Automatic driving obstacle collision detection method, controller and vehicle

Abstract

The present invention provides an obstacle collision detection method, a controller and a vehicle for automatic driving, the method comprising: obtaining an expected travel point sequence of the vehicle; sorting a first expected horizontal coordinate point sequence to obtain a first sorting sequence; sorting a first expected vertical coordinate point sequence to obtain a second sorting sequence; obtaining a predicted trajectory point sequence of the obstacle; creating a corresponding horizontal coordinate interval for each horizontal coordinate point in the second expected horizontal coordinate point sequence, and creating a corresponding vertical coordinate interval for each vertical coordinate point; using a binary search algorithm, based on the first sorting sequence, the horizontal coordinate interval, the second sorting sequence and the vertical coordinate interval, determining whether there is a collision risk between the vehicle and the obstacle; in this way, the first expected horizontal coordinate point sequence and the first expected vertical coordinate point sequence are sorted to obtain a corresponding sorting sequence. Since the binary search algorithm can quickly determine whether there are coordinate points that may collide, the calculation time is greatly shortened, thereby improving the safety of automatic driving of the vehicle.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to an autonomous driving obstacle collision detection method, controller and vehicle. Background Art

[0002] The autonomous driving and advanced assisted driving functions of smart cars free drivers from highly stressful and fatigue-prone driving tasks, providing a safe and comfortable driving experience. The bottom line of the autonomous driving software algorithm is to avoid collisions with surrounding obstacles and avoid any possibility of accidents.

[0003] In order to ensure the safety of operation, it is necessary to detect the possibility of collision between the vehicle itself and surrounding obstacles in real time and discover threats in the surrounding environment as soon as possible, so as to leave sufficient margin for decision-making.

[0004] However, due to the increasing complexity of the environment and the uncertainty of the behavior of obstacles such as pedestrians and other vehicles when performing autonomous driving tasks, traditional obstacle collision detection algorithms take up a lot of computing time when performing collision calculations, have low operating efficiency, and pose a threat to the safe operation of autonomous driving vehicles. It also increases the cost of computing hardware. Summary of the invention

[0005] In response to the problems existing in the prior art, the embodiments of the present invention provide an autonomous driving obstacle collision detection method, a controller and a vehicle to solve or partially solve the technical problem in the prior art that it takes a long time to perform collision calculations, resulting in increased calculation costs and reduced safety of autonomous driving.

[0006] A first aspect of the present invention provides an automatic driving obstacle collision detection method, characterized in that the method comprises:

[0007] Acquire an expected travel point sequence of the vehicle; the expected travel point sequence includes: a first expected horizontal coordinate point sequence and a first expected vertical coordinate point sequence;

[0008] Sorting the first expected horizontal coordinate point sequence based on a preset order to obtain a first sorting sequence; sorting the first expected vertical coordinate point sequence to obtain a second sorting sequence;

[0009] Determine a second expected horizontal coordinate point sequence and a second expected vertical coordinate point sequence of the obstacle based on the predicted trajectory point sequence of the obstacle;

[0010] Creating a corresponding abscissa interval for each abscissa point in the second expected abscissa point column, and creating a corresponding ordinate interval for each ordinate point in the second expected ordinate point column;

[0011] A binary search algorithm is used to determine whether there is a collision risk between the vehicle and the obstacle based on the first sorting sequence, the horizontal axis interval, the second sorting sequence, and the vertical axis interval.

[0012] In the above solution, the step of creating a corresponding horizontal coordinate interval for each horizontal coordinate point in the second expected horizontal coordinate point series includes:

[0013] Get the length L of the vehicle;

[0014] For each abscissa point in the second expected abscissa point column, according to formula X oin -L / 2 determines the lower limit of the horizontal axis interval, according to the formula X oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [X oin -L / 2,X oin +L / 2].

[0015] In the above solution, the step of creating a corresponding ordinate interval for each ordinate point in the second expected ordinate point column includes:

[0016] Get the length L of the vehicle;

[0017] For each abscissa point in the second expected abscissa point column, according to formula Y oin -L / 2 determines the lower limit of the horizontal axis interval, according to the formula Y oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [Y oin -L / 2, Y oin +L / 2].

[0018] In the above solution, the use of a binary search algorithm to determine whether there is a collision risk between the vehicle and the obstacle based on the first sorting sequence, the horizontal axis interval, the second sorting sequence and the vertical axis interval includes:

[0019] Determine whether each target horizontal coordinate point located in the horizontal coordinate interval can be found in the first sorting sequence by using a binary search algorithm, and if so, assign a valid value to the first search identifier;

[0020] Determine whether each target ordinate point located in the ordinate interval can be found in the second sorting sequence, and if so, assign a valid value to the second search identifier;

[0021] Determine whether there is a collision risk between the vehicle and the obstacle based on the target horizontal coordinate points, the target vertical coordinate points, the first search identifier, and the second search identifier.

[0022] In the above solution, determining whether there is a collision risk between the vehicle and the obstacle based on the target horizontal coordinate points, the target vertical coordinate points, the first search mark and the second search mark includes:

[0023] If it is determined that the maximum value of the target horizontal coordinate point is greater than or equal to the minimum value of the target vertical coordinate point, the maximum value of the target vertical coordinate point is greater than or equal to the minimum value of the target horizontal coordinate point, the first search identifier is a valid value and the second search identifier is a valid value, then it is determined that there is a risk of collision between the vehicle and the obstacle.

[0024] A second aspect of the present invention provides an autonomous driving obstacle collision detection controller, the controller comprising:

[0025] An acquisition unit is used to acquire an expected travel point sequence of the vehicle; the expected travel point sequence includes: a first expected horizontal coordinate point sequence and a first expected vertical coordinate point sequence; acquire a predicted trajectory point sequence of the obstacle; the predicted trajectory point sequence includes: a second expected horizontal coordinate point sequence and a second expected vertical coordinate point sequence;

[0026] A sorting unit, configured to sort the first expected horizontal coordinate point sequence based on a preset order to obtain a first sorting sequence; and sort the first expected vertical coordinate point sequence to obtain a second sorting sequence;

[0027] a creating unit, configured to create a corresponding abscissa interval for each abscissa point in the second expected abscissa point column, and to create a corresponding ordinate interval for each ordinate point in the second expected ordinate point column;

[0028] A judgment unit is used to determine whether there is a collision risk between the vehicle and the obstacle based on the first sorting sequence, the horizontal axis interval, the second sorting sequence and the vertical axis interval by using a binary search algorithm.

[0029] In the above solution, the creation unit is specifically used for:

[0030] Get the length L of the vehicle;

[0031] For each abscissa point in the second expected abscissa point column, the lower limit of the abscissa interval is determined according to the formula Xoin-L / 2, and the lower limit of the abscissa interval is determined according to the formula Xoin-L / 2. oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [X oin -L / 2,X oin +L / 2].

[0032] In the above solution, the creation unit is specifically used for:

[0033] Get the length L of the vehicle;

[0034] For each abscissa point in the second expected abscissa point column, according to formula Y oin -L / 2 determines the lower limit of the horizontal axis interval, according to the formula Y oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [Y oin -L / 2, Y oin +L / 2].

[0035] In the above solution, the judgment unit is specifically used for:

[0036] Determine whether each target horizontal coordinate point located in the horizontal coordinate interval can be found in the first sorting sequence by using a binary search algorithm, and if so, assign a valid value to the first search identifier;

[0037] Determine whether each target ordinate point located in the ordinate interval can be found in the second sorting sequence, and if so, assign a valid value to the second search identifier;

[0038] Determine whether there is a collision risk between the vehicle and the obstacle based on the target horizontal coordinate points, the target vertical coordinate points, the first search identifier, and the second search identifier.

[0039] A third aspect of the present invention provides a vehicle, the vehicle comprising the controller described in any one of the second aspects.

[0040] The present invention provides an obstacle collision detection method, a controller and a vehicle for autonomous driving, the method comprising: obtaining an expected travel point sequence of the vehicle; the expected travel point sequence comprises: a first expected horizontal coordinate point sequence and a first expected vertical coordinate point sequence; sorting the first expected horizontal coordinate point sequence based on a preset order to obtain a first sorting sequence; sorting the first expected vertical coordinate point sequence to obtain a second sorting sequence; obtaining a predicted trajectory point sequence of the obstacle, the predicted trajectory point sequence comprises: a second expected horizontal coordinate point sequence and a second expected vertical coordinate point sequence; creating a corresponding horizontal coordinate interval for each horizontal coordinate point in the second expected horizontal coordinate point sequence, and creating a corresponding horizontal coordinate interval for each horizontal coordinate point in the second expected vertical coordinate point sequence. For each ordinate point in the ordinate interval, a corresponding ordinate interval is created; using a binary search algorithm, whether there is a risk of collision between the vehicle and the obstacle is determined based on the first sorting sequence, the abscissa interval, the second sorting sequence and the ordinate interval; in this way, the first expected abscissa point sequence and the first expected ordinate point sequence are sorted to obtain a corresponding sorting sequence. When using the binary search algorithm and the corresponding sorting sequence to determine whether there is a risk of collision, since the binary search algorithm can quickly determine whether there are coordinate points that may collide, the calculation time can be greatly shortened, thereby improving the safety of vehicle automatic driving and reducing the hardware calculation cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] By reading the detailed description of the preferred embodiment below, various other advantages and benefits will become clear to those of ordinary skill in the art. The accompanying drawings are only used for the purpose of illustrating the preferred embodiment and are not considered to be limitations of the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings.

[0042] In the attached picture:

[0043] Figure 1 A schematic diagram of a process flow of an obstacle collision detection method for autonomous driving according to an embodiment of the present invention is shown;

[0044] Figure 2 A schematic diagram of a vehicle's expected travel point sequence and an obstacle prediction trajectory point sequence according to an embodiment of the present invention is shown;

[0045] Figure 3 A schematic diagram of the structure of an obstacle collision detection controller for autonomous driving according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0046] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0047] This embodiment provides an automatic driving obstacle collision detection method, such as Figure 1 As shown, the method comprises the following steps:

[0048] S110, obtaining an expected travel point sequence of the vehicle; the expected travel point sequence includes: a first expected horizontal coordinate point sequence and a first expected vertical coordinate point sequence; sorting the first expected horizontal coordinate point sequence based on a preset order to obtain a first sorting sequence; sorting the first expected vertical coordinate point sequence to obtain a second sorting sequence;

[0049] The autonomous driving vehicle can set a navigation path before driving, so the expected path point column of the vehicle can be obtained based on the navigation path ADC_Route = {<x1,y1> ,<x2,y2> … <x n ,y n >}.

[0050] The horizontal coordinates are extracted from the expected path point sequence to form a first expected horizontal coordinate point sequence ADC_RouteX={x1, x2, x3, ..., x n}; Extract the ordinate from the expected path point sequence to form the first expected ordinate point sequence ADC_RouteY = {y1, y2, y3...y n}.

[0051] Among them, x n Represents the x-axis coordinate of the autonomous vehicle in the world coordinate system, y n Represents the y-axis coordinate of the autonomous vehicle in the world coordinate system. The expected travel point column ADC_Route can be as follows Figure 2 shown.

[0052] After determining the first expected horizontal coordinate point sequence and the first expected vertical coordinate point sequence, the first expected horizontal coordinate point sequence is sorted based on a preset order to obtain a first sorting sequence Sort_RouteX={sx1, sx2, sx3…sx n}; Sort the first expected ordinate point sequence to obtain a second sorting sequence Sort_RouteY = {sy1, sy2, sy3...sy n}. The preset order can be ascending.

[0053] In this way, after the vehicle's coordinate data is preprocessed, a data basis is provided for subsequent collision calculations.

[0054] S111, determining a second expected horizontal coordinate point sequence and a second expected vertical coordinate point sequence of the obstacle based on the predicted trajectory point sequence of the obstacle;

[0055] The obstacle trajectory point sequence can be directly obtained from the calculation module for predicting obstacle trajectory points, such as Figure 2 As shown, the predicted trajectory point column Oi_Traj = { <x oi1 ,y oi1 ,t oi1 >、 <x oi2 ,y oi2 ,t oi2 >…… <x oin ,y oin ,t oin >}. In the point column, xoin represents the x-axis coordinate of the obstacle in the world coordinate system at the time toin, and yoin represents the y-axis coordinate of the obstacle in the world coordinate system at the time toin.

[0056] The second expected horizontal coordinate point sequence and the second expected vertical coordinate point sequence of the obstacle can be determined based on the predicted trajectory point sequence of the obstacle, as follows:

[0057] The horizontal coordinate point of the obstacle Oi is extracted from the predicted trajectory point sequence to form the second expected horizontal coordinate point sequence Oi_TrajX={x oi1 、x oi2 、xoi3 …x oin}.

[0058] The ordinate point of the obstacle Oi is extracted from the predicted trajectory point sequence to form the second expected ordinate point sequence Oi_TrajY={y oi1 ,y oi2 ,y oi3 …y oin}.

[0059] In this embodiment, the second expected horizontal coordinate point series is actually a point series of the expected obstacle trajectory on the X-axis, and the second expected vertical coordinate point series is actually a point series of the expected obstacle trajectory on the Y-axis.

[0060] S112, creating a corresponding abscissa interval for each abscissa point in the second expected abscissa point column, and creating a corresponding ordinate interval for each ordinate point in the second expected ordinate point column;

[0061] Since the vehicle is not a point but an object with a certain length, in order to improve the subsequent calculation accuracy, it is necessary to create a corresponding abscissa interval for each abscissa point in the second expected abscissa point column and a corresponding ordinate interval for each ordinate point in the second expected ordinate point column according to the vehicle body length.

[0062] In one implementation, creating a corresponding abscissa interval for each abscissa point in the second expected abscissa point column includes:

[0063] Get the length L of the vehicle;

[0064] For each abscissa point in the second expected abscissa point column, according to the formula X oin -L / 2 determines the upper limit of the horizontal axis interval, according to the formula X oin +L / 2 determines the lower limit of the horizontal axis interval; the horizontal axis interval is [X oin -L / 2,X oin +L / 2].

[0065] Create a corresponding ordinate interval for each ordinate point in the second expected ordinate point column, including:

[0066] Get the length L of the vehicle;

[0067] For each abscissa point in the second expected abscissa point column, according to the formula Y oin -L / 2 determines the lower limit of the horizontal axis interval, according to the formula Y oin +L / 2 determines the upper limit of the horizontal axis interval; the horizontal axis interval is [Y oin -L / 2, Y oin +L / 2].

[0068] S113: Using a binary search algorithm, determine whether there is a collision risk between the vehicle and the obstacle based on the first sorting sequence, the horizontal axis interval, the second sorting sequence, and the vertical axis interval.

[0069] After the horizontal axis interval and the vertical axis interval are determined, a binary search algorithm is used to determine whether there is a collision risk between the vehicle and the obstacle based on the first sorting sequence, the horizontal axis interval, the second sorting sequence and the vertical axis interval, specifically including:

[0070] Determine whether the target horizontal coordinate points within the horizontal coordinate interval can be found in the first sorting sequence by using the binary search algorithm. If so, assign a valid value to the first search identifier;

[0071] Determine whether each target ordinate point within the ordinate interval can be found in the second sorting sequence, and if so, assign a valid value to the second search identifier;

[0072] It is determined whether there is a collision risk between the vehicle and the obstacle based on each target horizontal coordinate point, each target vertical coordinate point, the first search mark and the second search mark.

[0073] In one embodiment, determining whether there is a collision risk between the vehicle and the obstacle based on each target horizontal coordinate point, each target vertical coordinate point, the first search mark, and the second search mark includes:

[0074] If it is determined that the maximum value of the target horizontal coordinate point is greater than or equal to the minimum value of the target vertical coordinate point, the maximum value of the target vertical coordinate point is greater than or equal to the minimum value of the target horizontal coordinate point, the first search identifier is a valid value and the second search identifier is a valid value, it is determined that there is a risk of collision between the vehicle and the obstacle.

[0075] Specifically, using the binary search algorithm, search for the first sorted sequence in the horizontal coordinate interval [X oin -L / 2,X oin +L / 2], if the target horizontal coordinate point can be found, the first search flag find_collide_x = 1. Since there may be multiple target horizontal coordinate points, the horizontal coordinate index interval [lowX, highX] can be determined based on the maximum and minimum values ​​of the target horizontal coordinate.

[0076] Use the binary search algorithm to find the number of cells in the horizontal coordinate interval [Y oin -L / 2, Y oin +L / 2], if the target horizontal coordinate point can be found, the second search flag find_collide_y = 1. Since there may be multiple target vertical coordinate points, the vertical coordinate index interval [lowY, highY] can be determined based on the maximum and minimum values ​​of the target total coordinates.

[0077] If it is determined that find_collide_x = 1, find_collide_y = 1, highX >= lowY and highY >= lowX, then it can be determined that there is an intersection between the predicted trajectory of the vehicle and the predicted trajectory of the obstacle, and there is a collision risk.

[0078] If it is determined that find_collide_x = 0, find_collide_y = 0, highX < lowY or highY < lowX, then it can be determined that there is no intersection between the predicted trajectory of the vehicle and the predicted trajectory of the obstacle, and there is no collision risk.

[0079] This embodiment uses a binary search algorithm to quickly find the target abscissa point and the target ordinate point that meet the requirements. Therefore, compared with the traditional traversal algorithm, it can significantly shorten the collision calculation time, improve the collision calculation efficiency, and reduce the hardware calculation cost.

[0080] To further improve the formation safety of autonomous driving, collision calculations can be performed at a preset calculation period, for example, once every 0.1 s. The calculation period can be determined based on the actual characteristics of the vehicle and will not be elaborated here.

[0081] Based on the same inventive concept as in the foregoing embodiment, this embodiment also provides an autonomous driving obstacle collision detection controller, as Figure 2 shown. The controller includes:

[0082] An acquisition unit 21, configured to acquire the expected travel point sequence of the vehicle; the expected travel point sequence includes: a first expected abscissa point sequence and a first expected ordinate point sequence; acquire the predicted trajectory point sequence of the obstacle, and the predicted trajectory point sequence includes: a second expected abscissa point sequence and a second expected ordinate point sequence;

[0083] A sorting unit 22, configured to sort the first expected abscissa point sequence based on a preset order to obtain a first sorted sequence; sort the first expected ordinate point sequence to obtain a second sorted sequence;

[0084] A creation unit 23, configured to create a corresponding abscissa interval for each abscissa point in the second expected abscissa point sequence, and create a corresponding ordinate interval for each ordinate point in the second expected ordinate point sequence;

[0085] A judgment unit 24, configured to use a binary search algorithm to determine whether there is a collision risk between the vehicle and the obstacle based on the first sorted sequence, the abscissa interval, the second sorted sequence, and the ordinate interval.

[0086] In one implementation, the creation unit 23 is specifically configured to:

[0087] Get the length L of the vehicle;

[0088] For each abscissa point in the second expected abscissa point column, the lower limit of the abscissa interval is determined according to the formula Xoin-L / 2, and the lower limit of the abscissa interval is determined according to the formula Xoin-L / 2. oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [X oin -L / 2,X oin +L / 2].

[0089] The creation unit 23 is specifically used for:

[0090] Get the length L of the vehicle;

[0091] For each abscissa point in the second expected abscissa point column, according to formula Y oin -L / 2 determines the lower limit of the horizontal axis interval, according to the formula Y oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [Y oin -L / 2, Y oin +L / 2].

[0092] The judging unit 24 is specifically used for:

[0093] Determine whether each target horizontal coordinate point located in the horizontal coordinate interval can be found in the first sorting sequence by using a binary search algorithm, and if so, assign a valid value to the first search identifier;

[0094] Determine whether each target ordinate point located in the ordinate interval can be found in the second sorting sequence, and if so, assign a valid value to the second search identifier;

[0095] Determine whether there is a collision risk between the vehicle and the obstacle based on the target horizontal coordinate points, the target vertical coordinate points, the first search identifier, and the second search identifier.

[0096] Since the controller introduced in the embodiment of the present invention is the controller used to implement the automatic driving obstacle collision detection method of the embodiment of the present invention, based on the method introduced in the embodiment of the present invention, the person skilled in the art can understand the specific structure and deformation of the controller, so it is not repeated here. All controllers used in the method of the embodiment of the present invention belong to the scope of protection of the present invention.

[0097] Based on the same inventive concept as the aforementioned embodiment, this embodiment further provides a vehicle, which includes the controller mentioned above. The specific structure and execution logic of the controller can be referred to the above description, so it will not be repeated here.

[0098] Through one or more embodiments of the present invention, the present invention has the following beneficial effects or advantages:

[0099] The present invention provides an obstacle collision detection method, a controller and a vehicle for autonomous driving, the method comprising: obtaining an expected travel point sequence of the vehicle; the expected travel point sequence comprises: a first expected horizontal coordinate point sequence and a first expected vertical coordinate point sequence; sorting the first expected horizontal coordinate point sequence based on a preset order to obtain a first sorting sequence; sorting the first expected vertical coordinate point sequence to obtain a second sorting sequence; obtaining a predicted trajectory point sequence of the obstacle, the predicted trajectory point sequence comprises: a second expected horizontal coordinate point sequence and a second expected vertical coordinate point sequence; creating a corresponding horizontal coordinate interval for each horizontal coordinate point in the second expected horizontal coordinate point sequence, and creating a corresponding horizontal coordinate interval for each horizontal coordinate point in the second expected vertical coordinate point sequence. For each ordinate point in the ordinate interval, a corresponding ordinate interval is created; using a binary search algorithm, whether there is a risk of collision between the vehicle and the obstacle is determined based on the first sorting sequence, the abscissa interval, the second sorting sequence and the ordinate interval; in this way, the first expected abscissa point sequence and the first expected ordinate point sequence are sorted to obtain a corresponding sorting sequence. When using the binary search algorithm and the corresponding sorting sequence to determine whether there is a risk of collision, since the binary search algorithm can quickly determine whether there are coordinate points that may collide, the calculation time can be greatly shortened, thereby improving the safety of vehicle automatic driving and reducing the hardware calculation cost.

[0100] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0101] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0102] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0103] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0104] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0105] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components in a gateway, proxy server, or system according to an embodiment of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0106] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

[0107] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An automatic driving obstacle collision detection method, characterized in that: The method comprises: Acquire an expected travel point sequence of the vehicle; the expected travel point sequence includes: a first expected horizontal coordinate point sequence and a first expected vertical coordinate point sequence; Sorting the first expected horizontal coordinate point sequence based on a preset order to obtain a first sorting sequence; sorting the first expected vertical coordinate point sequence to obtain a second sorting sequence; Determine a second expected horizontal coordinate point sequence and a second expected vertical coordinate point sequence of the obstacle based on the predicted trajectory point sequence of the obstacle; Creating a corresponding abscissa interval for each abscissa point in the second expected abscissa point column, and creating a corresponding ordinate interval for each ordinate point in the second expected ordinate point column; Using a binary search algorithm, it is determined whether there is a collision risk between the vehicle and the obstacle based on the first sorting sequence, the horizontal axis interval, the second sorting sequence and the vertical axis interval; wherein, The using of a binary search algorithm to determine whether there is a collision risk between the vehicle and the obstacle based on the first sorting sequence, the horizontal axis interval, the second sorting sequence, and the vertical axis interval includes: Determine whether each target horizontal coordinate point located in the horizontal coordinate interval can be found in the first sorting sequence by using a binary search algorithm, and if so, assign a valid value to the first search identifier; Determine whether each target ordinate point located in the ordinate interval can be found in the second sorting sequence, and if so, assign a valid value to the second search identifier; Determining whether there is a collision risk between the vehicle and the obstacle based on the target horizontal coordinate points, the target vertical coordinate points, the first search identifier, and the second search identifier; The determining whether there is a collision risk between the vehicle and the obstacle based on the target horizontal coordinate points, the target vertical coordinate points, the first search identifier, and the second search identifier includes: Determine the horizontal coordinate index interval [lowX, highX] based on the maximum and minimum values ​​of the target horizontal coordinate; Determine the ordinate index interval [lowY, highY] based on the maximum and minimum values ​​of the target ordinate; If it is determined that find_collide_x=1, find_collide_y=1, highX>=lowY and highY>=lowX, it is determined that there is a collision risk between the vehicle and the obstacle; find_collide_x is a first search identifier, and find_collide_y is a second search identifier.

2. The method according to claim 1, characterized in that The step of creating a corresponding abscissa interval for each abscissa point in the second expected abscissa point column comprises: Get the length L of the vehicle; For each abscissa point in the second expected abscissa point column, according to formula X oin -L / 2 determines the lower limit of the horizontal axis interval, according to the formula X oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [X oin -L / 2,X oin +L / 2].

3. The method according to claim 1, characterized in that The step of creating a corresponding ordinate interval for each ordinate point in the second expected ordinate point column comprises: Get the length L of the vehicle; For each abscissa point in the second expected abscissa point column, according to formula Y oin -L / 2 determines the lower limit of the horizontal axis interval, according to the formula Y oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [Y oin -L / 2, Y oin +L / 2].

4. An automatic driving obstacle collision detection controller, characterized in that: The controller comprises: An acquisition unit is used to acquire an expected travel point sequence of the vehicle; the expected travel point sequence includes: a first expected horizontal coordinate point sequence and a first expected vertical coordinate point sequence; acquire a predicted trajectory point sequence of the obstacle; the predicted trajectory point sequence includes: a second expected horizontal coordinate point sequence and a second expected vertical coordinate point sequence; A sorting unit, configured to sort the first expected horizontal coordinate point sequence based on a preset order to obtain a first sorting sequence; and sort the first expected vertical coordinate point sequence to obtain a second sorting sequence; a creating unit, configured to create a corresponding abscissa interval for each abscissa point in the second expected abscissa point column, and to create a corresponding ordinate interval for each ordinate point in the second expected ordinate point column; A judgment unit is used to determine whether there is a collision risk between the vehicle and the obstacle based on the first sorting sequence, the horizontal axis interval, the second sorting sequence and the vertical axis interval by using a binary search algorithm; wherein, The judging unit is specifically used for: Determine whether each target horizontal coordinate point located in the horizontal coordinate interval can be found in the first sorting sequence by using a binary search algorithm, and if so, assign a valid value to the first search identifier; Determine whether each target ordinate point located in the ordinate interval can be found in the second sorting sequence, and if so, assign a valid value to the second search identifier; Determining whether there is a collision risk between the vehicle and the obstacle based on the target horizontal coordinate points, the target vertical coordinate points, the first search identifier, and the second search identifier; The determining whether there is a collision risk between the vehicle and the obstacle based on the target horizontal coordinate points, the target vertical coordinate points, the first search identifier, and the second search identifier includes: Determine the horizontal coordinate index interval [lowX, highX] based on the maximum and minimum values ​​of the target horizontal coordinate; Determine the ordinate index interval [lowY, highY] based on the maximum and minimum values ​​of the target ordinate; If it is determined that find_collide_x=1, find_collide_y=1, highX>=lowY and highY>=lowX, it is determined that there is a collision risk between the vehicle and the obstacle; find_collide_x is a first search identifier, and find_collide_y is a second search identifier.

5. The controller according to claim 4, characterized in that The creation unit is specifically used for: Get the length L of the vehicle; For each abscissa point in the second expected abscissa point column, the lower limit of the abscissa interval is determined according to the formula Xoin-L / 2, and the lower limit of the abscissa interval is determined according to the formula Xoin-L / 2. oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [X oin -L / 2,X oin +L / 2].

6. The controller according to claim 4, characterized in that The creation unit is specifically used for: Get the length L of the vehicle; For each abscissa point in the second expected abscissa point column, according to formula Y oin -L / 2 determines the lower limit of the horizontal axis interval, according to the formula Y oin + L / 2 determines the upper limit of the horizontal coordinate interval; the horizontal coordinate interval is [Y oin -L / 2, Y oin +L / 2].

7. A vehicle, characterized in that: The vehicle comprises the controller according to any one of claims 4 to 6.

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