Obstacle information display method, vehicle, storage medium and computer program product

Abstract

The invention provides an obstacle information display method, a vehicle, a storage medium and a computer program product. The method comprises the steps that initial obstacle information is acquired, and the initial obstacle information is used for representing rasterized space information corresponding to a plurality of candidate obstacles sensed by a target vehicle based on an occupied network; the initial obstacle information is screened through preset constraint conditions, target obstacle information is obtained, the preset constraint conditions comprise area constraint conditions and lane constraint conditions, the area constraint conditions are used for determining whether candidate obstacles are located in a target area range or not, and the lane constraint conditions are used for determining whether the candidate obstacles are located in the target area range or not; the lane constraint condition is used for determining whether the candidate obstacle is located in a preset lane range; performing rendering processing based on the target obstacle information to generate an obstacle rendering model; and displaying the obstacle rendering model in the environment reconstruction display interface. The obstacle information display method and device solve the technical problem that obstacle information display is not comprehensive in an obstacle information display method provided in the related technology.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent driving, and in particular, to an obstacle information display method, a vehicle, a storage medium, and a computer program product. Background Art

[0002] With the rapid development of intelligent driving technology, Surrounding Reality (SR), as a key function in assisted driving systems, can provide drivers with an intuitive view of the vehicle's surroundings to enhance driving safety and reduce the need for takeover. In existing technologies, the SR interface mainly displays obstacles with clear semantic information, such as pedestrians and vehicles. However, for general obstacles without semantic information, such as construction fences and bridge piers, the SR interface is often not displayed because their importance in driving scenarios is not easy to quantify. This incomplete information display method cannot provide sufficient explanation to the driver when the assisted driving system takes obstacle avoidance actions, which may cause the driver to panic due to not understanding the vehicle's behavior, thereby increasing the number of unnecessary takeovers and affecting the driving experience and safety.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present disclosure provide an obstacle information display method, a vehicle, a storage medium, and a computer program product to at least solve the technical problem of incomplete obstacle information display existing in the obstacle information display method provided in the related art.

[0005] According to one aspect of an embodiment of the present disclosure, a method for displaying obstacle information is provided, comprising: obtaining initial obstacle information, wherein the initial obstacle information is used to represent rasterized spatial information corresponding to multiple candidate obstacles perceived by a target vehicle based on an occupied network; filtering the initial obstacle information using preset constraints to obtain target obstacle information, wherein the preset constraints include: an area constraint and a lane constraint, wherein the area constraint is used to determine whether the candidate obstacle is located within a target area, and the lane constraint is used to determine whether the candidate obstacle is located within a preset lane; performing rendering processing based on the target obstacle information to generate an obstacle rendering model; and displaying the obstacle rendering model in an environment reconstruction display interface.

[0006] Optionally, the initial obstacle information is screened and processed using preset constraints to obtain the target obstacle information, including: using the regional constraint condition to determine the position of the candidate obstacle to obtain a first judgment result; in response to determining that the candidate obstacle satisfies the regional constraint condition based on the first judgment result, the initial obstacle information corresponding to the candidate obstacle is determined as the target obstacle information.

[0007] Optionally, the initial obstacle information is screened using the preset constraint condition to obtain the target obstacle information, including: in response to the first judgment result, determining that the candidate obstacle does not satisfy the area constraint condition, performing position judgment on the candidate obstacle using the lane constraint condition to obtain a second judgment result; in response to determining that the candidate obstacle satisfies the lane constraint condition based on the second judgment result, determining the initial obstacle information corresponding to the candidate obstacle as the target obstacle information; in response to determining that the candidate obstacle does not satisfy the lane constraint condition based on the second judgment result, filtering the initial obstacle information corresponding to the candidate obstacle.

[0008] Optionally, the position of the candidate obstacle is judged using the regional constraint condition to obtain a first judgment result, including: obtaining the motion state information and the calibrated area width of the target vehicle, wherein the motion state information is used to represent the driving speed and vehicle rotation speed of the target vehicle; determining the target area length based on the motion state information; determining the target area range corresponding to the regional constraint condition using the calibrated area width and the target area length; and performing position judgment based on the target area range and the grid coordinate position of the candidate obstacle to obtain the first judgment result.

[0009] Optionally, the position of the candidate obstacle is judged by using the lane constraint condition to obtain the second judgment result, including: traversing multiple lane lines of the target road based on the grid coordinate position of the candidate obstacle to obtain a target lane linear point, wherein the target lane linear point is used to represent the edge lane linear point corresponding to the candidate obstacle; and judging the position of the candidate obstacle according to the target lane linear point to determine the second judgment result.

[0010] Optionally, performing position judgment on the candidate obstacle according to the target lane linear points, and determining the second judgment result includes: in response to the first lane linear point or the second lane linear point where the candidate obstacle does not exist in the target lane linear points, determining that the candidate obstacle is not in a preset lane range, wherein the first lane linear point is used to represent the left lane linear point, and the second lane linear point is used to represent the right lane linear point; in response to the first lane linear point and the second lane linear point where the candidate obstacle exists in the target lane linear points, performing position judgment using driving attribute information associated with the target lane linear points to determine the second judgment result.

[0011] Optionally, the position is determined by using the driving attribute information associated with the target lane linear point, and determining the second judgment result includes: in response to determining that the first lane linear point does not satisfy the first attribute constraint based on the driving attribute information, determining that the candidate obstacle is not in the preset lane range, wherein the first attribute constraint is used to indicate that the right driving direction of the first lane linear point is the forward direction, and the left driving direction of the first lane linear point is the non-forward direction; in response to determining that the first lane linear point satisfies the first attribute constraint based on the driving attribute information, and the second lane linear point does not satisfy the second attribute constraint, determining that the candidate obstacle is not in the preset lane range, wherein the second attribute constraint is used to indicate that the left driving direction of the second lane linear point is the forward direction, and the right driving direction of the second lane linear point is the non-forward direction; in response to determining that the first lane linear point satisfies the first attribute constraint based on the driving attribute information, and the second lane linear point satisfies the second attribute constraint, determining that the candidate obstacle is in the preset lane range.

[0012] According to one aspect of an embodiment of the present disclosure, an obstacle information display device is provided, comprising: an acquisition module, used to acquire initial obstacle information, wherein the initial obstacle information is used to represent rasterized spatial information corresponding to multiple candidate obstacles perceived by a target vehicle based on an occupied network; a processing module, used to filter and process the initial obstacle information using preset constraints to obtain target obstacle information, wherein the preset constraints include: an area constraint and a lane constraint, the area constraint is used to determine whether the candidate obstacle is located within a target area, and the lane constraint is used to determine whether the candidate obstacle is located within a preset lane; a rendering module, used to perform rendering processing based on the target obstacle information to generate an obstacle rendering model; and a display module, used to display the obstacle rendering model in an environment reconstruction display interface.

[0013] Optionally, the processing module is further used to: use preset constraints to filter and process the initial obstacle information to obtain the target obstacle information, including: using regional constraints to determine the position of the candidate obstacle to obtain a first judgment result; in response to determining that the candidate obstacle satisfies the regional constraint based on the first judgment result, determine the initial obstacle information corresponding to the candidate obstacle as the target obstacle information.

[0014] Optionally, the processing module is further used to: use preset constraints to filter the initial obstacle information to obtain the target obstacle information, including: in response to the first judgment result, determining that the candidate obstacle does not satisfy the area constraint, using the lane constraint to determine the position of the candidate obstacle to obtain a second judgment result; in response to determining that the candidate obstacle satisfies the lane constraint based on the second judgment result, determining the initial obstacle information corresponding to the candidate obstacle as the target obstacle information; in response to determining that the candidate obstacle does not satisfy the lane constraint based on the second judgment result, filtering the initial obstacle information corresponding to the candidate obstacle.

[0015] Optionally, the processing module is also used to: use the area constraint condition to judge the position of the candidate obstacle, and obtain the first judgment result including: obtaining the motion state information and the calibrated area width of the target vehicle, wherein the motion state information is used to represent the driving speed and vehicle speed of the target vehicle; determining the target area length based on the motion state information; determining the target area range corresponding to the area constraint condition using the calibrated area width and the target area length; and performing position judgment based on the target area range and the grid coordinate position of the candidate obstacle to obtain the first judgment result.

[0016] Optionally, the processing module is also used to: use lane constraint conditions to determine the position of the candidate obstacle, and obtain a second judgment result including: traversing multiple lane lines of the target road based on the grid coordinate position of the candidate obstacle to obtain a target lane linear point, wherein the target lane linear point is used to represent the edge lane linear point corresponding to the candidate obstacle; and determining the position of the candidate obstacle according to the target lane linear point to determine the second judgment result.

[0017] Optionally, the processing module is further used to: perform position judgment on the candidate obstacle according to the target lane linear point, and determine the second judgment result including: in response to the first lane linear point or the second lane linear point where the candidate obstacle does not exist in the target lane linear point, determining that the candidate obstacle is not in the preset lane range, wherein the first lane linear point is used to represent the left lane linear point, and the second lane linear point is used to represent the right lane linear point; in response to the first lane linear point and the second lane linear point where the candidate obstacle exists in the target lane linear point, performing position judgment using the driving attribute information associated with the target lane linear point to determine the second judgment result.

[0018] Optionally, the processing module is also used to: use the driving attribute information associated with the target lane linear point to perform position judgment, and determine the second judgment result including: in response to determining based on the driving attribute information that the first lane linear point does not satisfy the first attribute constraint, determining that the candidate obstacle is not in the preset lane range, wherein the first attribute constraint is used to indicate that the right driving direction of the first lane linear point is the forward direction, and the left driving direction of the first lane linear point is the non-forward direction; in response to determining based on the driving attribute information that the first lane linear point satisfies the first attribute constraint, and the second lane linear point does not satisfy the second attribute constraint, determining that the candidate obstacle is not in the preset lane range, wherein the second attribute constraint is used to indicate that the left driving direction of the second lane linear point is the forward direction, and the right driving direction of the second lane linear point is the non-forward direction; in response to determining based on the driving attribute information that the first lane linear point satisfies the first attribute constraint, and the second lane linear point satisfies the second attribute constraint, determining that the candidate obstacle is in the preset lane range.

[0019] According to one embodiment of the present disclosure, a vehicle is also provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the obstacle information display method in the embodiment of the present disclosure.

[0020] According to one embodiment of the present disclosure, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the obstacle information display method in the embodiment of the present disclosure.

[0021] According to one embodiment of the present disclosure, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the obstacle information display method in the embodiment of the present disclosure is implemented.

[0022] In the embodiment of the present disclosure, by acquiring initial obstacle information and filtering the initial obstacle information using preset constraints, target obstacle information is obtained, and then rendering is performed based on the target obstacle information to generate an obstacle rendering model. Finally, the obstacle rendering model is displayed in the environment reconstruction display interface, thereby achieving the purpose of enhancing the user's understanding of the vehicle's intelligent driving behavior, thereby achieving the technical effect of improving the user's driving experience and safety, and thus solving the technical problem of incomplete obstacle information display in the obstacle information display method provided in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0024] Figure 1 is a flow chart of a method for displaying obstacle information according to one embodiment of the present disclosure;

[0025] Figure 2 is a schematic diagram of an obstacle information display method according to one embodiment of the present disclosure;

[0026] Figure 3 is a schematic diagram of a regional constraint condition according to one embodiment of the present disclosure;

[0027] Figure 4 is a schematic diagram of a lane constraint condition according to one embodiment of the present disclosure;

[0028] Figure 5 is a schematic diagram of an obstacle information display method according to one embodiment of the present disclosure;

[0029] Figure 6 It is a structural block diagram of an obstacle information display device according to one embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present disclosure, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present disclosure.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0032] In related technologies, the SR interface mainly displays obstacles with clear semantic information, such as pedestrians and vehicles, while common obstacles without semantic information, such as construction fences and bridge piers, are often not displayed because their importance in driving scenarios is not easy to quantify. This incomplete information display method cannot provide sufficient explanation to the driver when the assisted driving system takes obstacle avoidance actions, causing the driver to panic due to not understanding the vehicle's behavior, thereby increasing the number of unnecessary takeovers and affecting the driving experience and safety.

[0033] Specifically, in assisted driving or autonomous driving mode, the vehicle can detect obstacles based on its perception system and automatically adjust its driving strategy to avoid potential traffic risks. However, since the SR interface does not display general obstacles without semantic information, the driver may not immediately understand the vehicle's behavior logic when seeing the vehicle suddenly slow down or change lanes, especially in high-speed driving or complex road conditions. This uncertainty will increase the driver's anxiety and panic, causing the driver to over-intervene in the vehicle's autonomous driving function and take over unnecessarily, thus affecting overall driving safety.

[0034] According to an embodiment of the present disclosure, a method embodiment of a method for displaying obstacle information is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0035] Figure 1 is a flow chart of a method for displaying obstacle information according to one embodiment of the present disclosure, such as Figure 1 As shown, the method comprises the following steps:

[0036] Step S11, obtaining initial obstacle information, wherein the initial obstacle information is used to represent rasterized spatial information corresponding to a plurality of candidate obstacles perceived by the target vehicle based on the occupancy network;

[0037] Step S12, screening the initial obstacle information using preset constraints to obtain target obstacle information, wherein the preset constraints include: area constraints and lane constraints, the area constraints are used to determine whether the candidate obstacle is located in the target area, and the lane constraints are used to determine whether the candidate obstacle is located in the preset lane;

[0038] Step S13, performing rendering processing based on the target obstacle information to generate an obstacle rendering model;

[0039] Step S14: displaying the obstacle rendering model in the environment reconstruction display interface.

[0040] The above-mentioned initial obstacles refer to the obstacle information in the environment that is first detected and recorded by the perception system of the intelligent driving vehicle. According to their nature and impact on driving behavior, initial obstacles can be divided into two main categories: one is obstacles with clear semantic information, such as pedestrians, other vehicles, bicycles, traffic signs, etc., which play a direct and clear role in driving decisions; the other is general obstacles without semantic information, including construction fences, bridge piers, curbs, trees, street lights, etc.

[0041] The above-mentioned occupancy network (Occupancy Network algorithm) is a deep learning model that is mainly used for the representation and generation of objects in three-dimensional space. In the field of intelligent driving, the occupancy network is used to perceive and understand the vehicle's surrounding environment, especially for detecting and identifying obstacles that do not have clear semantic classifications, such as construction fences, bridge piers and other general obstacles. The occupancy network learns the relationship between input data, such as point clouds, images, etc., and the occupancy of objects in three-dimensional space, and outputs a probability distribution to represent the possibility of each position in three-dimensional space being occupied by an obstacle, thereby generating three-dimensional rasterized spatial information of obstacles.

[0042] The candidate obstacles mentioned above refer to general obstacles without semantic information. Due to the lack of specific semantic labels, general obstacles may not be fully identified or displayed in traditional environment perception and display solutions. Although general obstacles without semantic information do not directly participate in traffic behavior, they still have an important impact on the vehicle's driving path, obstacle avoidance strategy or safe driving.

[0043] The above-mentioned rasterized spatial information refers to the information that the position and attributes of obstacles in three-dimensional space are represented by a grid coordinate system. Each obstacle is divided into a series of grids in space. The coordinates, height information and grid size information of each grid together constitute the rasterized spatial information of the obstacle, which is used to accurately describe the position, size and shape of the obstacle in the vehicle's surrounding environment.

[0044] The above-mentioned screening process refers to the process of analyzing and screening general obstacles without semantic information detected by the perception module in the intelligent driving system to determine which obstacles should be presented to the driver on the vehicle's SR interface. Its main goal is to ensure that the displayed obstacle information can not only reflect objects that have a practical impact on driving safety and path planning, but also avoid information overload, ensure stable controller performance, and provide the driver with clear and timely visual feedback.

[0045] The above-mentioned area constraints are used to determine whether the candidate obstacle is within the target area. The target area, i.e., the rectangular danger area, is used to evaluate and determine whether the candidate obstacle needs to be considered as an obstacle with potential or direct safety impact on the current driving operation. The setting of the rectangular danger area is based on the consideration of improving driving safety and optimizing the driver's trust in the autonomous driving system, ensuring that the driver can understand the vehicle's surrounding environment in a timely and accurate manner, especially when encountering general obstacles without semantic information, and can understand the rationality of the vehicle's deceleration and avoidance behavior.

[0046] The above lane constraints are used to determine whether a candidate obstacle is within the preset lane range. The fundamental purpose is to determine whether a candidate obstacle without specific semantic information is within the preset lane range that is directly related to driving safety and path planning. Through in-depth data analysis and situational understanding, efficient screening of environmental perception information is achieved to ensure that only obstacles that are truly within the lane will be identified by the system and presented to the driver on the SR interface.

[0047] The above-mentioned environment reconstruction display interface is usually referred to as SR interface in the field of intelligent driving and autonomous driving vehicle technology. SR interface is a highly integrated visualization tool that can provide drivers with a real-time, intuitive and detailed virtual reconstruction of the vehicle's surrounding environment. SR interface reconstructs road conditions on the display screen in front of the driver by utilizing data collected by sensors on the vehicle, such as radar, camera, lidar, etc., combined with advanced algorithms such as Occupancy Network.

[0048] Exemplarily, in the operating environment of an intelligent driving vehicle, when the vehicle is traveling on the road, its equipped sensors such as laser radar and camera continuously monitor the surrounding environment. When the laser radar captures a construction fence for the first time, the point cloud data is processed based on the occupancy network algorithm to generate three-dimensional rasterized spatial information of the construction fence, including its precise position, height and width in three-dimensional space. After receiving the above data, the data processing module immediately starts the screening process to determine whether the above construction fence is a target obstacle based on the regional constraints and lane constraints. If the above construction fence is a target obstacle, it enters the rendering processing stage, converts the above construction fence information into a format that can be understood by the graphics rendering module, generates an obstacle rendering model, and presents it on the SR interface of the vehicle. Based on the above steps, the driver can see a virtual representation corresponding to the actual position of the construction fence, which helps the driver understand why the vehicle suddenly slows down or changes its driving trajectory.

[0049] Based on the above steps S11 to S14, by obtaining initial obstacle information and screening the initial obstacle information using preset constraints, target obstacle information is obtained, and then rendering is performed based on the target obstacle information to generate an obstacle rendering model. Finally, the obstacle rendering model is displayed in the environment reconstruction display interface, thereby achieving the purpose of enhancing the user's understanding of the vehicle's intelligent driving behavior, thereby achieving the technical effect of improving the user's driving experience and safety, and thus solving the technical problem of incomplete obstacle information display in the obstacle information display method provided in the relevant technology.

[0050] The obstacle information display method in the embodiment of the present disclosure is further introduced below.

[0051] Optionally, in step S12, the initial obstacle information is screened using preset constraints to obtain target obstacle information including:

[0052] Step S121, using the regional constraint condition to determine the position of the candidate obstacle to obtain a first determination result;

[0053] Step S122: in response to determining that the candidate obstacle satisfies the area constraint condition based on the first judgment result, initial obstacle information corresponding to the candidate obstacle is determined as target obstacle information.

[0054] For example, after determining the target area range, the grid coordinate position of the candidate obstacle is compared with the position information of the target area range to determine whether the candidate obstacle is within the target area range. If the grid coordinate position of the candidate obstacle falls within the predefined target area range, it is considered that the candidate obstacle meets the area constraint condition, and the corresponding initial obstacle information will be determined as the target obstacle information.

[0055] Based on the above steps S121 to S122, the position of the candidate obstacle is judged using the regional constraint condition to obtain a first judgment result, and the initial obstacle information corresponding to the candidate obstacle that meets the regional constraint condition is determined as the target obstacle information, which can ensure that only obstacles located in a specific area are identified and displayed, thereby helping to improve the accuracy and usability of the SR interface.

[0056] Optionally, in step S12, the initial obstacle information is screened using preset constraints to obtain target obstacle information including:

[0057] Step S21, in response to the first judgment result, determining that the candidate obstacle does not satisfy the area constraint condition, using the lane constraint condition to perform position judgment on the candidate obstacle to obtain a second judgment result;

[0058] Step S22, in response to determining that the candidate obstacle satisfies the lane constraint condition based on the second judgment result, determining the initial obstacle information corresponding to the candidate obstacle as the target obstacle information;

[0059] Step S23, in response to determining that the candidate obstacle does not satisfy the lane constraint condition based on the second judgment result, filtering the initial obstacle information corresponding to the candidate obstacle.

[0060] For example, suppose the vehicle is traveling on a multi-lane highway, and the sensor detects a construction fence in front of it. However, according to the preliminary regional constraint condition, the construction fence is not within the rectangular danger zone, that is, the first judgment result confirms that it does not meet the regional constraint condition. Subsequently, the construction fence is further judged according to the lane constraint condition to evaluate whether it is within the preset lane range. If the construction fence is within the preset lane range, it is determined as a target obstacle. Otherwise, the information of the construction fence is filtered.

[0061] Based on the above steps S21 to S23, when the candidate obstacle does not meet the area constraint condition, the lane constraint condition is used to judge the position of the candidate obstacle to obtain a second judgment result, and the target obstacle information is determined based on the second judgment result, and then the initial obstacle information corresponding to the candidate obstacle that does not meet the lane constraint condition is filtered and processed, so that not only obstacles that pose an immediate threat to driving safety can be accurately identified and responded to, but also "potential obstacles" that do not directly threaten driving safety but may affect vehicle passage under certain conditions can be intelligently screened out, thereby ensuring the comprehensiveness and intelligence of information processing, and providing the driver with more accurate and comprehensive driving environment information, thereby improving driving safety and user experience.

[0062] Figure 2 is a schematic diagram of an obstacle information display method according to one embodiment of the present disclosure, such as Figure 2 As shown, in the process of determining the obstacle information, it is necessary to first obtain the grid coordinates of the candidate obstacle, and then determine whether the grid coordinates of the candidate obstacle meet the regional constraint conditions. If the grid coordinates of the candidate obstacle meet the regional constraint conditions, the current candidate obstacle information is output as the target obstacle information. Otherwise, it is determined whether the grid coordinates of the candidate obstacle meet the lane constraint conditions. If the grid coordinates of the candidate obstacle meet the lane constraint conditions, the current candidate obstacle information is output as the target obstacle information. Otherwise, the obstacle information is filtered.

[0063] Optionally, in step S121, the position of the candidate obstacle is determined by using the regional constraint condition, and the first determination result includes:

[0064] Step S1211, obtaining the motion state information of the target vehicle and the width of the calibration area, wherein the motion state information is used to indicate the driving speed and vehicle rotation speed of the target vehicle;

[0065] Step S1212, determining the length of the target area based on the motion state information;

[0066] Step S1213, determining the target area range corresponding to the area constraint condition by using the calibration area width and the target area length;

[0067] Step S1214: Perform position determination based on the target area range and the grid coordinate position of the candidate obstacle to obtain a first determination result.

[0068] The above-mentioned motion state information refers to the real-time dynamic parameters of the vehicle during driving, mainly including the vehicle's own speed and vehicle speed. The vehicle speed is the rotation speed of the vehicle around the vertical axis, usually called yaw_rate. The motion state information is collected by the vehicle's sensors in real time and transmitted to the core processor of the intelligent driving system for dynamic evaluation of the vehicle's driving environment and potential risks. Among them, the driving speed is an indicator of the vehicle's forward speed, and the vehicle speed reflects the intensity of the vehicle's turn. The motion state information is a key parameter for dynamically generating the length of the rectangular danger zone, which directly affects the vehicle's evaluation and response strategy to the obstacles ahead.

[0069] The above calibration area width refers to a fixed value preset in the intelligent driving system to define the width of the rectangular danger area. The selection of the calibration area width needs to comprehensively consider factors such as vehicle type, driving speed, road conditions, and driving safety to achieve the best environmental perception and risk assessment effect.

[0070] The above target area length can be calculated by the following formula:

[0071]

[0072] in, is the target area length, c is the linear parameter of speed change, v is the vehicle speed, min_length is the minimum value of the preset length, c and v are usually calibrated according to the actual vehicle. When v=0, the target area length is the minimum value of the preset length.

[0073] For example, assuming that the width of the calibration area is 3m, c=1.0, min_length=15.0m, v=60km / h, yaw_rate=0.1rad / s, when the vehicle is in a large turn, the length of the rectangular filter is set to 1.0m for safety restrictions under special conditions. Based on the above parameters, it can be calculated that =31.57m, that is, the length of the rectangular danger zone is 31.57m. At this time, a target range area with a length of 31.57 meters and a width of 3 meters is constructed. Taking the position of the vehicle as the origin and the direction of the vehicle as the x-axis, assuming that the grid coordinate position of the candidate obstacle is (x0=20, y0=1.5), it can be judged that the candidate obstacle is within the target area at this time, which is the first judgment result.

[0074] Based on the above steps S1211 to S1214, by obtaining the motion state information and the calibrated area width of the target vehicle, and determining the target area length based on the motion state information, and then using the calibrated area width and the target area length to determine the target area range corresponding to the area constraint condition, finally based on the target area range and the grid coordinate position of the candidate obstacle, a position judgment is performed to obtain a first judgment result. Even if the obstacle detected by the vehicle does not have clear semantic information, a visual representation of the obstacle can be displayed on the SR interface through position judgment, so that the driver can understand the reason for the vehicle's deceleration or avoidance, thereby avoiding unnecessary panic takeover and improving driving experience and safety.

[0075] Figure 3 is a schematic diagram of a regional constraint condition according to one embodiment of the present disclosure, such as Figure 3 As shown in Figure 3, the regional constraint is the rectangular danger zone.

[0076] Optionally, in step S21, the position of the candidate obstacle is determined by using the lane constraint condition, and the second determination result includes:

[0077] Step S211, traversing multiple lane lines of the target road based on the grid coordinate position of the candidate obstacle to obtain a target lane linear point, wherein the target lane linear point is used to represent an edge lane linear point corresponding to the candidate obstacle;

[0078] Step S212: determine the position of the candidate obstacle according to the target lane linear point and determine a second determination result.

[0079] The above-mentioned traversal processing refers to the process of starting from the general obstacle position obtained by the perception module and checking the lane lines along the road one by one until the lane edge lines on the farthest sides of the obstacle are determined.

[0080] The lane linear points are specific locations in the road environment that define lane boundaries. In the perception systems of smart and autonomous vehicles, lane linear points are key reference points used to build an accurate map of the vehicle's surroundings and help the vehicle understand its specific location and direction on the road.

[0081] Specifically, after determining that the candidate obstacle is not within the target area based on the first judgment result, it is necessary to further judge the position of the candidate obstacle to determine whether the candidate obstacle is located on the driving path of the road. First, starting from the grid coordinate position of the candidate obstacle obtained by the perception module, the lane lines of the target road are traversed until the lane edge lines on both sides farthest from the obstacle are found, that is, the target lane linear point; secondly, according to the attributes of the found target lane linear point, the position of the candidate obstacle is judged to determine the second judgment result.

[0082] Based on the above steps S211 to S212, multiple lane lines of the target road are traversed based on the grid coordinate position of the candidate obstacle to obtain the target lane linear point, and the position of the candidate obstacle is judged based on the target lane linear point to determine the second judgment result, thereby ensuring that even if the candidate obstacle is not within the rectangular danger zone, the obstacle that may actually affect the vehicle driving can be identified through detailed lane line traversal and position judgment, thereby not only avoiding excessive presentation of information, but also ensuring that the driver can obtain obstacle information that is crucial to driving safety in a timely manner, thereby improving the overall performance and driving experience of the intelligent driving system.

[0083] Optionally, in step S212, the position of the candidate obstacle is determined according to the linear point of the target lane, and the second determination result includes:

[0084] Step S2121, in response to the absence of the first lane linear point or the second lane linear point of the candidate obstacle in the target lane linear point, determining that the candidate obstacle is not in the preset lane range, wherein the first lane linear point is used to represent the left lane linear point, and the second lane linear point is used to represent the right lane linear point;

[0085] Step S2122, in response to the first lane linear point and the second lane linear point that are candidate obstacles existing in the target lane linear point, position judgment is performed using the driving attribute information associated with the target lane linear point to determine a second judgment result.

[0086] Specifically, when the left lane linear point or the right lane linear point of the candidate obstacle does not exist in the target lane linear point, it means that the candidate obstacle is not within the preset lane range at this time; when the left lane linear point and the right lane linear point of the candidate obstacle exist in the target lane linear point at the same time, it means that the candidate obstacle is within the preset lane range at this time, so that the second judgment result can be obtained according to the lane linear point type contained in the target lane linear point.

[0087] Based on the above steps S2121 to S2122, judging whether the candidate obstacle is within the preset lane range based on the first lane linear point and the second lane linear point can avoid processing and displaying long-distance obstacles outside the road, thereby reducing unnecessary calculations and information overload.

[0088] Figure 4 is a schematic diagram of a lane constraint condition according to one embodiment of the present disclosure, such as Figure 4 The black solid lines shown represent the left boundary of the road and the right boundary of the road respectively, the black arrows represent the driving direction of vehicles in each lane, and “×” represents the non-forward direction.

[0089] Optionally, in step S2122, the position is determined by using the driving attribute information associated with the target lane linear point, and the second determination result includes:

[0090] Step S31, in response to determining that the first lane linear point does not satisfy a first attribute constraint condition based on the driving attribute information, determining that the candidate obstacle is not in a preset lane range, wherein the first attribute constraint condition is used to indicate that the right driving direction of the first lane linear point is a forward direction, and the left driving direction of the first lane linear point is a non-forward direction;

[0091] Step S32, in response to determining based on the driving attribute information that the first lane linear point satisfies the first attribute constraint condition, and the second lane linear point does not satisfy the second attribute constraint condition, determining that the candidate obstacle is not within the preset lane range, wherein the second attribute constraint condition is used to indicate that the left driving direction of the second lane linear point is the forward direction, and the right driving direction of the second lane linear point is the non-forward direction;

[0092] Step S33, in response to determining based on the driving attribute information that the first lane linear point satisfies the first attribute constraint condition, and the second lane linear point satisfies the second attribute constraint condition, it is determined that the candidate obstacle is within the preset lane range.

[0093] The first attribute constraint condition means that the right side of the lane line on the left side of the general obstacle has a driving direction of "forward" and the left side is "not drivable" or "reverse". The second attribute constraint condition means that the left side of the lane line on the right side of the general obstacle has a driving direction of "forward" and the right side is "not drivable" or "reverse".

[0094] Specifically, in the process of implementing the obstacle information display method, the driving attribute information of the first lane linear point is first checked to determine whether the right driving direction of the first lane linear point is "forward" and whether the left driving direction is "unable to drive" or "backward". If the left driving direction of the first lane linear point is "forward", it indicates that the first lane linear point does not meet its preset first attribute constraint condition, indicating that the currently identified candidate obstacle is not within the predetermined lane range, and thus does not pose a direct threat to the current driving path of the vehicle. If the first lane linear point meets its preset first attribute constraint condition, but the driving attribute of the second lane linear point does not meet the second attribute constraint condition, it will also be determined that the candidate obstacle is not within the preset lane range. Only when the first lane linear point meets the first attribute constraint condition and the second lane linear point also meets the second attribute constraint condition, can it be finally determined that the candidate obstacle is indeed within the preset lane range.

[0095] Based on the above optional embodiments, whether the candidate obstacle is within the preset lane range is determined based on the first attribute constraint and the second attribute constraint, thereby avoiding user confusion or panic caused by excessive display of obstacles, ensuring the rationality of displayed information, and improving the driver's trust in and user experience of the automatic driving assistance system.

[0096] Figure 5 is a schematic diagram of an obstacle information display method according to one embodiment of the present disclosure, such as Figure 5 As shown in the figure, during the obstacle display process, the grid coordinates (x0, y0) of the candidate obstacle are first obtained; secondly, the lane line is traversed to obtain the target lane linear point. If the difference between the target lane linear point and the candidate obstacle x-axis coordinate is greater than 3m, it indicates that the target lane linear point is far away from the candidate obstacle in the direction of vehicle travel, and it is necessary to continue searching for a target lane linear point closer to the obstacle. When the x-axis coordinate difference between a lane linear point and the obstacle is less than or equal to 3 meters, it indicates that the lane linear point is closer to the obstacle and is used as a candidate value. If there is no first lane linear point or second lane linear point in the above candidate values, it means that the candidate obstacle is not in the preset lane range; if the above candidate value does not satisfy the first attribute constraint, or the above candidate value satisfies the first attribute constraint but does not satisfy the second attribute constraint, the candidate obstacle is also not in the preset lane range; when the above candidate value satisfies both the first attribute constraint and the second attribute constraint, it can be determined that the candidate obstacle is in the preset lane range, ensuring that only those obstacles that really affect the vehicle driving safety will be further processed by the system and displayed on the SR interface, thereby avoiding information redundancy and ensuring the accuracy and effectiveness of driving assistance information, providing the driver with a clear and timely driving environment reconstruction display, and improving the safety of the driving process and the user experience.

[0097] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present disclosure.

[0098] Figure 6 is a structural block diagram of an obstacle information display device according to one embodiment of the present disclosure, such as Figure 6 As shown, the device comprises:

[0099] An acquisition module 601 is used to acquire initial obstacle information, wherein the initial obstacle information is used to represent rasterized spatial information corresponding to a plurality of candidate obstacles perceived by the target vehicle based on the occupancy network;

[0100] The processing module 602 is used to filter and process the initial obstacle information using preset constraints to obtain target obstacle information, wherein the preset constraints include: area constraints and lane constraints, the area constraints are used to determine whether the candidate obstacle is located in the target area, and the lane constraints are used to determine whether the candidate obstacle is located in the preset lane;

[0101] The rendering module 603 is used to perform rendering processing based on the target obstacle information to generate an obstacle rendering model;

[0102] The display module 604 is used to display the obstacle rendering model in the environment reconstruction display interface.

[0103] Optionally, the processing module 602 is further used to: use preset constraints to filter the initial obstacle information to obtain target obstacle information, including: using regional constraints to determine the position of the candidate obstacle to obtain a first judgment result; in response to determining that the candidate obstacle satisfies the regional constraint based on the first judgment result, determine the initial obstacle information corresponding to the candidate obstacle as the target obstacle information.

[0104] Optionally, the processing module 602 is further used to: use preset constraints to filter the initial obstacle information to obtain the target obstacle information, including: in response to the first judgment result, determining that the candidate obstacle does not satisfy the area constraint, using the lane constraint to determine the position of the candidate obstacle to obtain a second judgment result; in response to determining that the candidate obstacle satisfies the lane constraint based on the second judgment result, determining the initial obstacle information corresponding to the candidate obstacle as the target obstacle information; in response to determining that the candidate obstacle does not satisfy the lane constraint based on the second judgment result, filtering the initial obstacle information corresponding to the candidate obstacle.

[0105] Optionally, the processing module 602 is also used to: use the area constraint condition to determine the position of the candidate obstacle, and obtain the first judgment result including: obtaining the motion state information and the calibrated area width of the target vehicle, wherein the motion state information is used to represent the driving speed and vehicle speed of the target vehicle; determining the target area length based on the motion state information; determining the target area range corresponding to the area constraint condition using the calibrated area width and the target area length; and performing position judgment based on the target area range and the grid coordinate position of the candidate obstacle to obtain the first judgment result.

[0106] Optionally, the processing module 602 is further used to: use lane constraint conditions to determine the position of the candidate obstacle, and obtain a second judgment result including: traversing multiple lane lines of the target road based on the grid coordinate position of the candidate obstacle to obtain a target lane linear point, wherein the target lane linear point is used to represent the edge lane linear point corresponding to the candidate obstacle; and determining the position of the candidate obstacle according to the target lane linear point to determine the second judgment result.

[0107] Optionally, the processing module 602 is further used to: perform position judgment on the candidate obstacle according to the target lane linear point, and determine the second judgment result including: in response to the first lane linear point or the second lane linear point where the candidate obstacle does not exist in the target lane linear point, determining that the candidate obstacle is not in the preset lane range, wherein the first lane linear point is used to represent the left lane linear point, and the second lane linear point is used to represent the right lane linear point; in response to the first lane linear point and the second lane linear point where the candidate obstacle exists in the target lane linear point, performing position judgment using the driving attribute information associated with the target lane linear point to determine the second judgment result.

[0108] Optionally, the processing module 602 is further used to: perform position judgment using the driving attribute information associated with the target lane linear point, and determine the second judgment result including: in response to determining based on the driving attribute information that the first lane linear point does not satisfy the first attribute constraint, determining that the candidate obstacle is not in the preset lane range, wherein the first attribute constraint is used to indicate that the right driving direction of the first lane linear point is the forward direction, and the left driving direction of the first lane linear point is the non-forward direction; in response to determining based on the driving attribute information that the first lane linear point satisfies the first attribute constraint, and the second lane linear point does not satisfy the second attribute constraint, determining that the candidate obstacle is not in the preset lane range, wherein the second attribute constraint is used to indicate that the left driving direction of the second lane linear point is the forward direction, and the right driving direction of the second lane linear point is the non-forward direction; in response to determining based on the driving attribute information that the first lane linear point satisfies the first attribute constraint, and the second lane linear point satisfies the second attribute constraint, determining that the candidate obstacle is in the preset lane range.

[0109] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0110] According to one embodiment of the present disclosure, a vehicle is also provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the obstacle information display method in the embodiment of the present disclosure.

[0111] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0112] S1, obtaining initial obstacle information, wherein the initial obstacle information is used to represent rasterized spatial information corresponding to multiple candidate obstacles perceived by the target vehicle based on the occupancy network;

[0113] S2, screening and processing the initial obstacle information using preset constraints to obtain target obstacle information, wherein the preset constraints include: area constraints and lane constraints, the area constraints are used to determine whether the candidate obstacle is located in the target area, and the lane constraints are used to determine whether the candidate obstacle is located in the preset lane;

[0114] S3, performing rendering processing based on target obstacle information to generate an obstacle rendering model;

[0115] S4, displaying the obstacle rendering model in the environment reconstruction display interface.

[0116] According to one embodiment of the present disclosure, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the obstacle information display method in the embodiment of the present disclosure.

[0117] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0118] S1, obtaining initial obstacle information, wherein the initial obstacle information is used to represent rasterized spatial information corresponding to multiple candidate obstacles perceived by the target vehicle based on the occupancy network;

[0119] S2, screening and processing the initial obstacle information using preset constraints to obtain target obstacle information, wherein the preset constraints include: area constraints and lane constraints, the area constraints are used to determine whether the candidate obstacle is located in the target area, and the lane constraints are used to determine whether the candidate obstacle is located in the preset lane;

[0120] S3, performing rendering processing based on target obstacle information to generate an obstacle rendering model;

[0121] S4, displaying the obstacle rendering model in the environment reconstruction display interface.

[0122] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0123] According to one embodiment of the present disclosure, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the obstacle information display method in the embodiment of the present disclosure is implemented.

[0124] Optionally, in this embodiment, the computer program product may be configured as a computer program for executing the following steps:

[0125] S1, obtaining initial obstacle information, wherein the initial obstacle information is used to represent rasterized spatial information corresponding to multiple candidate obstacles perceived by the target vehicle based on the occupancy network;

[0126] S2, screening and processing the initial obstacle information using preset constraints to obtain target obstacle information, wherein the preset constraints include: area constraints and lane constraints, the area constraints are used to determine whether the candidate obstacle is located in the target area, and the lane constraints are used to determine whether the candidate obstacle is located in the preset lane;

[0127] S3, performing rendering processing based on target obstacle information to generate an obstacle rendering model;

[0128] S4, displaying the obstacle rendering model in the environment reconstruction display interface.

[0129] In the above embodiments of the present disclosure, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] In the several embodiments provided in the present disclosure, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0131] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0132] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present disclosure. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0134] The above are only preferred embodiments of the present disclosure. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present disclosure. These improvements and modifications should also be regarded as the protection scope of the present disclosure.

Claims

1. A method for displaying obstacle information, characterized in that: include: Acquire initial obstacle information, wherein the initial obstacle information is used to represent rasterized spatial information corresponding to a plurality of candidate obstacles perceived by the target vehicle based on the occupancy network; The initial obstacle information is screened and processed using preset constraints to obtain target obstacle information, wherein the preset constraints include: an area constraint and a lane constraint, the area constraint is used to determine whether the candidate obstacle is located in a target area, and the lane constraint is used to determine whether the candidate obstacle is located in a preset lane; Performing rendering processing based on the target obstacle information to generate an obstacle rendering model; The obstacle rendering model is displayed in the environment reconstruction display interface.

2. The obstacle information display method according to claim 1, characterized in that: The initial obstacle information is screened and processed using the preset constraint condition to obtain the target obstacle information including: Using the regional constraint condition to determine the position of the candidate obstacle, and obtaining a first determination result; In response to determining that the candidate obstacle satisfies the area constraint condition based on the first judgment result, the initial obstacle information corresponding to the candidate obstacle is determined as the target obstacle information.

3. The obstacle information display method according to claim 2, characterized in that: The initial obstacle information is screened and processed using the preset constraint condition to obtain the target obstacle information including: In response to the first judgment result, determining that the candidate obstacle does not satisfy the area constraint condition, performing position judgment on the candidate obstacle using the lane constraint condition to obtain a second judgment result; In response to determining that the candidate obstacle satisfies the lane constraint condition based on the second judgment result, determining the initial obstacle information corresponding to the candidate obstacle as the target obstacle information; In response to determining that the candidate obstacle does not satisfy the lane constraint condition based on the second judgment result, filtering the initial obstacle information corresponding to the candidate obstacle.

4. The obstacle information display method according to claim 2, characterized in that: Using the regional constraint condition to determine the position of the candidate obstacle, the first determination result includes: Acquiring motion state information and a calibration area width of a target vehicle, wherein the motion state information is used to indicate a driving speed and a vehicle rotation speed of the target vehicle; determining a target area length based on the motion state information; Determine the target area range corresponding to the area constraint condition by using the calibration area width and the target area length; Position judgment is performed based on the target area range and the grid coordinate position of the candidate obstacle to obtain the first judgment result.

5. The obstacle information display method according to claim 3, characterized in that: The lane constraint condition is used to determine the position of the candidate obstacle, and the second determination result is obtained including: Based on the grid coordinate position of the candidate obstacle, multiple lane lines of the target road are traversed to obtain a target lane linear point, wherein the target lane linear point is used to represent an edge lane linear point corresponding to the candidate obstacle; The position of the candidate obstacle is judged according to the linear point of the target lane to determine the second judgment result.

6. The obstacle information display method according to claim 5, characterized in that: The position of the candidate obstacle is determined according to the linear point of the target lane, and the second determination result includes: In response to the absence of a first lane linear point or a second lane linear point of the candidate obstacle in the target lane linear point, determining that the candidate obstacle is not in the preset lane range, wherein the first lane linear point is used to represent a left lane linear point, and the second lane linear point is used to represent a right lane linear point; In response to the first lane linear point and the second lane linear point of the candidate obstacle existing in the target lane linear point, position judgment is performed using driving attribute information associated with the target lane linear point to determine the second judgment result.

7. The obstacle information display method according to claim 6, characterized in that: Using the driving attribute information associated with the target lane linear point to perform position judgment, determining the second judgment result includes: In response to determining that the first lane linear point does not satisfy a first attribute constraint condition based on the driving attribute information, determining that the candidate obstacle is not in the preset lane range, wherein the first attribute constraint condition is used to indicate that a right driving direction of the first lane linear point is a forward direction, and a left driving direction of the first lane linear point is a non-forward direction; In response to determining, based on the driving attribute information, that the first lane linear point satisfies the first attribute constraint condition, and the second lane linear point does not satisfy the second attribute constraint condition, determining that the candidate obstacle is not within the preset lane range, wherein the second attribute constraint condition is used to indicate that the left driving direction of the second lane linear point is a forward direction, and the right driving direction of the second lane linear point is a non-forward direction; In response to determining that the first lane linear point satisfies the first attribute constraint condition and the second lane linear point satisfies the second attribute constraint condition based on the driving attribute information, it is determined that the candidate obstacle is within the preset lane range.

8. A vehicle, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the obstacle information display method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the storage medium is located is controlled to execute the obstacle information display method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the obstacle information display method according to any one of claims 1 to 7.

Images