Vehicle obstacle avoidance trajectory planning methods, devices, computer equipment, and storage media

Abstract

This application relates to a vehicle obstacle avoidance trajectory planning method, apparatus, computer equipment, computer-readable storage medium, and computer program product. The method includes: acquiring obstacle information around the current vehicle; predicting the travel trajectories of all obstacles within a preset future time period based on the obstacle information; identifying target obstacles requiring lane avoidance if the analysis of the travel trajectories indicates no rapidly approaching obstacles behind the current vehicle; calculating the lane occupancy space of the target obstacles based on their travel data; calculating the lane travel space of the current vehicle based on the lane occupancy space of the target obstacles; and performing path planning and speed planning based on the current vehicle's lane travel space to generate the current vehicle's obstacle avoidance trajectory. Using this method, when driving on highways, it is possible to effectively avoid vehicles traveling in the same direction in adjacent lanes, improving driving safety.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and in particular to a vehicle obstacle avoidance trajectory planning method, device, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] The development of autonomous driving technology has made obstacle avoidance trajectory planning crucial for safe and efficient driving. In ever-changing traffic environments, autonomous vehicles must accurately perceive their surroundings and make correct decisions. The core of obstacle avoidance trajectory planning lies in generating safe and comfortable driving paths that avoid obstacles.

[0003] In high-speed scenarios, autonomous vehicles need to plan safe trajectories to avoid collisions, especially large vehicles such as tractor-trailers and vans. Sufficient space must be provided for adjacent vehicles on highways to improve safety. This relies on environmental perception modules accurately identifying and tracking obstacles, predicting their trajectories, and thus planning avoidance paths.

[0004] Currently, autonomous driving solutions employ different strategies for static and dynamic obstacles, but many have shortcomings, especially when driving on highways, where they cannot effectively avoid vehicles traveling in the same direction in adjacent lanes, posing safety risks. Therefore, there is an urgent need for a vehicle obstacle avoidance trajectory planning method, device, computer equipment, computer-readable storage medium, and computer program product that can effectively avoid vehicles traveling in the same direction in adjacent lanes on highways, thereby improving driving safety. Summary of the Invention

[0005] Therefore, it is necessary to provide a vehicle obstacle avoidance trajectory planning method, device, computer equipment, computer-readable storage medium, and computer program product that can effectively avoid vehicles traveling in the same direction in adjacent lanes and improve driving safety when driving on highways, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a vehicle obstacle avoidance trajectory planning method, including:

[0007] Obtain information about obstacles around the current vehicle;

[0008] Based on the current obstacle information around the vehicle, predict the trajectory of all obstacles within a preset time period in the future;

[0009] If, based on the driving trajectory analysis, there are no rapidly approaching obstacles behind the current vehicle, target obstacles requiring lane avoidance are identified.

[0010] Calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle;

[0011] Calculate the current lane travel space of the vehicle based on the lane occupancy space of the target obstacle;

[0012] Based on the current vehicle's lane travel space, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory, which is then output to the vehicle control module.

[0013] In one embodiment, the screening process for identifying target obstacles requiring lane-keeping avoidance includes:

[0014] The obstacle that is in front of the current vehicle, is not changing lanes, has a speed no greater than the current vehicle's speed, and is in a different lane from the current vehicle is designated as the target obstacle.

[0015] In one embodiment, the driving data includes location information and speed information; calculating the lane occupancy space of the target obstacle based on the driving data of the target obstacle includes:

[0016] Based on the driving data of the target obstacle, obtain the distance difference and speed difference between the obstacle and the current vehicle;

[0017] Calculate the remaining time for the obstacle and the current vehicle to meet based on the distance difference and speed difference between them.

[0018] Based on the remaining time before the two objects meet, calculate the length of the reserved space corresponding to the obstacle to obtain the longitudinal occupancy dimension of the target obstacle in the lane;

[0019] Based on obstacle width information and lane line information, the lateral lane occupancy size of the target obstacle is calculated.

[0020] In one embodiment, the step of solving path planning and speed planning based on the current vehicle's lane travel space to generate the current vehicle's obstacle avoidance trajectory includes:

[0021] Based on the current lane driving space of the vehicle, analyze whether the current vehicle meets the vehicle passage space requirements;

[0022] If the current lane space for the vehicle does not meet the requirements for vehicle passage space, adjust the current vehicle's driving area and speed.

[0023] If the current vehicle's lane travel space meets the vehicle passage space requirements, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory.

[0024] In one embodiment, the method further includes:

[0025] If the driving trajectory analysis indicates that there is a rapidly approaching obstacle behind the current vehicle, the vehicle should remain centered in the current lane to avoid performing any avoidance maneuvers.

[0026] In one embodiment, the method further includes:

[0027] Obstacles that meet any of the preset conditions are considered obstacles that do not need to be avoided.

[0028] The preset conditions include not being in front of the current vehicle, being in a lane change state, the obstacle's speed being greater than the current vehicle's speed, and being in the same lane as the current vehicle.

[0029] Secondly, this application also provides a vehicle obstacle avoidance trajectory planning device, comprising:

[0030] The perception module is used to acquire information about obstacles around the current vehicle;

[0031] The prediction module is used to predict the trajectory of all obstacles within a preset time period in the future, based on the obstacle information around the current vehicle.

[0032] The filtering module is used to filter and identify target obstacles that require lane avoidance when the driving trajectory analysis shows that there are no rapidly approaching obstacles behind the current vehicle.

[0033] The calculation module is used to calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle;

[0034] The calculation module is also used to calculate the current vehicle's lane travel space based on the lane occupancy space of the target obstacle;

[0035] The trajectory planning module is used to solve path planning and speed planning based on the current vehicle's lane driving space, generate the current vehicle's obstacle avoidance trajectory, and output it to the vehicle control module.

[0036] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0037] Obtain information about obstacles around the current vehicle;

[0038] Based on the current obstacle information around the vehicle, predict the trajectory of all obstacles within a preset time period in the future;

[0039] If, based on the driving trajectory analysis, there are no rapidly approaching obstacles behind the current vehicle, target obstacles requiring lane avoidance are identified.

[0040] Calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle;

[0041] Calculate the current lane travel space of the vehicle based on the lane occupancy space of the target obstacle;

[0042] Based on the current vehicle's lane travel space, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory, which is then output to the vehicle control module.

[0043] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0044] Obtain information about obstacles around the current vehicle;

[0045] Based on the current obstacle information around the vehicle, predict the trajectory of all obstacles within a preset time period in the future;

[0046] If, based on the driving trajectory analysis, there are no rapidly approaching obstacles behind the current vehicle, target obstacles requiring lane avoidance are identified.

[0047] Calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle;

[0048] Calculate the current lane travel space of the vehicle based on the lane occupancy space of the target obstacle;

[0049] Based on the current vehicle's lane travel space, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory, which is then output to the vehicle control module.

[0050] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0051] Obtain information about obstacles around the current vehicle;

[0052] Based on the current obstacle information around the vehicle, predict the trajectory of all obstacles within a preset time period in the future;

[0053] If, based on the driving trajectory analysis, there are no rapidly approaching obstacles behind the current vehicle, target obstacles requiring lane avoidance are identified.

[0054] Calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle;

[0055] Calculate the current lane travel space of the vehicle based on the lane occupancy space of the target obstacle;

[0056] Based on the current vehicle's lane travel space, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory, which is then output to the vehicle control module.

[0057] The aforementioned vehicle obstacle avoidance trajectory planning method, device, computer equipment, computer-readable storage medium, and computer program product, through precise perception of the surrounding environment and prediction of obstacle trajectories, can effectively identify obstacles that need to be avoided and plan safe obstacle avoidance trajectories, thereby reducing the risk of collisions with obstacles, especially improving the safety of autonomous vehicles under high-speed driving conditions. Under the premise of ensuring safety, this method, through in-lane obstacle avoidance trajectory planning, enables vehicles to effectively avoid obstacles within a limited space, avoiding traffic congestion or unnecessary stopping and waiting caused by improper avoidance, thus improving road traffic efficiency. This method is particularly suitable for large vehicles such as tractor-trailers and vans driving in high-speed scenarios, effectively handling the problem of merging with vehicles traveling in the same direction in adjacent lanes, and adapting to complex and changing traffic environments. By introducing the calculation of lane occupancy space and dynamic planning of lane driving space, this method enhances the reliability of autonomous driving systems when handling complex traffic situations, enabling autonomous vehicles to operate stably under various conditions. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a diagram illustrating the application environment of a vehicle obstacle avoidance trajectory planning method in one embodiment.

[0060] Figure 2 This is a flowchart illustrating a vehicle obstacle avoidance trajectory planning method in one embodiment;

[0061] Figure 3 This is a flowchart illustrating the vehicle obstacle avoidance trajectory planning method in another embodiment;

[0062] Figure 4 This is a flowchart of the vehicle obstacle avoidance trajectory planning in the most detailed embodiment;

[0063] Figure 5 This is a flowchart of the vehicle obstacle avoidance and target obstacle selection process in the most detailed embodiment;

[0064] Figure 6 This is a schematic diagram of the drivable area corresponding to the vehicle's obstacle avoidance maneuver in the most detailed embodiment.

[0065] Figure 7 This is a structural block diagram of a vehicle obstacle avoidance trajectory planning device in one embodiment;

[0066] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0068] The vehicle obstacle avoidance trajectory planning method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0069] Server 104 acquires obstacle information around the current vehicle; based on the obstacle information, it predicts the driving trajectory of all obstacles within a preset time period in the future; if the driving trajectory analysis shows that there are no rapidly approaching obstacles behind the current vehicle, it filters and identifies target obstacles that need to be avoided within the lane; based on the driving data of the target obstacles, it calculates the lane occupancy space of the target obstacles; based on the lane occupancy space of the target obstacles, it calculates the lane driving space of the current vehicle; based on the lane driving space of the current vehicle, it performs path planning and speed planning to generate the obstacle avoidance trajectory of the current vehicle and outputs it to the vehicle control module of terminal 102.

[0070] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0071] In one exemplary embodiment, such as Figure 2As shown, a vehicle obstacle avoidance trajectory planning method is provided, which is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps S202 to S212. Wherein:

[0072] Step S202: Obtain obstacle information around the current vehicle.

[0073] Specifically, the perception module is responsible for collecting obstacle information around the vehicle, which forms the basis for obstacle avoidance trajectory planning. The perception module typically includes various sensors, such as cameras, LiDAR, and millimeter-wave radar, which provide detailed data on the vehicle's surrounding environment. For autonomous vehicles operating on highways, the forward perception distance needs to be at least 200 meters to ensure sufficient space for obstacle avoidance. The obstacle information acquired by the perception module needs to include the obstacle's size, center point position, speed, and heading angle. The acquired obstacle information mainly includes dimensions (length x width x height), center point information, speed information, and heading angle information.

[0074] Step S204: Based on the obstacle information around the current vehicle, predict the driving trajectory of all obstacles in the future preset time period.

[0075] Specifically, the prediction module utilizes data provided by the perception module, combined with machine learning and artificial intelligence algorithms, to predict the possible trajectory and behavior of obstacles within a future time period (e.g., 5-8 seconds). The selection of the preset time period is crucial for the accuracy and practicality of the prediction. A suitable preset time period ensures that the vehicle has sufficient time to respond to the prediction results and plan a safe obstacle avoidance trajectory. The predictions include, but are not limited to, changes in the obstacle's speed, possible steering maneuvers, lane-changing behaviors, acceleration, and deceleration. Due to the complexity of the traffic environment, the prediction module needs to be able to handle various traffic conditions, including multi-lane, multi-vehicle, and obstacles with different speeds and directions.

[0076] The prediction results directly impact obstacle avoidance trajectory planning decisions. If the prediction indicates that an obstacle will enter the vehicle's path, the obstacle avoidance trajectory planning needs to consider this situation and plan a new, safe path. The prediction module needs to be updated in real time to reflect the latest behavior of obstacles and environmental changes, ensuring the real-time performance and accuracy of obstacle avoidance trajectory planning.

[0077] Step S206: If the driving trajectory analysis shows that there are no rapidly approaching obstacles behind the current vehicle, the target obstacles that need to be avoided within the lane are screened and identified.

[0078] Specifically, before performing lane-keeping obstacle avoidance, it's essential to analyze the obstacle situation behind the vehicle. This is to ensure that a collision with a rapidly approaching vehicle from behind is avoided during the lane-keeping obstacle avoidance maneuver. Confirming the absence of rapidly approaching obstacles from behind is a prerequisite for performing lane-keeping obstacle avoidance. If a vehicle is rapidly approaching from behind, performing lane-keeping obstacle avoidance may increase the risk of a collision, thus requiring maintaining the current lane or taking other obstacle avoidance measures.

[0079] Once the rear is confirmed to be safe, the system will filter obstacles ahead, identifying those requiring lane avoidance. This typically involves analyzing information such as the obstacle's position, speed, and predicted trajectory. Filtering criteria may include whether the obstacle is in front of the vehicle, whether it is changing lanes, whether its speed is greater than the vehicle's speed, and whether it is in the same lane as the vehicle. These criteria help determine which obstacles pose a direct obstacle avoidance requirement to the current vehicle.

[0080] Step S208: Calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle.

[0081] Specifically, firstly, the perception module collects relevant information about the target obstacle, including its position, speed, dimensions (length, width, height), and heading angle. Lane occupancy space refers to the area occupied by an obstacle in the lane, which includes the obstacle's physical dimensions as well as any additional space reserved for safety. Calculating lane occupancy space typically involves analyzing the obstacle's dimensions and driving status. For example, the obstacle's width and length, combined with its position in the lane, can determine its occupied area. Safety redundancy is usually incorporated when calculating lane occupancy space, meaning that additional space is added around the obstacle's actual occupied area to ensure safety during obstacle avoidance. Since the obstacle's driving status is dynamic, the lane occupancy space calculation also needs to be updated in real time to reflect the obstacle's latest position and speed.

[0082] Step S210: Calculate the current vehicle's lane travel space based on the lane occupancy space of the target obstacle.

[0083] Specifically, first, based on the size, location, and trajectory of the target obstacles, the space they occupy on the lane is calculated. Then, the total width of the lane is determined, which is usually a fixed value but can vary depending on the region and road design.

[0084] The lane space occupied by the target obstacle is subtracted from the total lane width to obtain the current driving space for the vehicle. During the calculation, a safety redundancy is typically added around the obstacle's occupied space to ensure sufficient space to avoid collisions. Therefore, the actual drivable space will be less than the difference between the total lane width and the obstacle's occupied space. Since the traffic environment is dynamic, the calculated drivable space needs to be updated in real time to adapt to changes in obstacle position and speed. When calculating the drivable space, the current vehicle size also needs to be considered to ensure that the vehicle has sufficient space to pass through during obstacle avoidance without contact with obstacles or lane boundaries.

[0085] Step S212: Based on the current vehicle's lane driving space, perform path planning and speed planning to generate the current vehicle's obstacle avoidance trajectory and output it to the vehicle control module.

[0086] Specifically, path planning refers to determining the specific trajectory of a vehicle within a lane to avoid obstacles and ensure safety. This typically involves calculating the optimal driving path for the vehicle, which conforms to both lane space constraints and the position and movement of obstacles. Speed ​​planning refers to determining the optimal speed for the vehicle during obstacle avoidance to ensure that the vehicle can smoothly and safely pass through potentially hazardous areas. Speed ​​planning needs to consider the vehicle's dynamic performance, road conditions, traffic rules, and relative speed to obstacles.

[0087] Based on the results of path planning and velocity planning, the system solves for an obstacle avoidance trajectory that allows the vehicle to avoid obstacles and continue towards its destination while maintaining a safe distance. Since the traffic environment is dynamic, path planning and velocity planning need to be performed in real time to adapt to the movement of obstacles and changes in road conditions.

[0088] The planned obstacle avoidance trajectory needs to be output to the vehicle control module for execution. Based on the received trajectory information, the vehicle control module adjusts the vehicle's steering wheel, accelerator, and brakes to accurately track the obstacle avoidance trajectory. During the execution of the obstacle avoidance trajectory, the vehicle's perception module continuously monitors the surrounding environment and feeds real-time data back to the path planning and speed planning systems. If the environment changes, the system will replan the trajectory and update the vehicle control module's instructions.

[0089] The aforementioned vehicle obstacle avoidance trajectory planning method, by accurately perceiving the surrounding environment and predicting obstacle trajectories, can effectively identify obstacles that need to be avoided and plan safe obstacle avoidance trajectories, thereby reducing the risk of collisions with obstacles, especially under high-speed driving conditions, thus improving the safety of autonomous vehicles. Under the premise of ensuring safety, this method, through in-lane obstacle avoidance trajectory planning, enables vehicles to effectively avoid obstacles within limited space, avoiding traffic congestion or unnecessary stopping and waiting caused by improper avoidance, thereby improving road traffic efficiency. This method is particularly suitable for large vehicles such as tractor-trailers and vans driving in high-speed scenarios, effectively handling the problem of merging with vehicles traveling in the same direction in adjacent lanes, and adapting to complex and changing traffic environments. By introducing the calculation of lane occupancy space and dynamic planning of lane driving space, this method enhances the reliability of autonomous driving systems when handling complex traffic situations, enabling autonomous vehicles to operate stably under various conditions.

[0090] In one exemplary embodiment, identifying target obstacles requiring lane-keeping avoidance includes:

[0091] The obstacle that is in front of the current vehicle, is not changing lanes, has a speed no greater than the current vehicle's speed, and is in a different lane from the current vehicle is designated as the target obstacle.

[0092] Specifically, the condition of being in front of the current vehicle ensures that only obstacles located in front of the autonomous vehicle will be considered for lane-keeping obstacle avoidance. This is because the vehicle typically only needs to focus on obstacles in front of it to avoid collisions.

[0093] Not changing lanes: If an obstacle is changing lanes, especially into the lane where the autonomous vehicle is located, then this obstacle may soon become an obstacle in the same lane and requires special attention.

[0094] The condition that the obstacle's speed is no greater than the current vehicle's speed is used to exclude obstacles that are faster than the autonomous vehicle. If the obstacle is faster, it is less likely that the autonomous vehicle will need to perform lane-keeping avoidance, as it may pass in front of the vehicle.

[0095] The condition that the obstacle is in a different lane from the current vehicle ensures that only obstacles in the adjacent lane are considered. If the obstacle is in the same lane as the autonomous vehicle, the vehicle may need to consider other obstacle avoidance strategies, such as slowing down or changing lanes.

[0096] In this embodiment, these conditions allow for the precise identification of target obstacles requiring special attention, and the planning of obstacle avoidance paths within the lane for these obstacles. This screening method helps improve the efficiency and safety of obstacle avoidance strategies, ensuring that autonomous vehicles can make correct obstacle avoidance decisions in complex traffic environments. In this way, autonomous vehicles can reduce unnecessary obstacle avoidance maneuvers while reacting quickly to obstacles that truly need to be avoided.

[0097] In one exemplary embodiment, such as Figure 3 As shown, the driving data includes location information and speed information; based on the driving data of the target obstacle, the lane occupancy space of the target obstacle is calculated, including:

[0098] Step S302: Based on the driving data of the target obstacle, obtain the distance difference and speed difference between the obstacle and the current vehicle;

[0099] Step S304: Calculate the remaining time for the obstacle and the current vehicle to meet based on the distance difference and speed difference between the obstacle and the current vehicle.

[0100] Step S306: Calculate the length of the reserved space corresponding to the obstacle based on the remaining time of their encounter, and obtain the longitudinal occupancy dimension of the target obstacle in the lane.

[0101] Step S308: Based on the obstacle width information and lane line information, calculate the lateral lane occupancy size of the target obstacle.

[0102] Specifically, first, the system needs to determine the distance difference (i.e., the distance between the obstacle and the vehicle) and the speed difference (i.e., the difference between the obstacle's speed and the vehicle's speed). This data provides the basis for subsequent calculations. Using the distance and speed differences, the remaining time before the obstacle meets the vehicle is calculated. This time reflects how much time the vehicle has to react and plan obstacle avoidance before the obstacle reaches its current position.

[0103] Based on the remaining time before the encounter, calculate the reserved space length corresponding to the obstacle, that is, the length of space the obstacle will occupy when the encounter occurs. This length is the length of the obstacle plus the extra length reserved for safety considerations. The longitudinal lane occupancy dimension refers to the space occupied by the obstacle along the vehicle's direction of travel. By calculating the reserved space length of the obstacle, the longitudinal occupancy dimension of the obstacle in the lane can be obtained. The lateral lane occupancy dimension refers to the space occupied by the obstacle along the width of the vehicle's travel. This is usually calculated based on the actual width of the obstacle and lane marking information, and considering safety redundancy, a certain amount of space may be added to the obstacle's width.

[0104] By combining the longitudinal and lateral occupancy dimensions, the complete spatial area occupied by the obstacle in the lane can be obtained, i.e., the lane occupancy space. This spatial area is the area to be avoided when planning an obstacle avoidance path. After obtaining the lane occupancy space of the obstacle, the system can plan an obstacle avoidance path to ensure that the vehicle does not enter this area during travel, thereby avoiding a collision with the obstacle. Since the states of obstacles and vehicles are dynamically changing, these calculations need to be updated in real time to ensure the accuracy and safety of the obstacle avoidance path.

[0105] In this embodiment, through this process, the autonomous driving system can accurately predict the future position of obstacles and plan a safe obstacle avoidance path accordingly, thereby improving the safety and reliability of autonomous driving.

[0106] In an exemplary embodiment, path planning and speed planning are solved based on the current vehicle's lane travel space to generate the current vehicle's obstacle avoidance trajectory, including:

[0107] Based on the current lane driving space of the vehicle, analyze whether the current vehicle meets the vehicle passage space requirements;

[0108] If the current lane space for the vehicle does not meet the requirements for vehicle passage space, adjust the current vehicle's driving area and speed.

[0109] If the current vehicle's lane travel space meets the vehicle passage space requirements, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory.

[0110] Specifically, first, the system needs to assess whether the current lane space is sufficient for the vehicle to pass safely. This includes considering the vehicle's own size, the space occupied by obstacles, and the additional space required for safe driving (such as safety redundancy).

[0111] If the current lane space is insufficient for safe passage, the system needs to adjust the vehicle's driving area and speed. This may include: widening the driving area: attempting to widen the available driving space by changing the vehicle's position within the lane; slowing down: reducing vehicle speed to increase reaction time and reduce the risk of collisions in confined spaces; and temporarily stopping: in extreme cases where passage is not safe, the vehicle may need to temporarily stop and wait for sufficient driving space to become available.

[0112] If the current lane space for the vehicle meets the traffic requirements, the system will perform specific path planning and speed planning to generate an obstacle avoidance trajectory. This includes: Path planning: determining the vehicle's specific driving path within the lane, avoiding obstacles while considering the vehicle's dynamic performance and road conditions. Speed ​​planning: determining the optimal speed for the vehicle during obstacle avoidance to ensure the vehicle can smoothly and safely pass through potentially hazardous areas. Obstacle avoidance trajectory generation: Based on the results of path planning and speed planning, the system generates an obstacle avoidance trajectory. This trajectory details how the vehicle should drive within a specific time period to safely avoid obstacles. The planned obstacle avoidance trajectory needs to be output to the vehicle control module for execution. The vehicle control module adjusts the vehicle's steering wheel, accelerator, and brakes according to the received trajectory information to achieve precise tracking of the obstacle avoidance trajectory.

[0113] It should be noted that during the execution of the obstacle avoidance trajectory, the system's perception module continuously monitors the surrounding environment and feeds real-time data back to the path planning and speed planning systems. If the environment changes, the system will replan the trajectory and update the instructions of the vehicle control module.

[0114] In this embodiment, through this process, the autonomous vehicle can effectively avoid obstacles and continue driving towards its destination while ensuring safety.

[0115] In one exemplary embodiment, the method further includes:

[0116] If the vehicle detects a rapidly approaching obstacle behind it based on its driving trajectory analysis, it should maintain its position in the center of the current lane and avoid performing any evasive maneuvers.

[0117] Specifically, the rules first require autonomous driving systems to be able to detect what's behind the vehicle, particularly whether an obstacle is rapidly approaching. This is typically achieved through rearward perception modules, such as radar, cameras, or other sensors. When the system detects a rapidly approaching obstacle, it needs to assess whether the current avoidance maneuver would increase the risk of a collision with the obstacle. This assessment is based on the obstacle's speed, distance, and trajectory.

[0118] If the assessment indicates that performing an avoidance maneuver would increase the risk of a collision, the rule requires the autonomous vehicle to remain centered in its current lane and refrain from any lane changes or lane-keeping maneuvers. In this scenario, keeping the vehicle centered in its lane reduces interference with obstacles behind it, while providing both the autonomous vehicle and the obstacle with sufficient time and space to respond and adjust. This rule emphasizes that safety is the paramount consideration in obstacle avoidance strategies. Even if it means forgoing some avoidance opportunities, the safety of the vehicle and its passengers must be prioritized.

[0119] The system needs to continuously monitor the dynamics of obstacles behind it and dynamically adjust its strategy based on real-time data. If the threat from the obstacle behind it is eliminated, the system can reassess whether an avoidance maneuver is necessary.

[0120] While maintaining lane centering, the system may also need to consider other driving strategies, such as slowing down and warning the driver, to cope with complex traffic conditions.

[0121] In this embodiment, the obstacle avoidance strategy of autonomous vehicles is made more comprehensive and detailed, enabling them to effectively cope with various complex traffic situations while ensuring safety. Through accurate risk assessment and timely strategy adjustment, autonomous vehicles can protect passenger safety while minimizing interference with traffic flow.

[0122] In one exemplary embodiment, the method further includes:

[0123] Obstacles that meet any of the preset conditions are considered obstacles that do not need to be avoided.

[0124] The preset conditions include not being in front of the current vehicle, being in a lane change, the obstacle's speed being greater than the current vehicle's speed, and being in the same lane as the current vehicle.

[0125] Specifically, preset conditions are the criteria used by the autonomous driving system to determine which obstacles do not require avoidance. These conditions help the system filter out obstacles that do not pose a direct threat to the current vehicle or do not require avoidance action. If an obstacle meets any of the preset conditions, the system marks it as an obstacle that does not need to be avoided. This means that the vehicle can disregard these obstacles when planning its obstacle avoidance path.

[0126] Not in front of the vehicle: If the obstacle is not directly in front of the vehicle, it may mean that it will not directly affect the vehicle's path and therefore does not need to be avoided.

[0127] In a lane-changing situation: If an obstacle is changing lanes, especially into the lane where the vehicle is located, the vehicle may need to maintain its current position to avoid interfering with the obstacle's lane-changing behavior, or it may need to take other measures (such as slowing down) instead of simply avoiding it.

[0128] Obstacle speed greater than current vehicle speed: If the obstacle speed is greater than the vehicle speed, it means that the obstacle may quickly overtake the vehicle or is behind the vehicle, so there is no need to avoid it.

[0129] If the obstacle is in the same lane as the vehicle, the vehicle may need to take other measures (such as slowing down or changing lanes) to deal with it, rather than simply avoiding it.

[0130] In this embodiment, by setting these preset conditions, the autonomous driving system can simplify the obstacle avoidance decision-making process, reduce unnecessary avoidance actions, and improve driving efficiency, which helps to improve the safety and efficiency of autonomous vehicles because it allows the system to focus its resources and attention on obstacles that really need to be avoided.

[0131] The most detailed embodiment of this application is as follows:

[0132] This invention provides a method for planning vehicle obstacle avoidance trajectories, the specific process of which is shown in the appendix. Figure 4 As shown.

[0133] First, the autonomous driving system consists of a perception module, a prediction module, a trajectory planning module, and a vehicle control module. Information about obstacles within a certain range around the vehicle is primarily obtained by the perception module, using methods such as camera sensing, LiDAR point cloud sensing, and millimeter-wave radar sensing. The perception range required by the perception module is mainly determined by the operating scenario of the autonomous vehicle. For highway scenarios, the forward perception distance needs to be no less than 200m to ensure sufficient space for obstacle avoidance. The obstacle information acquired by the perception module mainly includes dimensions (length x width x height), center point information, speed information, and heading angle information. Based on this information, the prediction module calculates the trajectory of the obstacle over a future period (5-8 seconds). The prediction module typically uses machine learning and artificial intelligence algorithms to predict the behavior of other obstacles on the road, such as possible steering actions, lane-changing actions of other vehicles, and walking directions of pedestrians. This information guides the trajectory planning module in making driving strategy decisions, ensuring vehicle safety and traffic efficiency. Based on the obstacle information and the corresponding predicted obstacle trajectory, the autonomous vehicle can safely perform obstacle avoidance trajectory planning tasks.

[0134] To improve the safety of autonomous vehicles during obstacle avoidance, it is first necessary to detect the vehicle's backward obstacle-avoidance status. If there is a fast-moving obstacle behind the vehicle, lane-keeping obstacle avoidance should be avoided. In this case, if there is an obstacle in front, the autonomous vehicle should primarily reduce speed. If, after inspection, there is no fast-moving and approaching obstacle behind the vehicle, then when there is a suitable obstacle in the adjacent lane in front of the vehicle, lane-keeping obstacle avoidance can be implemented, improving vehicle efficiency while ensuring safe driving.

[0135] Lane-keeping obstacle avoidance primarily targets obstacles in adjacent lanes. When an obstacle in an adjacent lane is crossing the lane line, it needs to be avoided to prevent a collision. Additionally, for vehicles with wider bodies, such as tractor-trailers and vans, lane-keeping obstacle avoidance is necessary when encountering obstacles in adjacent lanes to prevent collisions and ensure driving safety. Therefore, before planning obstacle avoidance trajectories, the perception module needs to screen obstacles to identify those that meet the lane-keeping obstacle avoidance criteria, and then plan the avoidance trajectory accordingly. Specific screening criteria are as follows... Figure 5 The criteria are as follows: ① Is the obstacle within the vehicle's forward range? If so, it's necessary to consider whether to execute an in-lane obstacle avoidance strategy; otherwise, no action is required. ② Is the obstacle changing lanes? If it's changing from an adjacent lane to the vehicle's lane, in-lane obstacle avoidance is not possible; if it's changing from the vehicle's lane to an adjacent lane, in-lane obstacle avoidance is possible. ③ Is the obstacle's speed greater than the vehicle's speed? If it is, in-lane obstacle avoidance is not required. ④ Is the obstacle in the same lane as the vehicle? If so, there is no space for in-lane obstacle avoidance. If the obstacle meets the above criteria, the obstacle information is recorded, and the trajectory planning module performs targeted in-lane obstacle avoidance trajectory planning.

[0136] Before performing trajectory planning, the trajectory planning module needs to trim the drivable area based on obstacle information. The specific principle is as follows: Figure 6 As shown. First, there are obstacles A and B in front of the autonomous vehicle that meet the screening criteria. Information that can be obtained includes the obstacle's length, width, and speed. The drivable area trimming mainly consists of two parts: longitudinal length calculation and lateral dimension calculation. The longitudinal length is mainly obtained by adding the vehicle length and the reserved space length. The vehicle length is a fixed value, while the reserved space length is calculated by multiplying the encounter time between the autonomous vehicle and the obstacle by the obstacle's speed. The encounter time is set to be less than or equal to 3 seconds, and can be adjusted according to the scenario. The lateral dimension is mainly obtained by subtracting the lane width from the obstacle width. To increase safety, a redundancy can be added, for example, setting a redundancy of 0.4m, which can be adjusted according to the scenario.

[0137] After trimming the drivable area, it's necessary to determine if it meets the vehicle's passage space requirements. If the lateral space is insufficient to support the autonomous vehicle's passage, the drivable area needs to be appropriately widened to support trajectory planning and ensure stable, continuous driving. In this case, the autonomous vehicle needs to decelerate to avoid collisions with obstacles ahead. If the drivable area meets the autonomous vehicle's driving requirements, obstacle avoidance trajectory planning within the lane can be performed, enabling safe and efficient obstacle avoidance for the autonomous vehicle.

[0138] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0139] Based on the same inventive concept, this application also provides a vehicle obstacle avoidance trajectory planning device for implementing the vehicle obstacle avoidance trajectory planning method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more vehicle obstacle avoidance trajectory planning device embodiments provided below can be found in the limitations of the vehicle obstacle avoidance trajectory planning method described above, and will not be repeated here.

[0140] In one exemplary embodiment, such as Figure 7 As shown, a vehicle obstacle avoidance trajectory planning device is provided, comprising:

[0141] The perception module 702 is used to acquire obstacle information around the current vehicle;

[0142] The prediction module 704 is used to predict the driving trajectory of all obstacles in a future preset time period based on the obstacle information around the current vehicle;

[0143] The filtering module 706 is used to filter and identify target obstacles that require lane avoidance when the driving trajectory analysis shows that there are no rapidly approaching obstacles behind the current vehicle.

[0144] Calculation module 708 is used to calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle;

[0145] The calculation module 708 is also used to calculate the current vehicle's lane travel space based on the lane occupancy space of the target obstacle;

[0146] The trajectory planning module 710 is used to solve path planning and speed planning based on the current vehicle's lane driving space, generate the current vehicle's obstacle avoidance trajectory, and output it to the vehicle control module 712.

[0147] In an exemplary embodiment, the filtering module 706 is specifically used to: identify obstacles that are in front of the current vehicle, are not in a lane-changing state, have a speed no greater than the current vehicle's speed, and are in a different lane from the current vehicle as target obstacles.

[0148] In an exemplary embodiment, the driving data includes location information and speed information; the calculation module 708 is specifically used to obtain the distance difference and speed difference between the obstacle and the current vehicle based on the driving data of the target obstacle; calculate the remaining time for the obstacle and the current vehicle to meet based on the distance difference and speed difference; calculate the reserved space length corresponding to the obstacle based on the remaining time for the obstacle to meet, and obtain the longitudinal lane occupancy size of the target obstacle; and calculate the lateral lane occupancy size of the target obstacle based on the obstacle width information and lane line information.

[0149] In an exemplary embodiment, the trajectory planning module 710 is specifically used to analyze whether the current vehicle meets the vehicle passage space requirements based on the current vehicle's lane driving space; if the current vehicle's lane driving space does not meet the vehicle passage space requirements, adjust the current vehicle's driving area and driving speed; if the current vehicle's lane driving space meets the vehicle passage space requirements, perform path planning and speed planning to generate the current vehicle's obstacle avoidance trajectory.

[0150] In an exemplary embodiment, the vehicle control module 712 is used to keep the current vehicle centered in the current lane and avoid performing an avoidance action when the driving trajectory analysis indicates that there is a rapidly approaching obstacle behind the current vehicle.

[0151] In an exemplary embodiment, the filtering module 706 is specifically used to classify obstacles that meet any of the preset conditions as obstacles that do not need to be avoided; the preset conditions include not being in front of the current vehicle, being in a lane change state, the obstacle speed being greater than the current vehicle speed, and being in the same lane as the current vehicle.

[0152] Each module in the aforementioned vehicle obstacle avoidance trajectory planning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0153] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores driving data of target obstacles. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a vehicle obstacle avoidance trajectory planning method.

[0154] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0155] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0156] Obtain information about obstacles around the current vehicle;

[0157] Based on the current obstacle information around the vehicle, predict the trajectory of all obstacles within a preset time period in the future;

[0158] If the driving trajectory analysis shows that there are no rapidly approaching obstacles behind the current vehicle, then the target obstacles that need to be avoided within the lane are identified.

[0159] Calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle;

[0160] Calculate the current lane travel space of the vehicle based on the lane occupancy space of the target obstacle;

[0161] Based on the current vehicle's lane travel space, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory, which is then output to the vehicle control module.

[0162] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0163] The obstacle that is in front of the current vehicle, is not changing lanes, has a speed no greater than the current vehicle's speed, and is in a different lane from the current vehicle is designated as the target obstacle.

[0164] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0165] Driving data includes location information and speed information;

[0166] Based on the driving data of the target obstacle, obtain the distance difference and speed difference between the obstacle and the current vehicle;

[0167] Calculate the remaining time for the obstacle and the current vehicle to meet based on the distance difference and speed difference between them.

[0168] Based on the remaining time before the two objects meet, calculate the length of the reserved space corresponding to the obstacle to obtain the longitudinal occupancy dimension of the target obstacle in the lane;

[0169] Based on obstacle width information and lane line information, the lateral lane occupancy size of the target obstacle is calculated.

[0170] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0171] Based on the current lane driving space of the vehicle, analyze whether the current vehicle meets the vehicle passage space requirements;

[0172] If the current lane space for the vehicle does not meet the requirements for vehicle passage space, adjust the current vehicle's driving area and speed.

[0173] If the current vehicle's lane travel space meets the vehicle passage space requirements, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory.

[0174] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0175] If the vehicle detects a rapidly approaching obstacle behind it based on its driving trajectory analysis, it should maintain its position in the center of the current lane and avoid performing any evasive maneuvers.

[0176] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0177] Obstacles that meet any of the preset conditions are considered obstacles that do not need to be avoided.

[0178] The preset conditions include not being in front of the current vehicle, being in a lane change, the obstacle's speed being greater than the current vehicle's speed, and being in the same lane as the current vehicle.

[0179] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0180] Obtain information about obstacles around the current vehicle;

[0181] Based on the current obstacle information around the vehicle, predict the trajectory of all obstacles within a preset time period in the future;

[0182] If the driving trajectory analysis shows that there are no rapidly approaching obstacles behind the current vehicle, then the target obstacles that need to be avoided within the lane are identified.

[0183] Calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle;

[0184] Calculate the current lane travel space of the vehicle based on the lane occupancy space of the target obstacle;

[0185] Based on the current vehicle's lane travel space, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory, which is then output to the vehicle control module.

[0186] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0187] The obstacle that is in front of the current vehicle, is not changing lanes, has a speed no greater than the current vehicle's speed, and is in a different lane from the current vehicle is designated as the target obstacle.

[0188] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0189] Driving data includes location information and speed information;

[0190] Based on the driving data of the target obstacle, obtain the distance difference and speed difference between the obstacle and the current vehicle;

[0191] Calculate the remaining time for the obstacle and the current vehicle to meet based on the distance difference and speed difference between them.

[0192] Based on the remaining time before the two objects meet, calculate the length of the reserved space corresponding to the obstacle to obtain the longitudinal occupancy dimension of the target obstacle in the lane;

[0193] Based on obstacle width information and lane line information, the lateral lane occupancy size of the target obstacle is calculated.

[0194] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0195] Based on the current lane driving space of the vehicle, analyze whether the current vehicle meets the vehicle passage space requirements;

[0196] If the current lane space for the vehicle does not meet the requirements for vehicle passage space, adjust the current vehicle's driving area and speed.

[0197] If the current vehicle's lane travel space meets the vehicle passage space requirements, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory.

[0198] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0199] If the vehicle detects a rapidly approaching obstacle behind it based on its driving trajectory analysis, it should maintain its position in the center of the current lane and avoid performing any evasive maneuvers.

[0200] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0201] Obstacles that meet any of the preset conditions are considered obstacles that do not need to be avoided.

[0202] The preset conditions include not being in front of the current vehicle, being in a lane change, the obstacle's speed being greater than the current vehicle's speed, and being in the same lane as the current vehicle.

[0203] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0204] Obtain information about obstacles around the current vehicle;

[0205] Based on the current obstacle information around the vehicle, predict the trajectory of all obstacles within a preset time period in the future;

[0206] If the driving trajectory analysis shows that there are no rapidly approaching obstacles behind the current vehicle, then the target obstacles that need to be avoided within the lane are identified.

[0207] Calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle;

[0208] Calculate the current lane travel space of the vehicle based on the lane occupancy space of the target obstacle;

[0209] Based on the current vehicle's lane travel space, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory, which is then output to the vehicle control module.

[0210] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0211] The obstacle that is in front of the current vehicle, is not changing lanes, has a speed no greater than the current vehicle's speed, and is in a different lane from the current vehicle is designated as the target obstacle.

[0212] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0213] Driving data includes location information and speed information;

[0214] Based on the driving data of the target obstacle, obtain the distance difference and speed difference between the obstacle and the current vehicle;

[0215] Calculate the remaining time for the obstacle and the current vehicle to meet based on the distance difference and speed difference between them.

[0216] Based on the remaining time before the two objects meet, calculate the length of the reserved space corresponding to the obstacle to obtain the longitudinal occupancy dimension of the target obstacle in the lane;

[0217] Based on obstacle width information and lane line information, the lateral lane occupancy size of the target obstacle is calculated.

[0218] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0219] Based on the current lane driving space of the vehicle, analyze whether the current vehicle meets the vehicle passage space requirements;

[0220] If the current lane space for the vehicle does not meet the requirements for vehicle passage space, adjust the current vehicle's driving area and speed.

[0221] If the current vehicle's lane travel space meets the vehicle passage space requirements, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory.

[0222] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0223] If the vehicle detects a rapidly approaching obstacle behind it based on its driving trajectory analysis, it should maintain its position in the center of the current lane and avoid performing any evasive maneuvers.

[0224] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0225] Obstacles that meet any of the preset conditions are considered obstacles that do not need to be avoided.

[0226] The preset conditions include not being in front of the current vehicle, being in a lane change, the obstacle's speed being greater than the current vehicle's speed, and being in the same lane as the current vehicle.

[0227] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0228] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0229] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0230] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for planning vehicle obstacle avoidance trajectory, characterized in that, The method includes: Obtain information about obstacles around the current vehicle; Based on the current obstacle information around the vehicle, predict the trajectory of all obstacles within a preset time period in the future; If, based on the driving trajectory analysis, there are no rapidly approaching obstacles behind the current vehicle, target obstacles requiring lane avoidance are identified. Based on the driving data of the target obstacle, the lane occupancy space of the target obstacle is calculated. The driving data includes position information and speed information, including: obtaining the distance difference and speed difference between the obstacle and the current vehicle based on the driving data of the target obstacle; calculating the remaining time for the obstacle and the current vehicle to meet based on the distance difference and speed difference between the obstacle and the current vehicle; calculating the reserved space length corresponding to the obstacle based on the remaining time for the obstacle to meet, and obtaining the longitudinal lane occupancy size of the target obstacle; and calculating the lateral lane occupancy size of the target obstacle based on the obstacle width information and lane line information. Calculate the current lane travel space of the vehicle based on the lane occupancy space of the target obstacle; Based on the current vehicle's lane travel space, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory, which is then output to the vehicle control module.

2. The method according to claim 1, characterized in that, The screening process identifies target obstacles requiring lane avoidance, including: The obstacle that is in front of the current vehicle, is not changing lanes, has a speed no greater than the current vehicle's speed, and is in a different lane from the current vehicle is designated as the target obstacle.

3. The method according to claim 1, characterized in that, The process of solving path planning and speed planning based on the current vehicle's lane travel space to generate the current vehicle's obstacle avoidance trajectory includes: Based on the current lane driving space of the vehicle, analyze whether the current vehicle meets the vehicle passage space requirements; If the current lane space for the vehicle does not meet the requirements for vehicle passage space, adjust the current vehicle's driving area and speed. If the current vehicle's lane travel space meets the vehicle passage space requirements, path planning and speed planning are solved to generate the current vehicle's obstacle avoidance trajectory.

4. The method according to claim 1, characterized in that, The method further includes: If the driving trajectory analysis indicates that there is a rapidly approaching obstacle behind the current vehicle, the vehicle should remain centered in the current lane to avoid performing any avoidance maneuvers.

5. The method according to claim 2, characterized in that, The method further includes: Obstacles that meet any of the preset conditions are considered obstacles that do not need to be avoided. The preset conditions include not being in front of the current vehicle, being in a lane change state, the obstacle's speed being greater than the current vehicle's speed, and being in the same lane as the current vehicle.

6. A vehicle obstacle avoidance trajectory planning device, characterized in that, The device includes: The perception module is used to acquire information about obstacles around the current vehicle; The prediction module is used to predict the trajectory of all obstacles within a preset time period in the future, based on the obstacle information around the current vehicle. The filtering module is used to filter and identify target obstacles that require lane avoidance when the driving trajectory analysis shows that there are no rapidly approaching obstacles behind the current vehicle. The calculation module is used to calculate the lane occupancy space of the target obstacle based on the driving data of the target obstacle, including position and speed information. Specifically, it is used to: obtain the distance difference and speed difference between the obstacle and the current vehicle based on the driving data of the target obstacle; calculate the remaining time for the obstacle and the current vehicle to meet based on the distance difference and speed difference between the obstacle and the current vehicle; calculate the length of the reserved space corresponding to the obstacle based on the remaining time for the obstacle to meet, and obtain the longitudinal lane occupancy size of the target obstacle; and calculate the lateral lane occupancy size of the target obstacle based on the obstacle width information and lane line information. The calculation module is also used to calculate the current vehicle's lane travel space based on the lane occupancy space of the target obstacle; The trajectory planning module is used to solve path planning and speed planning based on the current vehicle's lane driving space, generate the current vehicle's obstacle avoidance trajectory, and output it to the vehicle control module.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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