Collision detection method and device

Abstract

The present application discloses a collision detection method and device, which relates to the field of driving control technology. The method is applied to a first smart car, including: in response to the first smart car starting to execute the obstacle avoidance process, obtaining the driving speed, first distance, real-time position and obstacle avoidance end position of the first smart car, as well as the driving speed and real-time position of the second vehicle; based on the driving speed and first distance of the first smart car, obtaining the first time; based on the first time and the driving speed of the second vehicle, obtaining the second distance; based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance, detecting whether there is a collision risk between the first smart car and the second vehicle. The present application realizes the detection of the collision risk between the first smart car and the oncoming vehicle in the adjacent lane during the obstacle avoidance process, so as to avoid the collision between the first smart car and the oncoming vehicle during the obstacle avoidance process, thereby improving the driving safety of the first smart car.

Description

Technical Field

[0001] The present application relates to the field of driving control technology, and in particular to a collision detection method and device. Background Art

[0002] An intelligent driving vehicle (IDV), also known as a self-driving car, driverless car, or autonomous vehicle, is a vehicle that uses a built-in intelligent driving system to autonomously perceive its surroundings and navigate and operate with minimal or no human intervention. In other words, an IDV integrates environmental perception, decision-making, and multi-level assisted driving, utilizing technologies such as computers, modern sensing, information fusion, communications, artificial intelligence, and automatic control.

[0003] With current technology, when it comes to obstacles ahead, smart driving cars use sensors and other sensing devices, such as lidar, millimeter-wave radar, ultrasonic radar, and forward-looking cameras, to detect non-emergent obstacles around the smart driving car, such as potholes, rocks, and other debris. Then, based on the shape, size, speed, direction, and position of the obstacle in the lane, they execute a corresponding obstacle avoidance process to avoid collision with the obstacle ahead, thus achieving pre-emptive avoidance of obstacles and completing the complete smart driving task.

[0004] However, the traffic in actual road conditions is more complicated. When the smart driving car completes the process of circumventing the obstacle in front (obstacle avoidance process), it executes a new bypass trajectory and cannot take into account the collision risk of oncoming vehicles in adjacent lanes. It may collide with the oncoming vehicle, resulting in reduced driving safety of the smart driving car. Summary of the Invention

[0005] Based on the above problems, the present application provides a collision detection method and device, which can detect the risk of collision between an intelligent driving car and an oncoming vehicle in an adjacent lane during the obstacle avoidance process, so as to avoid the collision between the intelligent driving car and the oncoming vehicle during the obstacle avoidance process, thereby improving the driving safety of the intelligent driving car.

[0006] The embodiments of this application disclose the following technical solutions:

[0007] In a first aspect, the present application discloses a collision detection method, applied to a first smart car, the method comprising:

[0008] In response to the first smart car starting to perform an obstacle avoidance process, obtaining the driving speed, first distance, real-time position, and obstacle avoidance completion position of the first smart car, as well as the driving speed and real-time position of a second vehicle; wherein the first distance is the distance traveled by the first smart car after completing the obstacle avoidance process, and the obstacle avoidance completion position is the position of the first smart car when the obstacle avoidance process is completed; and the second vehicle is located in an adjacent lane of the first smart car and traveling in an opposite direction to the first smart car;

[0009] Obtaining a first time based on the driving speed and the first distance of the first smart car; wherein the first time is the time it takes for the first smart car to complete the obstacle avoidance process;

[0010] Obtaining a second distance based on the first time and the travel speed of the second vehicle; wherein the second distance is the travel distance of the second vehicle within the first time;

[0011] Based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance, detect whether there is a collision risk between the first smart car and the second vehicle.

[0012] Optionally, the detecting whether there is a collision risk between the first smart car and the second vehicle based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position, and the second distance includes:

[0013] Obtaining a third distance based on the real-time position of the first smart car and the obstacle avoidance completion position, and calculating the sum of the third distance and the second distance to obtain a collision prediction distance;

[0014] Obtaining a real-time distance between the first smart car and the second vehicle according to the real-time position of the first smart car and the real-time position of the second vehicle;

[0015] determining whether the real-time distance between the first smart car and the second vehicle is greater than the predicted collision distance;

[0016] When the real-time distance between the first smart car and the second vehicle is greater than the collision prediction distance, it is determined that there is no collision risk between the first smart car and the second vehicle;

[0017] When the real-time distance between the first smart car and the second vehicle is not greater than the collision prediction distance, it is determined that there is a collision risk between the first smart car and the second vehicle.

[0018] Optionally, when it is determined that there is a collision risk between the first smart car and the second vehicle, the method further includes:

[0019] terminating the obstacle avoidance process, decelerating and reversing to return to the original lane and parking; wherein the original lane is the lane in which the first smart car was located before starting the obstacle avoidance process.

[0020] Optionally, when it is determined that there is a collision risk between the first smart car and the second vehicle, the method further includes:

[0021] The user is prompted by one or more of the display, voice, and signal lights of the first smart car to indicate that there is a risk of collision between the first smart car and the second vehicle.

[0022] Optionally, when it is determined that there is no collision risk between the first smart car and the second vehicle, the method further includes:

[0023] Continue to execute the obstacle avoidance process for the obstacle.

[0024] Optionally, in response to the first smart car starting to execute the obstacle avoidance process, obtaining the driving speed, first distance, real-time position, and obstacle avoidance completion position of the first smart car, and the driving speed and real-time position of the second vehicle includes:

[0025] In response to the first smart car starting to execute an obstacle avoidance process, detecting whether a second vehicle exists;

[0026] When it is determined that there is a second vehicle, the driving speed, first distance, real-time position and obstacle avoidance end position of the first smart car, as well as the driving speed and real-time position of the second vehicle are obtained.

[0027] Optionally, in response to the first smart car starting to execute the obstacle avoidance process, detecting whether a second vehicle exists includes:

[0028] In response to the first smart car starting to execute an obstacle avoidance process, based on the perception data obtained by the first smart car, detecting whether a second vehicle exists; wherein the perception data includes perception data obtained by one or more perception devices.

[0029] Optionally, the method further includes:

[0030] When it is determined that the second vehicle does not exist, the obstacle avoidance process for the obstacle is directly continued.

[0031] In a second aspect, the present application discloses a collision detection device, applied to a first smart car, comprising:

[0032] a data acquisition module configured to acquire, in response to the first smart vehicle initiating an obstacle avoidance process, a driving speed, a first distance, a real-time position, and a location at the end of the obstacle avoidance process of the first smart vehicle, as well as a driving speed and real-time position of a second vehicle; wherein the first distance is the distance traveled by the first smart vehicle after completing the obstacle avoidance process, and the location at the end of the obstacle avoidance process is the location of the first smart vehicle upon completion of the obstacle avoidance process; and the second vehicle is a vehicle located in an adjacent lane of the first smart vehicle and traveling in an opposite direction to the first smart vehicle;

[0033] a time determination module, configured to obtain a first time based on the driving speed and the first distance of the first smart car; wherein the first time is the time it takes for the first smart car to complete the obstacle avoidance process;

[0034] a distance determination module, configured to obtain a second distance based on the first time and the travel speed of the second vehicle; wherein the second distance is a travel distance of the second vehicle within the first time;

[0035] A collision detection module is used to detect whether there is a collision risk between the first smart car and the second vehicle based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance.

[0036] Optionally, the collision detection module is specifically used to:

[0037] Obtaining a third distance based on the real-time position of the first smart car and the obstacle avoidance completion position, and calculating the sum of the third distance and the second distance to obtain a collision prediction distance;

[0038] Obtaining a real-time distance between the first smart car and the second vehicle according to the real-time position of the first smart car and the real-time position of the second vehicle;

[0039] determining whether the real-time distance between the first smart car and the second vehicle is greater than the predicted collision distance;

[0040] When the real-time distance between the first smart car and the second vehicle is greater than the collision prediction distance, it is determined that there is no collision risk between the first smart car and the second vehicle;

[0041] When the real-time distance between the first smart car and the second vehicle is not greater than the collision prediction distance, it is determined that there is a collision risk between the first smart car and the second vehicle.

[0042] Optionally, the collision detection device further includes an execution module configured to terminate the obstacle avoidance process, slow down and reverse back to the original lane, and stop the vehicle when it is determined that there is a collision risk between the first smart car and the second vehicle.

[0043] Optionally, the execution module is further configured to continue executing the obstacle avoidance process when it is determined that there is no collision risk between the first smart car and the second vehicle.

[0044] Optionally, the collision detection device further includes a prompt module. The prompt module is configured to, upon determining that a collision risk exists between the first smart car and the second vehicle, prompt a user of the collision risk by using one or more of a display, voice, and signal lights on the first smart car.

[0045] Optionally, the data acquisition module is specifically used to detect whether a second vehicle exists in response to the first smart car starting to execute the obstacle avoidance process; when it is determined that a second vehicle exists, obtain the driving speed, first distance, real-time position and obstacle avoidance end position of the first smart car, as well as the driving speed and real-time position of the second vehicle.

[0046] Optionally, the execution module is further configured to directly continue executing the obstacle avoidance process for the obstacle when it is determined that the second vehicle does not exist.

[0047] Compared with the existing technology, the present application has the following beneficial effects: through the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance, the collision risk of the first smart car with the oncoming vehicle (i.e., the second vehicle) in the adjacent lane during the obstacle avoidance process can be detected, so as to avoid the collision of the smart driving car with the oncoming vehicle during the obstacle avoidance process, thereby improving the driving safety of the smart driving car. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0049] Figure 1 A flowchart of a collision detection method provided in an embodiment of the present application;

[0050] Figure 2 An example diagram of the information carried in the obstacle avoidance strategy provided in an embodiment of the present application;

[0051] Figure 3A schematic diagram of a process for detecting whether there is a collision risk between a first smart car and a second vehicle provided in an embodiment of the present application;

[0052] Figure 4 An example diagram of the relationship between the real-time distance and the predicted collision distance between the first smart car and the second vehicle provided in an embodiment of the present application;

[0053] Figure 5 A schematic structural diagram of a collision detection device provided in an embodiment of the present application;

[0054] Figure 6 A schematic structural diagram of a first smart car provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] As previously described, when a smart car circumvents an obstacle ahead (the obstacle avoidance process), it executes a new detour trajectory. However, due to the complex traffic conditions and the blind spots inherent in the smart car's obstacle avoidance, the vehicle cannot account for the collision risk with oncoming vehicles in the lead lane during the obstacle avoidance process. This can lead to a collision with oncoming vehicles, reducing the safety of the smart car. For example, if a smart car is in the obstacle avoidance process and has already entered an adjacent lane according to the obstacle avoidance trajectory, and there is an oncoming vehicle in the adjacent lane at this time, the smart car may collide with the oncoming vehicle during the obstacle avoidance process, causing traffic congestion and even impacting the safety of the smart car's users.

[0056] The present application provides a collision detection method, including: in response to a first smart car starting to execute an obstacle avoidance process, obtaining the driving speed, first distance, real-time position and obstacle avoidance end position of the first smart car, as well as the driving speed and real-time position of the second vehicle; obtaining a first time based on the driving speed and first distance of the first smart car; obtaining a second distance based on the first time and the driving speed of the second vehicle; and detecting whether there is a collision risk between the first smart car and the second vehicle based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance. The present application can detect the collision risk of the first smart car with an oncoming vehicle (i.e., the second vehicle) in an adjacent lane during the obstacle avoidance process through the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance, so as to avoid the collision of the smart driving car with the oncoming vehicle during the obstacle avoidance process, thereby improving the driving safety of the smart driving car.

[0057] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0058] Example 1:

[0059] The following combination Figures 1-4 , a collision detection method provided by an embodiment of the present application is described in detail. Specifically, a collision detection method provided by an embodiment of the present application is applied to a first smart car, which is a smart driving car.

[0060] like Figure 1 As shown, a collision detection method provided in an embodiment of the present application includes the following steps:

[0061] S101: In response to a first smart car starting to execute an obstacle avoidance process, obtaining a driving speed, a first distance, a real-time position, and an obstacle avoidance end position of the first smart car, and a driving speed and real-time position of a second smart car.

[0062] Among them, obstacles refer to all objects that appear in front of the first intelligent vehicle while it is driving and are higher than the height of the laser radar scanning surface.

[0063] The second vehicle is located in an adjacent lane of the first smart car and is traveling in the opposite direction of the first smart car. The adjacent lane is actually the lane that the first smart car needs to drive to when performing the obstacle avoidance process.

[0064] The first distance is the distance traveled by the first smart car to complete the obstacle avoidance process, and the obstacle avoidance end position is the position of the first smart car when the obstacle avoidance process is completed.

[0065] Specifically, when the first intelligent vehicle (intelligent driving vehicle) detects an obstacle ahead of it through its perception module, it can execute a corresponding obstacle avoidance route (strategy) based on information such as the obstacle's shape, size, speed, orientation, and position within the lane, allowing the first intelligent vehicle to execute the corresponding obstacle avoidance process according to the route (strategy). The obstacle avoidance strategy includes the position at which the first intelligent vehicle starts moving to an adjacent lane, the position at which the first intelligent vehicle starts returning to the original lane from the adjacent lane, the position of the first intelligent vehicle upon completion of the obstacle avoidance process, and the distance traveled during the entire obstacle avoidance process.

[0066] For ease of understanding, the following Figure 2 Let's take an example to introduce the information carried in the obstacle avoidance strategy (route). Figure 2In the example, the obstacle avoidance strategy of smart car A for obstacle B is taken as an example.

[0067] like Figure 2 As shown in the figure, in the obstacle avoidance strategy of smart car A around obstacle B, smart car A begins the obstacle avoidance process at position P1 (i.e., begins driving towards the adjacent lane); smart car A begins returning from the adjacent lane to the original lane at position P2; and smart car A completes the entire obstacle avoidance process at position P3, which is the end position of the obstacle avoidance. The lateral distance S1 between positions P1 and P3 (in the direction of smart car A's travel) is the first distance.

[0068] Exemplarily, the obstacle avoidance route can be obtained in the following manner: the perception module on the first smart car senses the state of the surrounding obstacles and its own operating state; then, the self-movement action within the next preset time under its own operating state and the obstacle movement action of the surrounding obstacles within the next preset time are analyzed, and the self-movement action and the obstacle movement action are compared. When it is analyzed that there will be a collision, the obstacle avoidance route is planned, and the self-movement action is automatically adjusted to execute the corresponding obstacle avoidance process.

[0069] It should be noted that the first distance is the distance in the driving direction of the first smart car.

[0070] In one possible implementation, in response to a first smart car initiating an obstacle avoidance process, the presence of a second vehicle is detected. Upon determining the presence of the second vehicle, the first smart car's speed, first distance, real-time location, and obstacle avoidance completion location, as well as the second vehicle's speed and real-time location, are acquired. If the first smart car detects a second vehicle traveling in an oncoming direction in an adjacent lane, this indicates that the oncoming vehicle may affect the first smart car's obstacle avoidance process, i.e., there is a possibility of a collision between the first smart car and the second vehicle during the obstacle avoidance process. Therefore, the first smart car's speed, first distance, and real-time location, as well as the second vehicle's speed and real-time location, are acquired.

[0071] Specifically, in response to the first smart car starting to execute the obstacle avoidance process, based on the perception data obtained by the first smart car, it is detected whether the second vehicle exists.

[0072] Perception data includes data acquired by one or more perception devices. Perception devices are devices designed specifically to perceive the surrounding environment and convert the perception results into useful information. Perception devices include cameras, radars, and lidars.

[0073] Furthermore, if it is determined that the second vehicle does not exist, the obstacle avoidance process is directly continued. Since the second vehicle does not exist, there is no oncoming vehicle that will affect the obstacle avoidance process of the first smart car, so the obstacle avoidance process can be directly continued.

[0074] S102: Obtain a first time based on the driving speed of the first smart car and the first distance.

[0075] Among them, the first time is the time when the first smart car completes the obstacle avoidance process.

[0076] Specifically, through the basic definition of speed, the first time is obtained based on the driving speed of the first smart car and the first distance.

[0077] Exemplarily, the first time is obtained by formula (1).

[0078] T1=S1 / V1 (1)

[0079] Among them, T1 is the first time, S1 is the first distance, and V1 is the driving speed of the first smart car.

[0080] S103: Obtain a second distance based on the first time and the driving speed of the second vehicle.

[0081] The second distance is the travel distance of the second vehicle within the first time.

[0082] Specifically, through the basic definition of speed, the second distance is obtained based on the first time and the driving speed of the second vehicle.

[0083] Exemplarily, the second distance is obtained by formula (2).

[0084] S2=V2*T1 (2)

[0085] Wherein, S2 is the second distance, V2 is the speed of the second vehicle, and T1 is the first time.

[0086] S104: Detect whether there is a collision risk between the first smart car and the second vehicle based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance completion position, and the second distance.

[0087] For ease of understanding, the following Figure 3 and Figure 4 The following describes in detail how to detect whether there is a collision risk between the first smart car and the second vehicle.

[0088] like Figure 3 As shown, S104 in the embodiment of the present application specifically includes the following steps:

[0089] S301. Obtain a third distance based on the real-time position of the first smart car and the obstacle avoidance completion position; and calculate the sum of the third distance and the second distance to obtain a collision prediction distance.

[0090] The collision prediction distance is a condition used to detect whether there is a collision risk between the first smart car and the second vehicle. That is, the collision prediction distance is the pre-estimated shortest distance between the first smart car and the second vehicle without a collision.

[0091] S302: Obtain a real-time distance between the first smart car and the second vehicle based on the real-time position of the first smart car and the real-time position of the second vehicle.

[0092] The real-time distance is the distance between the first smart car and the second vehicle in the direction of travel. For example, if the first smart car is traveling eastward and the second vehicle is traveling in the opposite direction, that is, westward, then the direction of travel between the first smart car and the second vehicle is east-west, and the real-time distance is the east-west distance between the first smart car and the second vehicle.

[0093] It should be noted that there is no sequential order for the above S301 and S302. S301 may be executed first and then S302, or S302 may be executed first and then S301, or S301 and S302 may be executed simultaneously.

[0094] S303: Determine whether the real-time distance between the first smart car and the second vehicle is greater than the predicted collision distance.

[0095] When the real-time distance between the first smart car and the second vehicle is greater than the predicted collision distance, proceed to S304.

[0096] When the real-time distance between the first smart car and the second vehicle is not greater than the predicted collision distance, step S305 is continued.

[0097] S304: Determine that there is no collision risk between the first smart car and the second vehicle.

[0098] When the real-time distance between the first smart car and the second vehicle is greater than the predicted collision distance, it indicates that the real-time distance between the first smart car and the second vehicle is greater than the pre-estimated minimum distance without a collision. Therefore, there is no collision risk between the first smart car and the second vehicle after the first smart car completes the obstacle avoidance process. Alternatively, this means that when the first smart car completes the obstacle avoidance process, the second vehicle has not yet reached the end point of the obstacle avoidance process, so there is no collision risk between the first smart car and the second vehicle.

[0099] In a possible implementation, when it is determined that there is no collision risk between the first smart car and the second smart car, the obstacle avoidance process is directly executed.

[0100] S305: Determine whether there is a collision risk between the first smart car and the second smart car.

[0101] When the real-time distance between the first smart car and the second vehicle is not greater than the predicted collision distance, it means that the real-time distance between the first smart car and the second vehicle is not greater than the pre-estimated shortest distance without a collision. Specifically, when the real-time distance between the first smart car and the second vehicle is less than the predicted collision distance, the real-time distance between the first smart car and the second vehicle cannot support the completion of the obstacle avoidance process without a collision, that is, there is a risk of collision; when the real-time distance between the first smart car and the second vehicle is equal to the predicted collision distance, the real-time distance between the first smart car and the second vehicle barely supports the completion of the obstacle avoidance process without a collision, but there is still the possibility of a minor collision. It can also be understood that when the first smart car completes the obstacle avoidance process, the second vehicle has already reached the obstacle avoidance end position, that is, the first smart car has not yet completed the obstacle avoidance process, and the second vehicle has already reached the obstacle avoidance end position. If the first smart car continues to execute the obstacle avoidance process, it will collide with the second vehicle.

[0102] For ease of understanding, the following Figure 4 , an example is given to introduce the relationship between the real-time distance between the first smart car and the second vehicle and the collision prediction distance.

[0103] like Figure 4 As shown, vehicle 401 is a first smart car, vehicle 402 is a second vehicle, and there is an obstacle 403. The first distance in the driving direction corresponding to vehicle 401's circumvention of obstacle 403 is S1, and the obstacle circumvention end position 404 is obtained. The first time corresponding to the obstacle circumvention process is calculated based on the driving speed of vehicle 401 and the first distance S1. The second distance S2 traveled by vehicle 402 during the obstacle circumvention process is calculated based on the driving speed of vehicle 402 and the first time. The distance between vehicle 401's real-time position and obstacle circumvention end position 404 is a third distance S3. Therefore, the shortest distance between vehicle 401 and vehicle 402 during the obstacle circumvention process without a collision is S4, which is the predicted collision distance. When the real-time distance d between vehicle 401 and vehicle 402 is less than or equal to S4, it indicates that vehicle 401 and vehicle 402 overlap or touch at a certain position, and there is a collision risk. That is, when vehicle 401 is in the obstacle avoidance process, vehicle 402 has already reached the obstacle avoidance end position 404. If vehicle 401 continues to perform the obstacle avoidance process, it will collide with vehicle 402. When the real-time distance d between vehicle 401 and vehicle 402 is greater than S4, it indicates that vehicle 401 will not overlap or touch vehicle 402 during the obstacle avoidance process, and there is no collision risk. That is, when vehicle 401 has completed the obstacle avoidance process, vehicle 402 has not yet reached the obstacle avoidance end position 404, and there will be no collision between vehicle 401 and vehicle 402.

[0104] In one possible implementation, when a collision risk is determined between the first smart car and the second vehicle, the obstacle avoidance process is terminated, the vehicle decelerates and reverses back to the original lane and stops. The original lane is the lane the first smart car was in before the obstacle avoidance process began. For example, if a collision risk is determined between the first smart car and the second vehicle, the distance the first smart car needs to reverse is calculated so that the first smart car can automatically reverse.

[0105] Furthermore, after the second vehicle has reached the collision risk with the first smart car, the obstacle avoidance process is automatically restarted. For example, when the second vehicle has passed the location where the first smart car was parked, the first smart car automatically restarts its own obstacle avoidance process.

[0106] In one possible implementation, when a collision risk is determined between the first smart car and the second vehicle, the user is notified of the collision risk between the first smart car and the second vehicle through one or more of the first smart car's display, voice, and signal lights. For example, the first smart car may display a prompt icon on its onboard display screen to notify the user of the collision between the first smart car and the second vehicle; the first smart car may directly broadcast a prompt message (e.g., "there is a collision risk ahead") to notify the user of the collision between the first smart car and the second vehicle; or the first smart car may turn on its signal lights and put them in a double flash state to notify the user of the collision between the first smart car and the second vehicle.

[0107] An embodiment of the present application provides a collision detection method, including: in response to a first smart car starting to execute an obstacle avoidance process, obtaining the driving speed, first distance, real-time position and obstacle avoidance end position of the first smart car, as well as the driving speed and real-time position of the second vehicle; obtaining a first time based on the driving speed and first distance of the first smart car; obtaining a second distance based on the first time and the driving speed of the second vehicle; and detecting whether there is a collision risk between the first smart car and the second vehicle based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance. The embodiment of the present application can detect the collision risk of the first smart car with an oncoming vehicle (i.e., the second vehicle) in an adjacent lane during the obstacle avoidance process through the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance, so as to avoid the collision of the smart driving car with the oncoming vehicle during the obstacle avoidance process, thereby improving the driving safety of the smart driving car.

[0108] Furthermore, when a collision risk is detected between the first smart car and the second vehicle, the first smart car itself is controlled to reverse and return to the original lane, thereby avoiding a collision with the second vehicle, thereby improving the driving safety and reliability of the smart driving car.

[0109] Example 2:

[0110] The following combination Figure 5 , a collision detection device provided in an embodiment of the present application is introduced in detail, and the collision detection device is applied to the first smart car.

[0111] like Figure 5 As shown, a collision detection device provided in an embodiment of the present application includes the following modules:

[0112] A data acquisition module 501 is configured to acquire, in response to the first smart car starting to execute an obstacle avoidance process, a driving speed, a first distance, a real-time position, and an obstacle avoidance completion position of the first smart car, as well as a driving speed and real-time position of the second smart car;

[0113] The time determination module 502 is configured to obtain a first time based on the driving speed of the first smart car and the first distance; wherein the first time is the time it takes for the first smart car to complete the obstacle avoidance process;

[0114] The distance determination module 503 is configured to obtain a second distance based on the first time and the travel speed of the second vehicle; wherein the second distance is the travel distance of the second vehicle within the first time;

[0115] The collision detection module 504 is used to detect whether there is a collision risk between the first smart car and the second vehicle based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance.

[0116] In one possible implementation, the collision detection module 504 is specifically used to obtain a third distance based on the real-time position of the first smart car and the obstacle avoidance end position, and calculate the sum of the third distance and the second distance to obtain a collision prediction distance; obtain the real-time distance between the first smart car and the second vehicle according to the real-time position of the first smart car and the real-time position of the second vehicle; determine whether the real-time distance between the first smart car and the second vehicle is greater than the collision prediction distance; when the real-time distance between the first smart car and the second vehicle is greater than the collision prediction distance, determine that there is no collision risk between the first smart car and the second vehicle; when the real-time distance between the first smart car and the second vehicle is not greater than the collision prediction distance, determine that there is a collision risk between the first smart car and the second vehicle.

[0117] In one possible implementation, the collision detection device further includes an execution module configured to terminate the obstacle avoidance process, slow down and reverse back to the original lane, and stop the vehicle when determining that there is a collision risk between the first smart car and the second vehicle.

[0118] In a possible implementation, the execution module is further configured to, when it is determined that there is no collision risk between the first smart car and the second vehicle, continue to execute the obstacle avoidance process.

[0119] In one possible implementation, the collision detection device further includes a prompting module. The prompting module is configured to, upon determining that a collision risk exists between the first smart car and the second vehicle, notify a user of the collision risk by using one or more of a display, voice, and signal lights on the first smart car.

[0120] In one possible implementation, the data acquisition module 501 is specifically used to detect whether a second vehicle exists in response to the first smart car starting to execute an obstacle avoidance process; when it is determined that a second vehicle exists, obtain the driving speed, first distance, real-time position and obstacle avoidance end position of the first smart car, as well as the driving speed and real-time position of the second vehicle.

[0121] In a possible implementation, the execution module is further configured to directly continue executing the obstacle avoidance process for the obstacle when it is determined that the second vehicle does not exist.

[0122] The embodiment of the present application provides a collision detection device, including: a data acquisition module 501, for obtaining the driving speed, first distance, real-time position and obstacle avoidance end position of the first smart car, as well as the driving speed and real-time position of the second vehicle in response to the first smart car starting to execute the obstacle avoidance process; a time determination module 502, for obtaining the first time based on the driving speed and the first distance of the first smart car; a distance determination module 503, for obtaining the second distance based on the first time and the driving speed of the second vehicle; a collision detection module 504, for detecting whether there is a collision risk between the first smart car and the second vehicle based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance. The embodiment of the present application can detect the collision risk between the first smart car and the oncoming vehicle (i.e., the second vehicle) in the adjacent lane during the obstacle avoidance process, so as to avoid the collision between the smart driving car and the oncoming vehicle during the obstacle avoidance process, thereby improving the driving safety of the smart driving car.

[0123] Furthermore, the contact detection device also includes an execution module. When a collision risk is detected between the first smart car and the second vehicle, it controls the first smart car to reverse, return to the original lane and stop, thereby avoiding a collision with the second vehicle, thereby improving the driving safety and reliability of the smart driving car.

[0124] Example 3:

[0125] The following combination Figure 6 , a detailed introduction to the system structure of the first smart car provided in an embodiment of the present application.

[0126] like Figure 6 As shown, the first smart car provided in the embodiment of the present application includes: a perception and positioning unit 610, a control unit 620 and an execution unit 630.

[0127] The perception and positioning unit 610 is a crucial component of the intelligent vehicle, providing it with the necessary information to achieve autonomous driving. This unit, which includes both positioning and perception functions, is the foundation for autonomous driving. The intelligent vehicle acquires multiple sensory data through the unit, integrating them to achieve a comprehensive understanding of its surroundings and accurate positioning, thus providing a basis for vehicle decision-making and control.

[0128] Specifically, the sensing and positioning unit 610 includes: a sensing unit 611 and a positioning unit 612 .

[0129] The perception module 611 is used to detect and identify the environment surrounding the first smart car. That is, the perception module 611 can obtain the position, shape, speed, trajectory, lane width, and driving boundary of obstacles, and transmit the above information to the control unit 620.

[0130] Illustratively, the perception unit 611 includes, but is not limited to, a laser radar (LIDAR), a front camera (FCam), a radar, and an ultrasonic sensor system. The laser radar uses laser pulses to create a three-dimensional model of the surrounding environment for accurate distance measurement; the front camera is a camera installed at the front of the vehicle, used to capture visual information in front of the vehicle; the radar uses radio waves to detect the position, speed, and other information of an object; and the ultrasonic sensor uses ultrasound for short-range detection.

[0131] The localization module 612 is used to determine the precise location of the first smart car in the geographic space. That is, the localization module 612 can obtain its own positioning coordinates, speed and heading, and the obstacle avoidance area judgment value of the track route.

[0132] Illustratively, the positioning unit 612 includes, but is not limited to, a high-definition map (HDMap), a global positioning system (GPS), and an inertial measurement unit (IMU). The HDMap is an important component for achieving precise vehicle positioning and path planning. The HDMap contains precise geographic information required for autonomous driving, such as lane markings and traffic signs. The GPS is used to determine the vehicle's position through signals provided by satellites. The IMU is used to measure the vehicle's motion state through accelerometers and gyroscopes, thereby assisting in vehicle positioning.

[0133] Among them, the control unit 620 is one of the core components for realizing the autonomous driving function of the first smart car. It is responsible for formulating appropriate driving strategies based on the perception data (e.g., environmental information and location information) provided by the perception and positioning unit 610, and controlling the execution unit 630 to implement these strategies. That is, the control unit 620 obtains the perception data from the perception and positioning unit 610, and after processing, generates control instructions to make the vehicle drive according to the predetermined target. In the embodiment of the present application, the control unit 620 is used to execute the collision detection method shown in Example 1.

[0134] Specifically, the control unit 620 sends a control instruction to the execution unit 630 via the CAN line. For example, when the control unit 620 determines that there is a collision risk between the first smart car and the second vehicle, it sends a reverse instruction to the execution unit 630 via the CAN line, and the execution unit 630 controls the first smart car to reverse back to the original lane.

[0135] CAN (Controller Area Network) is a serial communication protocol used for communication between electronic systems within a vehicle. It allows various electronic control units within the vehicle to exchange information, thereby achieving coordinated operation within the vehicle.

[0136] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the method and device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. The method and device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0137] The above is merely one specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A collision detection method, characterized in that: Applied to a first smart car, the method includes: In response to the first smart car starting to perform an obstacle avoidance process, obtaining the driving speed, first distance, real-time position, and obstacle avoidance completion position of the first smart car, as well as the driving speed and real-time position of a second vehicle; wherein the first distance is the distance in the driving direction of the first smart car when the obstacle avoidance process is completed, and the obstacle avoidance completion position is the position of the first smart car when the obstacle avoidance process is completed; the second vehicle is located in an adjacent lane of the first smart car and is traveling in the opposite direction of the first smart car; Obtaining a first time based on the driving speed and the first distance of the first smart car; wherein the first time is the time it takes for the first smart car to complete the obstacle avoidance process; Obtaining a second distance based on the first time and the travel speed of the second vehicle; wherein the second distance is the travel distance of the second vehicle within the first time; Based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance, detect whether there is a collision risk between the first smart car and the second vehicle.

2. The method according to claim 1, characterized in that The detecting whether there is a collision risk between the first smart car and the second vehicle based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position, and the second distance includes: Obtaining a third distance based on the real-time position of the first smart car and the obstacle avoidance completion position, and calculating the sum of the third distance and the second distance to obtain a collision prediction distance; Obtaining a real-time distance between the first smart car and the second vehicle according to the real-time position of the first smart car and the real-time position of the second vehicle; determining whether the real-time distance between the first smart car and the second vehicle is greater than the predicted collision distance; When the real-time distance between the first smart car and the second vehicle is greater than the collision prediction distance, it is determined that there is no collision risk between the first smart car and the second vehicle; When the real-time distance between the first smart car and the second vehicle is not greater than the collision prediction distance, it is determined that there is a collision risk between the first smart car and the second vehicle.

3. The method according to claim 2, characterized in that When it is determined that there is a collision risk between the first smart car and the second vehicle, the method further includes: terminating the obstacle avoidance process, decelerating and reversing to return to the original lane and parking; wherein the original lane is the lane in which the first smart car was located before starting the obstacle avoidance process.

4. The method according to claim 2, characterized in that When it is determined that there is a collision risk between the first smart car and the second vehicle, the method further includes: The user is prompted by one or more of the display, voice, and signal lights of the first smart car to indicate that there is a risk of collision between the first smart car and the second vehicle.

5. The method according to claim 2, characterized in that When it is determined that there is no collision risk between the first smart car and the second vehicle, the method further includes: Continue to execute the obstacle avoidance process for the obstacle.

6. The method according to claim 1, wherein In response to the first smart car starting to execute the obstacle avoidance process, obtaining the driving speed, first distance, real-time position and obstacle avoidance end position of the first smart car, and the driving speed and real-time position of the second vehicle, including: In response to the first smart car starting to execute an obstacle avoidance process, detecting whether a second vehicle exists; When it is determined that there is a second vehicle, the driving speed, first distance, real-time position and obstacle avoidance end position of the first smart car, as well as the driving speed and real-time position of the second vehicle are obtained.

7. The method according to claim 6, characterized in that The detecting whether a second vehicle exists in response to the first smart car starting to execute the obstacle avoidance process includes: In response to the first smart car starting to execute an obstacle avoidance process, based on the perception data obtained by the first smart car, detecting whether a second vehicle exists; wherein the perception data includes perception data obtained by one or more perception devices.

8. The method according to claim 6, characterized in that The method further comprises: When it is determined that the second vehicle does not exist, the obstacle avoidance process for the obstacle is directly continued.

9. A collision detection device, characterized in that: Applied to a first smart car, the device includes: a data acquisition module configured to acquire, in response to the first smart vehicle initiating an obstacle avoidance process, the driving speed, first distance, real-time position, and obstacle avoidance completion position of the first smart vehicle, as well as the driving speed and real-time position of a second vehicle; wherein the first distance is the distance in the driving direction of the first smart vehicle at the completion of the obstacle avoidance process, and the obstacle avoidance completion position is the position of the first smart vehicle at the completion of the obstacle avoidance process; and the second vehicle is a vehicle located in an adjacent lane of the first smart vehicle and traveling in the opposite direction of the first smart vehicle; a time determination module, configured to obtain a first time based on the driving speed and the first distance of the first smart car; wherein the first time is the time it takes for the first smart car to complete the obstacle avoidance process; a distance determination module, configured to obtain a second distance based on the first time and the travel speed of the second vehicle; wherein the second distance is a travel distance of the second vehicle within the first time; A collision detection module is used to detect whether there is a collision risk between the first smart car and the second vehicle based on the real-time position of the first smart car, the real-time position of the second vehicle, the obstacle avoidance end position and the second distance.

10. The device according to claim 9, characterized in that The collision detection module is specifically used to: Obtaining a third distance based on the real-time position of the first smart car and the obstacle avoidance completion position, and calculating the sum of the third distance and the second distance to obtain a collision prediction distance; Obtaining a real-time distance between the first smart car and the second vehicle according to the real-time position of the first smart car and the real-time position of the second vehicle; determining whether the real-time distance between the first smart car and the second vehicle is greater than the predicted collision distance; When the real-time distance between the first smart car and the second vehicle is greater than the collision prediction distance, it is determined that there is no collision risk between the first smart car and the second vehicle; When the real-time distance between the first smart car and the second vehicle is not greater than the collision prediction distance, it is determined that there is a collision risk between the first smart car and the second vehicle.

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