An automatic driving vehicle following target decision method in a laneless environment

Abstract

The application discloses a kind of automatic driving vehicle following target decision-making methods under no-lane environment, comprising:1, in the state information and surrounding environment information of automatic driving vehicle under no-lane environment;2, determine observation area and risk area;3, calculate the lateral and longitudinal distance of each preceding vehicle and the vehicle;4, determine the following target, and construct the following model;5, determine the acceleration of the vehicle using the following model.The application can solve the behavior planning problem of automatic driving vehicle in the environment lacking clear lane marking without relying on lane marking and parameters, thereby improving the traffic efficiency of road no-lane traffic and reducing vehicle delay under the premise of ensuring safe driving of vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, more specifically, it relates to a car-following target decision-making method for autonomous vehicles in a lane-free environment. BACKGROUND

[0002] With the rapid development of autonomous driving technology, autonomous vehicles have been able to improve traffic safety and efficiency, reduce energy consumption and emissions. These technologies have achieved intelligent speed control by precisely monitoring the position and speed of the vehicle in front, bringing a smoother and more comfortable travel experience for commuters. However, current autonomous driving technology relies heavily on lane markings and parameters, which are the basis for many car-following and lane-changing algorithms. In lane-free environments and future lane-free strategies, this dependency has become a major challenge for autonomous vehicle behavior planning. In environments without clear lane markings, such as poorly maintained urban roads and highways, vehicles often cannot form an orderly queue, and traffic flow is variable, making it difficult for autonomous vehicles to make appropriate driving decisions. SUMMARY

[0003] The present application overcomes the deficiencies of the prior art and proposes a car-following target decision-making method for autonomous vehicles in a lane-free environment, in order to adapt to the particularity of lane-free environments and improve the traffic efficiency of general urban road lane-free traffic while ensuring safe vehicle operation, thereby ensuring that autonomous vehicles can safely and efficiently navigate current and future road conditions.

[0004] To achieve the above-mentioned application purposes, the present application adopts the following technical solutions:

[0005] The car-following target decision-making method for autonomous vehicles in a lane-free environment is characterized by being applied in a lane-marking-free road scenario; the forward direction of the autonomous vehicle is taken as the positive direction of the x-axis, i.e. the longitudinal direction, the longitudinal coordinate of the center of mass of the autonomous vehicle is set to zero, and the lateral coordinate of the road centerline is set to zero. The car-following target decision-making method for autonomous vehicles includes the following steps:

[0006] Step 1, obtain the driving behavior state information and surrounding environment information of a certain autonomous vehicle car at time t, the driving behavior state information includes: the vehicle speed , vehicle width , vehicle length and position coordinates of the autonomous vehicle car, the surrounding environment information includes: the speed, vehicle width, vehicle length and position coordinates of all preceding vehicles of the autonomous vehicle car;

[0007] Step 2, determine the observation zone DZ and the risk zone RZ;

[0008] Step 3, determine whether there is a preceding vehicle in the risk zone RZ; if not, execute step 4, if yes, execute step 5;

[0009] Step 4, calculate the acceleration of the autonomous vehicle car at time t according to the Free-IDM method shown in formula (3) Then, execute step 6;

[0010] (3)

[0011] In formula (3), is the maximum acceleration; is the desired speed;

[0012] Step 5, determine the car-following target of the autonomous vehicle car and calculate the acceleration at time t ;

[0013] Step 6, assign t+1 to t, and then return to step 1 for sequential execution until the autonomous vehicle car ends driving.

[0014] The automatic driving vehicle car-following target decision method in a lane-free environment has the characteristics that the step 2 includes:

[0015] Step 2.1, calculate the observation zone DZ of the autonomous vehicle car according to formula (1);

[0016] (1)

[0017] In formula (1), is the longitudinal range of the observation zone DZ; is the desired time interval; is the comfortable acceleration; is the lateral range of the observation zone DZ; is the lateral coordinate of the right boundary of the road; is the lateral coordinate of the left boundary of the road;

[0018] Step 2.2, number all the preceding vehicles of the observation zone DZ of the autonomous vehicle car, and record any jth preceding vehicle as ;

[0019] Step 2.3, calculate the risk zone RZ of the autonomous vehicle car according to formula (2);

[0020] (2)

[0021] In formula (2), is the longitudinal range of the risk zone RZ, is the lateral range of the risk zone RZ, is a lateral position coordinate of the autonomous vehicle car at time t, is a front vehicle is a width of the front vehicle, is a safety factor; is a minimum lateral safety gap.

[0022] Further, the step 5 comprises:

[0023] Step 5.1, determining the front vehicle with the minimum longitudinal distance to the autonomous vehicle car in the risk zone RZ by using formula (4) , and taking as the following target of the autonomous vehicle car;

[0024] (4)

[0025] In formula (4), is a longitudinal position coordinate of the front vehicle at time t, is a lateral position coordinate of the front vehicle at time t;

[0026] Step 5.2, calculating the acceleration of the autonomous vehicle car at time t when following the front vehicle by using SIDM method shown in formula (5) :

[0027] (5)

[0028] In formula (5), is an acceleration intensity, is a longitudinal position of the front vehicle at time t, is a speed of the front vehicle at time t, is a cautious following distance, is a safe stopping distance; is an expected headway, and has:

[0029] (6).

[0030] The electronic device comprises a memory and a processor, and the feature lies in that the memory is used to store a program supporting the processor to execute the cooperative control method, and the processor is configured to execute the program stored in the memory.

[0031] The computer readable storage medium stores a computer program, and the feature lies in that the computer program is run by the processor to execute the steps of the following target decision method.

[0032] Compared with the prior art, the beneficial technical effects of the present application are embodied in:

[0033] 1、The present application can no longer rely on lane markings and parameters, and is specifically designed for general road lane-free scenes, solving the behavior planning problem of autonomous vehicles in environments lacking clear lane markings, adapting to lane-free environments, and overcoming the limitations of existing technologies that rely heavily on lane markings and parameters, making it difficult to make appropriate driving decisions in lane-free environments.

[0034] 2、The present application improves the traffic efficiency of general road lane-free traffic under the premise of ensuring safe vehicle travel, and reduces vehicle delays, helping to improve traffic conditions in lane-free environments, providing more effective driving strategies for autonomous vehicles in complex urban road lane-free scenes, and improving overall traffic operation effectiveness.

[0035] 3、The present application determines the following target and vehicle acceleration by considering the horizontal and vertical distances, which helps to improve the driving safety of autonomous vehicles in lane-free environments and reduce the risk of collision, especially in lane-free scenes where the direction of vehicle travel is not clear and the traffic order is relatively chaotic. This multi-factor consideration decision-making method can more effectively ensure the safety of vehicles and pedestrians.

[0036] 4、The present application provides a general method for following target decision of autonomous vehicles in lane-free environments, which is not only suitable for current general road lane-free scenes, but also has certain universality in principle and framework, which can provide reference and expansion basis for future similar lane-free scenes or application scenarios after changes in traffic rules, and help to promote the application and development of autonomous driving technology in more complex environments. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is the overall flowchart of the present application;

[0038] Figure 2 is the decision flowchart of the present application;

[0039] Figure 3 is the scene schematic diagram of the present application. DETAILED DESCRIPTION

[0040] In this embodiment, a following target decision method for autonomous vehicles in lane-free environments is shown in Figure 3 The following target decision method for autonomous vehicles involves a lane-free marking road scene, including environments with no lane markings or unclear lane markings, such as poorly maintained urban roads and highways, etc. Figure 3As shown in part (a) of FIG. 1, a plane rectangular coordinate system is established, with the advancing direction of the autonomous vehicle as the positive direction of the x-axis, i.e., the longitudinal direction, and the positive direction of the y-axis being perpendicular to the road boundary line, pointing to the left boundary. The longitudinal coordinate of the center of mass of the autonomous vehicle is set to zero, and the transverse coordinate of the road centerline position is set to zero, as shown in part (b) of FIG. 1. The target following decision method of the autonomous vehicle includes the following steps: Figure 1 and Figure 2 As shown in part (a) of FIG. 1, a plane rectangular coordinate system is established, with the advancing direction of the autonomous vehicle as the positive direction of the x-axis, i.e., the longitudinal direction, and the positive direction of the y-axis being perpendicular to the road boundary line, pointing to the left boundary. The longitudinal coordinate of the center of mass of the autonomous vehicle is set to zero, and the transverse coordinate of the road centerline position is set to zero, as shown in part (b) of FIG. 1. The target following decision method of the autonomous vehicle includes the following steps:

[0041] Step 1, obtaining the driving behavior state information and the surrounding environment information of a certain autonomous vehicle car at time t, the driving behavior state information including the vehicle speed , the vehicle width , the vehicle length and the position coordinates of the vehicle, and the surrounding environment information including the speed, vehicle width, vehicle length and position coordinates of all preceding vehicles of the autonomous vehicle car, and the road boundary position.

[0042] Step 2, determining the observation zone and the risk zone;

[0043] Step 2.1, calculating the observation zone DZ of the autonomous vehicle car according to formula (1), i.e., the observation range of the autonomous vehicle car;

[0044] (1)

[0045] In formula (1), is the longitudinal range of the observation zone DZ; is the expected speed; is the expected time interval; is the comfortable acceleration; is the transverse range of the observation zone DZ; is the transverse coordinate of the right road boundary; is the transverse coordinate of the left road boundary.

[0046] Step 2.2, numbering all preceding vehicles of the observation zone DZ of the autonomous vehicle car, and recording any jth preceding vehicle as ;

[0047] Step 2.3, calculating the risk zone RZ of the autonomous vehicle car according to formula (2), when the intelligent vehicle position is close to the road boundary, taking the risk zone boundary of the road boundary close to this side, as shown in part (b) of FIG. 1; Figure 3

[0048] (2)

[0049] In formula (2), is the longitudinal range of the risk zone RZ, ​is the lateral range of the risk zone RZ, is the lateral position coordinate of the autonomous vehicle car at time t, is the front vehicle width, is the safety factor; is the minimum lateral safety gap.

[0050] Step 3, determine whether there is a front vehicle in the risk zone RZ; if not, as shown in part (c) of the above, there is no front vehicle in the risk zone, the autonomous vehicle car will not be affected by the front vehicle in the risk zone, and can freely travel, then execute step 4, if yes, there is a front vehicle in the risk zone, then execute step 5. Figure 3

[0051] Step 4, calculate the acceleration of the autonomous vehicle car at time t according to formula (3) Free-IDM , then execute step 6;

[0052] (3)

[0053] In formula (3), is the maximum acceleration.

[0054] Step 5, determine the following target of the autonomous vehicle car, and calculate the acceleration at time t ;

[0055] Step 5.1, determine the front vehicle in the risk zone RZ with the smallest longitudinal distance from the autonomous vehicle car using formula (4) , the collision risk of the autonomous vehicle car with this front vehicle is larger, and as the following target of the autonomous vehicle car;

[0056] (4)

[0057] In formula (4), is the longitudinal position coordinate of the front vehicle at time t, is the lateral position coordinate of the front vehicle at time t.

[0058] Step 5.2, calculate the acceleration of the autonomous vehicle car at time t when following the front vehicle according to formula (5) SIDM :

[0059] (5)

[0060] In formula (5), is the acceleration intensity, ​for the preceding vehicle the longitudinal position at time t, for the preceding vehicle the speed at time t, for the safe following distance, for the safe stopping distance; for the desired headway, and has:

[0061] (6)

[0062] Step 6, after t+1 is assigned to t, return to step 1 for sequential execution until the autonomous vehicle car ends driving.

[0063] In this embodiment, an electronic device includes a memory for storing a program supporting a processor to execute the above-mentioned car-following target decision method, and the processor is configured to execute the program stored in the memory.

[0064] In this embodiment, a computer-readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to perform the steps of the above-mentioned car-following target decision method.

Claims

1. A target-following decision-making method for autonomous vehicles in laneless environments, characterized in that, This method is applied to road scenarios without lane markings. Taking the forward direction of the autonomous vehicle as the positive x-axis (longitudinal direction), and setting the longitudinal coordinate of the autonomous vehicle's center of mass to zero and the lateral coordinate of the road centerline to zero, the autonomous vehicle target following decision method includes the following steps: Step 1: Obtain the driving behavior state information and surrounding environment information of a certain autonomous vehicle (car) at time t. The driving behavior state information includes: the speed of the autonomous vehicle (car). vehicle width Train Length The surrounding environment information includes the vehicle's location coordinates, and the speed, width, length, and location coordinates of all vehicles ahead of the autonomous vehicle (car). Step 2: Determine the observation area (DZ) and the risk area (RZ); Step 2.1: Calculate the observation area DZ of the autonomous vehicle car according to formula (1); (1) In equation (1), It is the longitudinal range of the observation area DZ; It is the expected time interval; It's a comfortable acceleration; It is the lateral range of the observation area DZ; It is the lateral coordinate of the right boundary of the road; The left boundary of the road is represented by its lateral coordinates. Step 2.2: Number all preceding vehicles in the observation area DZ of the autonomous vehicle car, and denote any j-th preceding vehicle as _j_. ; Step 2.3: Calculate the risk zone RZ of the autonomous vehicle car according to formula (2); (2) In equation (2), It is the vertical range of the risk zone RZ. It is the horizontal range of the risk zone RZ. Let be the lateral coordinates of the autonomous vehicle car at time t. For the car in front width, For safety factor; It is the minimum lateral safety clearance; Step 3: Determine if there is a vehicle ahead in the risk zone RZ; if not, proceed to step 4; if yes, proceed to step 5. Step 4: Calculate the acceleration of the autonomous vehicle car at time t using the Free-IDM method shown in equation (3). Then, proceed to step 6; (3) In equation (3), It is the maximum acceleration; It is the expected speed; Step 5: Determine the target vehicle (car) to follow and calculate the acceleration at time t. ; Step 5.1: Use equation (4) to determine the vehicle in front with the smallest longitudinal distance to the autonomous vehicle car within the risk zone RZ. and will As a target for autonomous vehicles; (4) In equation (4), The car in front The vertical position coordinate at time t, The car in front The horizontal position coordinates at time t; Step 5.2: Calculate the speed of the autonomous vehicle car following the car in the SIDM method shown in equation (5). acceleration at time t : (5) In equation (5), To accelerate the intensity, For the car in front At time t, the vertical position For the car in front The velocity at time t To maintain a safe following distance, For safe parking distance; To determine the desired front-end spacing, we have: (6) Step 6: After assigning t+1 to t, return to step 1 and execute sequentially until the autonomous vehicle car ends driving.

2. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the autonomous vehicle following target decision method of claim 1, and the processor is configured to execute the program stored in the memory.

3. A computer-readable storage medium storing a computer program, characterized in that, The computer program, when run by the processor, executes the steps of the autonomous vehicle following target decision method of claim 1.

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