to change the lane, otherwise, judging the trafficability of the obstacle avoidance area, and if the obstacle avoidance area is trafficable, determining that the automatic driving vehicle drives around the obstacle avoidance target in the current driving lane.

Compared with the prior art, the transverse decision-making system of the automatic driving vehicle has the following advantages: the lane keeping and lane changing system can evaluate the abnormal conditions of a target lane and the lane, and accordingly make a transverse decision of lane keeping, lane changing or abnormal lane changing according with road characteristics, so that a control system of a vehicle can perform adaptive transverse control based on the transverse decision to ensure the driving safety of the vehicle.

Another objective of the present invention is to provide a method for determining a lateral decision of an autonomous vehicle, so as to realize a correct determination of the lateral behavior of the vehicle.

a lateral decision determination method for an autonomous vehicle, comprising: evaluating a target lane and lane abnormal conditions required by the automatic driving vehicle for transverse decision according to the road characteristic information, a preselected target line and an environmental object target; and determining and outputting an expected lateral behavior of the autonomous vehicle according to the evaluated target lane and lane abnormality in combination with the road characteristic information, wherein the expected lateral behavior comprises any one of lane keeping, lane changing and abnormal lane changing.

Further, the evaluating target lane and lane anomalies required for lateral decision making by the autonomous vehicle comprises: selecting a target lane of the automatic driving vehicle according to the road characteristic information, wherein the selection principle of the target lane comprises a principle of following a road scene, a principle of following lane attributes, a principle of not selecting an abnormal lane and a principle of selecting an adjacent lane and selecting the adjacent lane sequentially on the right when the lane is abnormal, wherein the road characteristic information comprises a road type, road characteristic points and the lane attributes, and the lane attributes comprise lane characteristic point attributes and lane number attributes; and identifying an abnormal lane according to the road characteristic information, and providing an obstacle avoidance strategy aiming at the abnormal lane.

Further, the selecting a target lane of the autonomous vehicle according to the road characteristic information includes: when the automatic driving vehicle runs in a main road conventional scene, selecting a target lane according to the selection principle, wherein the main road conventional scene comprises an acceleration lane, a normal running lane and a deceleration lane; when the automatic driving vehicle runs in a main road special scene, selecting a target lane according to the change of the lane number attribute of a front road relative to a current road, wherein the main road special scene comprises main road narrowing, main road widening, main road branching and/or a tunnel; and when the automatic driving vehicle runs on a ramp scene, selecting a target lane according to the change of the lane number attribute of the front road relative to the current road, wherein the ramp scene comprises a conventional ramp, a ramp narrowing, a ramp widening, a ramp branching and/or a ramp intersection.

Further, the identifying an abnormal lane according to the road characteristic information and providing an obstacle avoidance strategy for the abnormal lane includes: analyzing road characteristic information to screen out a static obstacle target of a road ahead of the autonomous vehicle, and identifying whether a lane is abnormal based on the static obstacle target; and guiding the automatic driving vehicle to avoid the obstacle when the lane is abnormal.

Further, the guiding the autonomous vehicle to avoid an obstacle 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 the static characteristic and the dynamic characteristic of the obstacle avoidance target relative to the automatic driving vehicle; establishing an obstacle avoidance area adaptive to road characteristics based on the static characteristics and the dynamic characteristics of the obstacle avoidance target; judging the trafficability of the obstacle avoidance area based on the static characteristic and the dynamic characteristic of the obstacle avoidance target; performing collision risk evaluation on a related environment object target when the automatic driving vehicle normally changes lanes, and determining lane changing feasibility according to a collision risk evaluation result; and controlling the automatic driving vehicle to change the lane or drive around the obstacle avoidance target in the current driving lane according to the lane changing feasibility and the trafficability of the obstacle avoidance area.

Further, the controlling the autonomous vehicle to change lanes or drive around the obstacle avoidance target in the current driving lane according to the lane change feasibility and the trafficability of the obstacle avoidance area includes: if the lane changing is feasible, controlling the automatic driving vehicle to change the lane, otherwise, judging the trafficability of the obstacle avoidance area, and if the obstacle avoidance area is trafficable, determining that the automatic driving vehicle drives around the obstacle avoidance target in the current driving lane.

The transverse decision-making method of the automatic driving vehicle has the same advantages as the transverse decision-making system compared with the prior art, and is not repeated herein.

Another object of the present invention is to propose a machine readable storage medium to enable correct judgment of the lateral behavior of a vehicle.

a machine-readable storage medium having instructions stored thereon for causing a machine to perform the above-described lateral decision making method for an autonomous vehicle.

Additional features and advantages of the invention will be set forth in the detailed description which follows.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention.

In the drawings:

FIG. 1 is a schematic diagram illustrating a vehicle environment divided into regions according to a vehicle coordinate system in an embodiment of the present invention;

FIG. 2 is a schematic diagram of a lateral decision making system for an autonomous vehicle according to an embodiment of the present invention;

FIG. 3 is an exemplary diagram of target lane selection for a normal driving lane in an embodiment of the present invention;

FIGS. 4(a) -4 (c) are schematic views of main lane narrowing, main lane widening, and main lane diverging, respectively, in an embodiment of the present invention;

FIG. 5 is a diagram showing an example of a lane abnormality determination in the embodiment of the present invention;

fig. 6 is an exemplary diagram of lane abnormality recognition of the lane multiple static obstacle in the embodiment of the present invention;

FIG. 7 is a schematic diagram of a vehicle for obstacle avoidance in an embodiment of the present invention;

FIG. 8 is a schematic diagram of the hardware arrangement of an autonomous vehicle in accordance with an embodiment of the invention; and

fig. 9 is a flowchart illustrating a lateral decision making method of an autonomous vehicle according to an embodiment of the present invention.

Detailed Description

In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.

The "environmental object target" mentioned in the embodiment of the present invention may refer to any object moving or stationary in front of, behind or at the side of the vehicle, such as a vehicle, a person, a building, etc., and the "target line" mentioned may refer to a lane center line, a dynamic target line or a safety offset line, etc., required for lateral control, of an autonomous vehicle (hereinafter, simply referred to as a vehicle), the vehicle travels along the target line, and the "target lane" corresponds to the "target line" that the lateral decision system will make a decision that the vehicle travels on the target lane. In addition, the lane abnormity in the embodiment of the invention is mainly the target lane abnormity, which indicates the condition that the lane cannot pass due to static obstacles (such as roadblocks, road cones, vehicles which cannot move accidents, and the like) or a red light at a tunnel entrance.

Fig. 1 is a schematic diagram of region division of a vehicle environment in a vehicle body coordinate system according to an embodiment of the present invention, including a front region, a left front region, and the like of a vehicle, and the positions of environmental object targets and the like will be described below with reference to the region division of fig. 1.

The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 2 is a schematic structural diagram of a lateral decision making system of an autonomous vehicle according to an embodiment of the present invention. As shown in fig. 2, the lateral decision making system includes: the evaluation unit 100 is configured to evaluate a target lane and lane abnormality required for a vehicle to make a transverse decision according to the road characteristic information, a preselected target line and an environmental object target; and a judging unit 200 for judging and outputting an expected lateral behavior of the vehicle according to the target lane and the lane abnormality evaluated by the evaluating unit 100 in conjunction with the road characteristic information.

The road characteristic information includes a road type, a road characteristic point and a lane attribute, and the lane attribute includes a lane characteristic point attribute and a lane number attribute, which will be specifically applied hereinafter and will not be described herein again. In addition, the pre-selected target line and the environmental object target are obtained by other functional modules of the decision system of the vehicle before the longitudinal decision and the transverse decision are made, and a person skilled in the art can understand by referring to the related technology, and the embodiment of the invention does not focus on the point.

Wherein the expected lateral behavior comprises any one of lane keeping, lane changing, and abnormal lane changing. It can be appreciated that lane keeping, i.e. the vehicle is travelling along the current lane; lane changing means that the vehicle enters an adjacent lane to run leftwards or rightwards, and possible influence of surrounding vehicles on the lane changing process of the automatic driving vehicle needs to be considered in the lane changing process; and (4) abnormal lane changing, namely when the front of the lane does not meet the lane keeping and changing conditions, the vehicle enters the abnormal lane changing (obstacle avoiding state). These three expected lateral behaviors will be described with reference to examples, and will not be described herein.

In a preferred embodiment, the evaluation unit 100 comprises: a target lane management module 110 for selecting a target lane of the autonomous vehicle according to the road characteristic information; and a lane abnormality management module 120, configured to identify an abnormal lane according to the road characteristic information, and provide an obstacle avoidance strategy for the abnormal lane.

For the target lane management module 110, the selection principle of the target lane includes a principle of following a road scene, a principle of following a lane attribute, a principle of not selecting an abnormal lane, and a principle of selecting an adjacent lane and sequentially selecting the adjacent lane to the right when the lane is abnormal. For example, the principle of following a road scene refers to selecting whether a target lane is a main lane or a ramp, the principle of following a lane attribute refers to selecting a target lane, considering lane type change (for example, driving into an acceleration lane) and lane number change when the target lane is selected, the principle of not selecting an abnormal lane refers to not being able to use the abnormal lane as the target lane, the principle of selecting an adjacent lane when the lane is abnormal and selecting the adjacent lane to the right in sequence refers to preferentially selecting the adjacent lane when the lanes are abnormal, and if a plurality of lanes are abnormal, selecting the adjacent lane to the right in sequence. It should be noted that the embodiments of the present invention are not limited to these selection principles, and in the selection of the target lane, more factors need to be considered in combination with the actual situation, and the following will illustrate four selection principles and some other selection principles.

In a more preferred embodiment, the target lane management module 110 includes: a main lane target lane selection submodule 111, configured to select a target lane according to the selection principle when the autonomous vehicle is running in a main lane normal scene, and further configured to select a target lane according to a change of a lane number attribute of a front road relative to a current road when the autonomous vehicle is running in a main lane special scene; and a ramp target lane selection submodule 112, configured to select a target lane according to a change in a lane number attribute of a road ahead relative to a current road when the autonomous vehicle travels in a ramp scene.

The following specifically describes the target lane selection in the main road normal scene and the main road special scene focused by the main road target lane selection sub-module 111, and the target lane selection in the ramp scene focused by the ramp target lane selection sub-module 112.

One, main road conventional scene

The conventional scene of the main road comprises an acceleration lane, a normal driving lane and a deceleration lane, the three lanes belong to the normal driving lane of the vehicle, and the three lanes can be identified through lane attributes.

Furthermore, the part before the exit ramp merges into the main lane is called an acceleration lane (following lane attribute), when the automatic driving vehicle enters the acceleration lane, the rightmost lane should be selected as the target lane, when the target lane is abnormal, the adjacent lane of the original target lane should be selected, and according to the characteristic that the acceleration lane is on the right side of the road (following road scene), the target lane should be selected as far as the right as possible.

Further, the section of the road with the distance from the normal driving lane to the starting point of the deceleration lane being smaller than the early warning distance and the part of the deceleration lane planned on the high speed are called the deceleration lane (following lane attribute), when the automatic driving vehicle enters the deceleration lane, according to the characteristic that the deceleration lane is at the rightmost side of the road (following road scene), the target lane is replaced by the rightmost lane, and the advance preparation is made for entering the deceleration lane and the ramp part. When the target lane is abnormal, the adjacent lane of the original target lane should be selected and the adjacent lane should be close to the right as much as possible, so that the vehicle can enter the deceleration lane and the ramp part can leave the road section as soon as possible at a proper time.

The selection of the target lane for the normal driving lane is described in detail below. The normal driving lane here refers to a section of road from the vehicle exiting from the acceleration lane to the main road of the expressway to the vehicle exiting from the deceleration lane (following the lane attribute), and does not include the above-described main road special scene.

Fig. 3 is an exemplary diagram of selecting a target lane of a normal driving lane according to an embodiment of the present invention, in which a position of a host vehicle, a position of an original target lane, and a position of an obstacle may be changed according to a scenario, where various changes are not shown one by one, and may be understood by a person skilled in the art by combining text. With reference to fig. 3, the principle of selecting the target lane of the normal driving lane mainly includes the following points:

1. two lanes (e.g., only two lanes C3, C4): the two lanes are normal, and the right lane is a target lane; only one lane is normal (e.g., C3 normal), the normal lane being the target lane.

2. Three lanes (e.g., only three lanes C2, C3, C4): the three lanes are normal, and the middle lane is a target lane; the middle lane is abnormal, and the right lane is a target lane; only one lane is normal, and the normal lane is the target lane.

3. The number of lanes is more than three: and when the left second lane is the target lane, for example, C1-C4 are normal, C2 is selected as the target lane.

4. When the target lane is abnormal, the target lane is selected according to the principle of gradually righting, and when the abnormality disappears, the target lane returns to the original target lane. As shown in fig. 3, the target lane is supposed to be C2, however, a static obstacle exists in C2, which causes the C2 lane to be abnormal and cannot pass through, at this time, the target lane is set to be C3 lane, and when the vehicle passes over the obstacle and the C2 lane is normal, the target lane still changes to be C2 lane. Similarly, if the lane C2 and the lane C3 are also abnormal and cannot pass through, the target lane is set in the lane C4, and the same goes on when the number of lanes is larger. This is because when the number of lanes is large, the target lane is selected to be close to the left side, because in the highway condition, the left side speed is faster (according to the principle of following the road scene), the vehicle can run at a faster speed, and when the original target lane is abnormal, the target lane is selected to be the right adjacent lane and is sequentially selected to the right, which is beneficial to more rapidly stopping the automatically driven vehicle on the emergency lane or driving away from the highway when the front road is abnormal.

Further, in addition to the above 4 selection principles, the lane selection of the target in some special scenes may be modified to better conform to the driving habits of the people, for example, the following selection principles:

1) and if the target lane is abnormal and the current lane where the vehicle is located is normal, the current lane is the target lane.

2) And if the lane is abnormal, selecting the nearest normal lane as the target lane. And when the left side and the right side are the same, selecting the right side as the target lane. Referring to fig. 3, the original target lane is C2, and the front lanes C2 and C3 are abnormal, so that the vehicle is at C2 and is closer to C1, and the target lane is set at C1.

It should be noted that the selection of the target lanes is not limited to the number of lanes, and corresponding principles may be adopted whenever the target lanes meet the above scenario.

Second, main road special scene

The special scene of the main road mainly comprises narrowing of the main road, widening of the main road, bifurcation of the main road (separated roadbed) and/or a tunnel.

1. Narrowing of the main road

Fig. 4(a) is a schematic diagram of main lane narrowing in the embodiment of the present invention, in which the main lane narrowing means that the autonomous vehicle travels on the main lane, and the number of lanes in front is reduced, which includes three cases of left-side narrowing, right-side narrowing, and both-side narrowing. In the embodiment of the invention, the principle of selecting the target lane under the situation that the main lane is narrowed is as follows: lane change attribute 1000m (scalar amount) ahead (normal lane → narrow lane); and if the original target lane is a road narrowing lane, setting the adjacent normal lane of the original target lane as a target lane.

The main road narrowing and the main road widening below, the main road branching and the selection principle when the lane corresponding to the tunnel is abnormal are similar to the conventional scene of the main road, and the details are not repeated here.

2. Widening of main track

Fig. 4(b) is a schematic diagram of widening of the main lane in the embodiment of the present invention, in which widening of the main lane means that the autonomous vehicle travels on the main lane, and the number of lanes in front is increased, which includes three cases of widening on the left side, widening on the right side, and widening on both sides.

In the embodiment of the invention, the principle of selecting the target lane under the situation of widening the main lane is as follows: lane attribute change 500m ahead (nominal) (normal lane → widened lane); and the vehicle runs along the current target lane until the vehicle enters the widening area, the number of lanes is changed, and the target lane is selected again. Referring to fig. 4(b), the current lane number is 2, the target lane is the rightmost lane, the vehicle travels along the current road, the attribute of the lane number of the vehicle is changed from 2 to 3, and the target lane is C2. Taking the original lane number as 2 as an example, the lane number is changed to 3 after the left side is widened and the right side is widened, the lane number is changed to 4 after the two sides are widened, and the target lane is selected again according to the changed lane number according to the above mentioned principle.

3. Bifurcation of main road

Fig. 4(c) is a schematic diagram of a main road bifurcation, also called a split roadbed, in an embodiment of the invention, wherein the road points in two different directions, generally accompanied by a change in the attribute of the number of lanes.

In the embodiment of the invention, the target lane selection principle under the situation of main road bifurcation comprises the following steps: advance 500m (nominal) change lane attribute (normal lane → main lane bifurcation); taking the 4-lane target direction as the right (similar to the left scenario), when the target side is 1 lane ahead, the lane is the target lane, when the target side is 2 lanes ahead, the right lane is the target lane, and when the target side is 3 lanes ahead, the middle lane is the target lane.

Referring to fig. 4(C), there are two lanes in front of the right side of the bifurcation, according to the above principle, the vehicle should go to the right lane after passing the bifurcation point, and before the lane number jump, the vehicle lane is set to the rightmost C4 lane, and the vehicle lane is kept to enter the direction side of the bifurcation target, i.e. to run in the C2 lane.

4. Tunnel

In the embodiment of the present invention, the selection principle of the tunnel target lane is the same as or similar to the normal driving lane corresponding to fig. 3, and is not repeated here.

Three, ramp scene

Wherein the ramp scene comprises a conventional ramp, a ramp narrowing, a ramp widening, a ramp bifurcation and/or a ramp intersection. It should be noted that, the target lane selection for the narrowing of the ramp, the widening of the ramp and the bifurcation of the ramp is the same as or similar to the target lane selection for the narrowing of the main lane, the widening of the main lane and the bifurcation of the main lane corresponding to the above fig. 4(a) -4 (c), and the difference is mainly that the main lane is changed into the ramp, and those skilled in the art can understand the target lane selection in combination with the road condition of the ramp, so the details are not described herein.

For a conventional ramp, the target lane selects the rightmost lane when the vehicle is driving on the ramp. And when the rightmost lane is abnormal, selecting the target lane close to the rightmost lane, wherein the target lane selection principle follows the principle of leaning to the right as much as possible.

For ramp intersection, or ramp combination, it means that ramps in different directions are combined into one. Under the situation, the vehicle runs on the ramp, the lane attribute (common ramp → junction ramp) is changed by 500m (calibration value) in advance, the ramp number attribute is changed, the vehicle runs along the current target lane and merges into the junction ramp, and after the lane number attribute is changed, the target lane is reselected according to the new number of lanes.

In the embodiment of the present invention, the target lane management module 110 limits different speeds of different lanes on the highway according to laws and regulations, selects the target lanes of the main lane and the ramp as the priority driving lanes for enabling the vehicle to travel at a faster speed according to a preset direction, and since the priority driving lanes are planned, the collision danger caused by a larger lateral deviation of the vehicle due to inaccurate map positioning is avoided, it is ensured that the vehicle can travel at a faster speed on the premise of safety, and the target lane selection planning mode conforms to the driving habits of people.

With continued reference to fig. 2, in a preferred embodiment, the lane abnormality management module 120 includes: a lane abnormality recognition sub-module 121 configured to analyze the road characteristic information to screen out a static obstacle target of a road ahead of the autonomous vehicle, and recognize whether a lane is abnormal based on the static obstacle target; and an obstacle avoidance submodule 122 for guiding the autonomous vehicle to avoid an obstacle when the lane is abnormal.

The lane abnormality recognition sub-module 121 is further configured to provide the target lane management module 110 with information of the recognized lane abnormality, so that the target lane management module 110 selects the target lane in combination with the lane abnormality.

Further, in combination with the above, when the vehicle travels on a non-tunnel entrance road section, the lane abnormality recognition sub-module 121 should include three parts, namely static obstacle target selection, lane abnormality judgment, and lane abnormality recognition for multiple static obstacles in the own lane.

1. Static obstacle target selection

In the embodiment of the invention, the selection principle of the static obstacle target comprises the following steps: road characteristic information (the number of lanes, the width of each lane and the like), road accessory information and environmental object target information of a current driving road section of the vehicle are extracted. In each lane, with an environmental object target closest to the vehicle as a reference, static obstacle targets (also called static obstacles) in each lane within a certain range are screened, wherein the static obstacle targets mainly comprise static object targets such as road cones, road blocks and fault vehicles, and also comprise dynamic targets when the target speed is less than a certain threshold value. Further, the information on the lateral/longitudinal distance of each static obstacle target to the host vehicle may be extracted on a lane-by-lane basis.

2. Principle for judging lane abnormality

Fig. 5 is an exemplary diagram of lane abnormality determination according to an embodiment of the present invention, which takes the lane where the vehicle is located as an example, and the principle of abnormality determination of other lanes is similar to this. As shown in FIG. 5, due to the effects of the static target 1 and the static target 2, the travelable area of the autonomous vehicle within the range D2 is as shown by ABCE, and the travelable width D is the point distance value l of the static target 1 within the range D2 closest to the transverse distance of the central line of the lane₁(plus left), the point distance value l closest to the lateral distance between the static object 2 and the central line of the lane₂(negative right) sum of absolute values (l)₁The transverse distance of the central line of the lane is-E point transverse distance; l₂Is the transverse distance of the central line of the lane-the transverse distance of the point C). If there is no lateral closest point, i.e. no static obstacle object in front, then l₁And l₂Take a fixed value (a certain calibration amount, which may be referred to as TBD). And when the driving width D of the lane is less than the TBD, the lane is considered to be abnormal, and the vehicle cannot pass through.

When the vehicle runs at the tunnel entrance, the traffic light condition of each lane at the tunnel entrance is also needed to be identified, and when the lane is a red light, the lane is set as an abnormal lane (from the entrance to the exit, the traffic light condition is abnormal); and the system re-identifies whether the road state is the tunnel or not until the automatic driving vehicle exits the tunnel, and re-identifies the traffic lights.

3. Lane anomaly identification for multiple static obstacles in own lane

Fig. 6 is an exemplary diagram of lane abnormality recognition of the lane multi-static obstacle in the embodiment of the present invention. Referring to fig. 6, there is a distance D3 between two of the

static objects

1, 2 in the lane, and the travelable width D is (abs (l)₁)+abs(l₂) Greater than a set threshold TBD (preferably 2.8m), the vehicle can safely pass through the static target 1; when D3 is greater than a set threshold TBD1 (the value of TBD1 is linearly related to the current vehicle speed by K × V, and the minimum value is 25m, where K is a proportionality coefficient and V is the vehicle speed), the travelable width D4 is (abs (l is the vehicle speed) (₁’)+abs(l₂') is greater than a set threshold TBD (preferably 2.8m), the vehicle can safely pass the static target 2. Therefore, the vehicle can safely pass through the lane abnormality.

Further, the obstacle avoidance sub-module 122, configured to guide the autonomous vehicle to avoid an obstacle when the lane is abnormal, may mainly include the following steps: determining an obstacle avoidance target according to the static obstacle target and the dynamic environment object target existing in a set area, and determining the static characteristic and the dynamic characteristic of the obstacle avoidance target relative to the automatic driving vehicle; establishing an obstacle avoidance area adaptive to road characteristics based on the static characteristics and the dynamic characteristics of the obstacle avoidance target; judging the trafficability of the obstacle avoidance area based on the static characteristic and the dynamic characteristic of the obstacle avoidance target; performing collision risk evaluation on a related environment object target when the automatic driving vehicle normally changes lanes, and determining lane changing feasibility according to a collision risk evaluation result; and controlling the automatic driving vehicle to change the lane or drive around the obstacle avoidance target in the current driving lane according to the lane changing feasibility and the trafficability of the obstacle avoidance area.

For example, the functions implemented by the obstacle avoidance sub-module 122 mainly include the following parts.

1. Obstacle avoidance target selection

The obstacle avoidance target comprises a static obstacle and a dynamic obstacle. The selection principle takes the object closest to the autonomous vehicle in the area as a reference. Static barriers are mainly static object targets such as road cones, road barriers and fault vehicles, and the obstacle avoidance targets comprise: firstly, a static object target in a region right ahead; secondly, a static object target in a left front area; thirdly, a static object target in the right front area; fourthly, a static object target in the left side area; the static object target in the right side area. The dynamic barrier is mainly a moving object target, and the obstacle avoidance target comprises: firstly, a dynamic object target with a front area lower than the speed of an automatic driving vehicle; a dynamic object target with a left front area lower than the speed of the automatic driving vehicle; a dynamic object target with a right front area lower than the speed of the autonomous vehicle; fourthly, the dynamic object target in the left side area; the dynamic object target in the right side area; sixthly, the dynamic object target with the left rear area higher than the speed of the automatic driving vehicle; the rear right region is higher than the dynamic object target of the autonomous vehicle speed.

2. Obstacle avoidance area establishment

The traditional obstacle avoidance area establishing method generally establishes a sector area, takes 1/2 with a sector angle as a deflection gesture, avoids obstacles successfully and then follows the obstacles, and is suitable for low-speed automatic driving vehicles such as urban/rural roads without lane lines. In the embodiment of the invention, the obstacle avoidance area is established by considering road characteristics besides the obstacle avoidance target, so that the obstacle avoidance behavior of automatic driving meets the behavior requirements of a highway on a driver (for example, the vehicle does not press lines except overtaking, does not draw dragon, and has not neglected speed and the like when running in the road).

Fig. 7 is a schematic diagram of obstacle avoidance performed by a vehicle in an embodiment of the present invention, wherein an ABCD forms an area where an obstacle avoidance area is formed, the arc lengths of the arc AC and the arc BD are equal to 200m, the curvature is equal to the curvature of the lane line L2, that is, the arc AC and the arc BD are parallel to the road, and the size of the area is determined by obstacle avoidance targets G1 and G2.

The target G1 is a dynamic object target in a front area, the relationship between G1 and the vehicle comprises outer contour points, namely a transverse closest point G11 and a longitudinal closest point G12, a curve s1 parallel to the road is constructed through G11, the intersection point of the longitudinal closest point G12 to a perpendicular line of the curve s1 is G13, G13 is used as the outer contour point of the target G1 for obstacle avoidance, and a BD curve is generated by considering the obstacle avoidance safety adding d2 to a safety distance of 0.3 m.

The target G2 is a static object target (road barricade) in the left front area, the finally selected outer contour point of G2 is G21, and an AC curve is generated by considering the safe distance d1 of 0.1 meter for avoiding obstacles.

3. Obstacle avoidance area trafficability judgment

After the obstacle avoidance area ABCD is generated according to the part 2, whether the vehicle can pass through the area needs to be judged, and the vehicle width W + the safety distance d3 is the most feasible condition, when the obstacle avoidance area width is larger than (W + d3), the automatic driving vehicle can avoid the obstacle; otherwise, the automatic driving vehicle judges other obstacle avoidance areas again (for example, whether the obstacle avoidance area can be generated on the right side or not).

In a preferred embodiment, the obstacle avoidance sub-module 122 is configured to control the autonomous vehicle to change lanes or to drive around the obstacle avoidance target in the current driving lane according to the lane change feasibility and the feasibility of the obstacle avoidance area, and includes: if the lane changing is feasible, controlling the automatic driving vehicle to change the lane, otherwise, judging the trafficability of the obstacle avoidance area, and if the obstacle avoidance area is trafficable, determining that the automatic driving vehicle drives around the obstacle avoidance target in the current driving lane. Specifically, in combination with the actual situation, the obstacle avoidance sub-module 122 is configured to identify whether a lane change is required, including three aspects of lane change intention generation (i.e. collision risk assessment), lane change direction determination and lane change feasibility determination,

1. lane change intention generation

When the vehicle normally runs and a non-static object lower than the highest speed limit of the vehicle appears in front of a detection area, the automatic driving vehicle determines whether the vehicle needs to change the lane according to the relative distance and speed balance between the vehicle and a front vehicle, and the lane changing frequency of the automatic driving vehicle is reduced.

Assuming that the lane change intention expectation factor threshold is set to η, the automatic driving vehicle speed V _ auto, the target vehicle speed V _ trg, the relative distance Dis _ rely between the automatic driving vehicle and the target vehicle, and the expected safe driving distance K × V _ auto of the automatic driving vehicle, where K preferably takes 0.8.

The lane change intention expectation factor β is K1 (V _ auto/V _ trg) + K2 (Dis _ rely/K V _ auto), wherein K1+ K2 is 1, which is satisfied by the autonomous vehicle intention when the lane change intention expectation factor β is less than η.

When the automatic driving vehicle normally runs and a static object appears in front of a detection area, the automatic driving vehicle should change lanes in advance to avoid collision with the static object in front.

Assuming that the lane change intention expectation factor threshold is set to be η s, the speed of the autonomous vehicle V _ auto, the relative distance between the autonomous vehicle and the static obstacle Dis _ s, and the expected safe driving distance K × V _ auto of the autonomous vehicle, where K takes 1 priority.

The lane change intention factor β s is K1 (Dis _ s/K V _ auto), where K1 preferably takes the value 1, and the automatic driving vehicle intention is satisfied when the lane change intention factor β s is smaller than η s.

2. Lane change direction determination

The lane changing direction judgment of the automatic driving vehicle needs to meet the following conditions:

a) the front area (left front, right front) has a non-stationary object target.

b) The difference between the target speed of the object in the left front area or the right front area and the speed of the vehicle in front of the lane is larger than a speed threshold value delta V, and the delta V is preferably 5 km/h.

c) The distance between the vehicle and the front left vehicle or the front right vehicle is greater than the expected safe driving distance K3V auto, wherein K3 preferably takes 0.6.

d) The vehicle is not arranged on the right side of the vehicle.

e) Performing collision risk assessment according to the relationship between the environmental vehicle and the autonomous vehicle in the rear area (left rear and right rear), and performing collision risk assessment according to a target TTC value (TTC is the time for the autonomous vehicle to collide with the front vehicle, TTC is the relative speed/relative distance; relative speed-vehicle speed ahead) determines the feasibility of lane change of the automatic driving vehicle, and preferentially recommends that the TTC value is greater than 2.

f) The relative distance between the ambient vehicle and the autonomous vehicle in the rear zone (left rear, right rear) is greater than the expected safe driving distance K4V auto of the autonomous vehicle, where K4 preferably takes 0.3.

g) And the left lane of the automatic driving vehicle is preferentially changed in the condition judgment, namely, the left lane is preferentially selected as the target lane when the left front area and the right front area simultaneously meet the conditions from a to f.

The autonomous vehicle determines the lane change target lane according to the above conditions a) to g).

3. Lane change feasibility assessment

Vehicles must comply with road traffic regulations, such as: broken solid lines, speed limit, light, horn, traffic lights, no head drop, etc.

The obstacle avoidance submodule 122 of the embodiment of the invention provides an obstacle avoidance method suitable for a high-speed running and structured road of a vehicle, which can avoid vehicle collision caused by blind areas in manual driving, can improve the running efficiency of the vehicle and reduce the workload of a driver by a lane changing function, has wide application range, and can be suitable for an automatic driving system under a curved road with larger curvature and a straight road, in particular to an automatic driving system under a structured road.

Here, the lane abnormality management module 120 according to the embodiment of the present invention can recognize a lane condition, and actively guide a vehicle to avoid an obstacle in advance or gradually approach to an emergency lane or leave an expressway, so as to avoid a collision risk of the vehicle.

It should be noted that the decision system and the environmental perception system of the vehicle and their respective functional modules may be understood as a control unit on the vehicle, and the hardware arrangement of the autonomous vehicle according to the embodiment of the present invention will be described based on this understanding. Fig. 8 is a schematic diagram of the hardware arrangement of an autonomous vehicle according to an embodiment of the present invention, wherein a transverse decision system according to the above-described embodiment is included in a decision system of the autonomous vehicle.

As shown in fig. 8, the control unit 1, the control unit 2, and the control unit 4 constitute an environment sensing system, and the control unit 3 constitutes a lateral decision making system of an embodiment of the present invention, which is part of a decision making system of a vehicle. The control unit 1 provides accurate position information for the automatic driving vehicle, preferably adopts high-precision GPS + IMU equipment, and has transverse positioning deviation within 10cm and longitudinal positioning deviation within 30 cm. The control unit 2 is used for storing and outputting high-precision lane lines, lane numbers, lane widths and other information within the range of 200m from front to back of the automatic driving vehicle, preferentially uses the storage space more than 50G, and has the processing memory more than 1G of hardware equipment. The control unit 4 is used for detecting and extracting object targets appearing in a range of 360 degrees around the automatic driving vehicle, and preferably selects all-weather sensor detection equipment to avoid object target false detection, object target missing detection and the like caused by rain, snow, fog, illumination and the like. The control unit 4 is not limited to the current installation position and the current number, a plurality of radar sensors (laser radar or millimeter wave radar equipment and the like) and visual sensors are arranged around the vehicle body for improving the object detection accuracy, and the object target detection accuracy and stability are improved through equipment redundancy.

The control unit 2 obtains accurate position information of the automatic driving vehicle provided by the control unit 1, and outputs high-precision map data within a range of 200m in front and back of the automatic driving vehicle in real time after processing and calculation, and the method comprises the following steps: the lane line discrete point longitude and latitude (the longitude and latitude takes the geocentric as the origin), the discrete point course angle (the true north direction is 0 degrees clockwise), the lane line type, the lane width, the lane number, the road boundary and other information, the control unit 3 receives lane line offline data in an Ethernet mode, converts the lane line offline data into a plane vehicle coordinate system through coordinates, provides road characteristic information required in the vehicle lane changing process, the control unit 4 simultaneously transmits object target information in a detection area to the control unit 3 in a CAN communication mode, and the control unit 3 executes the functions of the transverse decision system.

It can be seen that the horizontal decision making system of the embodiment of the present invention is easily implemented by hardware.

In summary, the transverse decision system according to the embodiment of the present invention can evaluate the abnormal situations of the target lane and the lane, and accordingly make a transverse decision for lane keeping, lane changing or abnormal lane changing according with the road characteristics, so that the vehicle control system can perform adaptive transverse control based on the transverse decision, thereby ensuring the driving safety of the vehicle.

Fig. 9 is a flowchart illustrating a lateral decision making method for an autonomous vehicle according to an embodiment of the present invention, which is based on the same inventive concept as the lateral decision making system described above. As shown in fig. 9, the method for determining a lateral decision of an autonomous vehicle may include the following steps S100 and S200:

and S100, evaluating a target lane and lane abnormity conditions required by the transverse decision of the automatic driving vehicle according to the road characteristic information, a preselected target line and an environmental object target.

Preferably, this step S100 in turn comprises the following sub-steps:

and step S110, selecting a target lane of the automatic driving vehicle according to the road characteristic information.

The selection principle of the target lane comprises a principle of following a road scene, a principle of following lane attributes, a principle of not selecting an abnormal lane and a principle of selecting an adjacent lane and sequentially selecting the adjacent lane to the right when the lane is abnormal.

More preferably, the step S110 specifically includes: when the automatic driving vehicle runs in a main road conventional scene, selecting a target lane according to the selection principle, wherein the main road conventional scene comprises an acceleration lane, a normal running lane and a deceleration lane; when the automatic driving vehicle runs in a main road special scene, selecting a target lane according to the change of the lane number attribute of a front road relative to a current road, wherein the main road special scene comprises main road narrowing, main road widening, main road branching and/or a tunnel; and when the automatic driving vehicle runs on a ramp scene, selecting a target lane according to the change of the lane number attribute of the front road relative to the current road, wherein the ramp scene comprises a conventional ramp, a ramp narrowing, a ramp widening, a ramp branching and/or a ramp intersection.

And step S120, identifying an abnormal lane according to the road characteristic information, and providing an obstacle avoidance strategy aiming at the abnormal lane.

More preferably, the step S120 further specifically includes: analyzing road characteristic information to screen out a static obstacle target of a road ahead of the autonomous vehicle, and identifying whether a lane is abnormal based on the static obstacle target; and guiding the automatic driving vehicle to avoid the obstacle when the lane is abnormal.

Further, the guiding the autonomous vehicle to avoid an obstacle comprises: determining an obstacle avoidance target according to the static obstacle target and the dynamic environment object target existing in a set area, and determining the static characteristic and the dynamic characteristic of the obstacle avoidance target relative to the automatic driving vehicle; establishing an obstacle avoidance area adaptive to road characteristics based on the static characteristics and the dynamic characteristics of the obstacle avoidance target; judging the trafficability of the obstacle avoidance area based on the static characteristic and the dynamic characteristic of the obstacle avoidance target; performing collision risk evaluation on a related environment object target when the automatic driving vehicle normally changes lanes, and determining lane changing feasibility according to a collision risk evaluation result; and controlling the automatic driving vehicle to change the lane or drive around the obstacle avoidance target in the current driving lane according to the lane changing feasibility and the trafficability of the obstacle avoidance area.

Further, the controlling the autonomous vehicle to change lanes or to drive around the obstacle avoidance target in the current driving lane according to the lane change feasibility and the trafficability of the obstacle avoidance area includes: if the lane changing is feasible, controlling the automatic driving vehicle to change the lane, otherwise, judging the trafficability of the obstacle avoidance area, and if the obstacle avoidance area is trafficable, determining that the automatic driving vehicle drives around the obstacle avoidance target in the current driving lane.

And step S200, judging and outputting the expected transverse behavior of the automatic driving vehicle according to the evaluated target lane and lane abnormal conditions by combining the road characteristic information.

It should be noted that the transverse decision determining method for an autonomous vehicle according to the embodiment of the present invention has the same details and effects as those of the transverse decision system for an autonomous vehicle according to the embodiment of the present invention, and thus, no further description is provided herein.

Another embodiment of the present invention also provides a machine-readable storage medium having stored thereon instructions for causing a machine to perform the above-described lateral decision making method. The machine-readable storage medium includes, but is not limited to, Phase Change Random Access Memory (PRAM, also known as RCM/PCRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory (Flash Memory) or other Memory technology, compact disc read only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices, and various other media capable of storing program code.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A lateral decision system for an autonomous vehicle, comprising:

the evaluation unit is used for evaluating a target lane and lane abnormal conditions required by the transverse decision of the automatic driving vehicle according to road characteristic information, a preselected target line and an environmental object target, wherein the road characteristic information comprises a road type, road characteristic points and lane attributes, and the lane attributes comprise lane characteristic point attributes and lane number attributes; and

a judging unit, configured to, in combination with the road characteristic information, judge and output an expected lateral behavior of the autonomous vehicle according to the target lane and the lane abnormality evaluated by the evaluating unit, where the expected lateral behavior includes any one of lane keeping, lane changing, and abnormal lane changing;

wherein the evaluation unit comprises:

a target lane management module for selecting a target lane of the autonomous vehicle according to the road characteristic information; and

the lane abnormity management module is used for identifying an abnormal lane according to the road characteristic information and providing an obstacle avoidance strategy aiming at the abnormal lane;

wherein the lane abnormality management module includes:

a lane abnormality recognition submodule for analyzing the road characteristic information to screen out a static obstacle target of a road ahead of the autonomous vehicle, and recognizing whether a lane is abnormal based on the static obstacle target; and

keep away barrier submodule for guide when the lane is unusual the autonomous vehicle keeps away the barrier, include:

determining an obstacle avoidance target according to the static obstacle target and the dynamic environment object target existing in a set area, and determining the static characteristic and the dynamic characteristic of the obstacle avoidance target relative to the automatic driving vehicle;

establishing an obstacle avoidance area adaptive to road characteristics based on the static characteristics and the dynamic characteristics of the obstacle avoidance target;

judging the trafficability of the obstacle avoidance area based on the static characteristic and the dynamic characteristic of the obstacle avoidance target;

performing collision risk evaluation on a related environment object target when the automatic driving vehicle normally changes lanes, and determining lane changing feasibility according to a collision risk evaluation result; and

and controlling the automatic driving vehicle to change the lane or drive around the obstacle avoidance target in the current driving lane according to the lane changing feasibility and the trafficability of the obstacle avoidance area.

2. The lateral decision system of an autonomous vehicle as claimed in claim 1 wherein the selection criteria of the target lane include the criteria of following a road scene, the criteria of following lane attributes, the criteria of not selecting an abnormal lane and the criteria of selecting an adjacent lane and selecting right in turn when a lane is abnormal.

3. The lateral decision system of an autonomous vehicle as claimed in claim 2, characterized in that the target lane management module comprises:

a main lane target lane selection submodule for selecting a target lane according to the selection principle when the autonomous vehicle is running in a main lane conventional scene, wherein the main lane conventional scene comprises an acceleration lane, a normal running lane and a deceleration lane; and selecting a target lane according to a change in lane number attribute of a road ahead with respect to a current road when the autonomous vehicle is traveling in a main lane special scene, wherein the main lane special scene includes a main lane narrowing, a main lane widening, a main lane branching and/or a tunnel; and

and the ramp target lane selection submodule is used for selecting a target lane according to the change of the lane number attribute of the road ahead relative to the current road when the automatic driving vehicle runs in a ramp scene, wherein the ramp scene comprises a conventional ramp, a ramp narrowing, a ramp widening, a ramp branching and/or a ramp intersection.

4. The lateral decision-making system of an autonomous vehicle as claimed in claim 1, wherein the obstacle avoidance sub-module is configured to control the autonomous vehicle to change lanes or to drive around the obstacle avoidance target in a current driving lane according to the lane change feasibility and the feasibility of the obstacle avoidance area, and comprises:

if the lane changing is feasible, controlling the automatic driving vehicle to change the lane, otherwise, judging the trafficability of the obstacle avoidance area, and if the obstacle avoidance area is trafficable, determining that the automatic driving vehicle drives around the obstacle avoidance target in the current driving lane.

5. A lateral decision determination method for an autonomous vehicle, the lateral decision determination method for an autonomous vehicle comprising:

evaluating a target lane and lane abnormal conditions required by the automatic driving vehicle for transverse decision according to road characteristic information, a preselected target line and an environmental object target, wherein the road characteristic information comprises a road type, road characteristic points and lane attributes, and the lane attributes comprise lane characteristic point attributes and lane number attributes; and

determining and outputting an expected lateral behavior of the autonomous vehicle according to the evaluated target lane and lane abnormality in combination with the road characteristic information, wherein the expected lateral behavior comprises any one of lane keeping, lane changing and abnormal lane changing;

wherein the evaluating target lane and lane anomalies required for lateral decision-making by the autonomous vehicle comprises:

selecting a target lane of the autonomous vehicle according to the road characteristic information; and

identifying an abnormal lane according to the road characteristic information, and providing an obstacle avoidance strategy for the abnormal lane, wherein the obstacle avoidance strategy comprises the following steps: analyzing road characteristic information to screen out a static obstacle target of a road ahead of the autonomous vehicle, and identifying whether a lane is abnormal based on the static obstacle target; when the lane is abnormal, guiding the automatic driving vehicle to avoid the obstacle;

wherein the guiding the autonomous vehicle to avoid obstacles comprises:

6. The lateral decision determination method of an autonomous vehicle according to claim 5,

the selection principle of the target lane comprises a principle of following a road scene, a principle of following lane attributes, a principle of not selecting an abnormal lane and a principle of selecting an adjacent lane and sequentially selecting the adjacent lane on the right when the lane is abnormal.

7. The method of lateral decision determination for an autonomous vehicle as claimed in claim 6, wherein the selecting a target lane of the autonomous vehicle as a function of the road characteristic information comprises:

when the automatic driving vehicle runs in a main road conventional scene, selecting a target lane according to the selection principle, wherein the main road conventional scene comprises an acceleration lane, a normal running lane and a deceleration lane;

when the automatic driving vehicle runs in a main road special scene, selecting a target lane according to the change of the lane number attribute of a front road relative to a current road, wherein the main road special scene comprises main road narrowing, main road widening, main road branching and/or a tunnel; and

when the automatic driving vehicle runs on a ramp scene, selecting a target lane according to the change of the lane number attribute of a front road relative to a current road, wherein the ramp scene comprises a conventional ramp, a ramp narrowing, a ramp widening, a ramp branching and/or a ramp intersection.

8. The method as claimed in claim 5, wherein the controlling the autonomous vehicle to change lanes or to drive around the obstacle avoidance target in a current driving lane according to the lane change feasibility and the trafficability of the obstacle avoidance area comprises:

9. A machine-readable storage medium having instructions stored thereon for causing a machine to perform the lateral decision making method of an autonomous vehicle of any of claims 5 to 8.