Vehicle collaborative obstacle avoidance method based on Internet of Vehicles

Abstract

The invention discloses a vehicle cooperative obstacle avoidance method based on Internet of Vehicles. The method comprises the following steps: respectively acquiring a GPS position signal of a vehicle, a GPS position signal of a front vehicle, a steering wheel angle signal of the front vehicle and a steering signal of the front vehicle; when the steering signal of the front vehicle is non-steering, calculating a mean value and a standard deviation of a steering wheel corner signal of the front vehicle, and identifying a steering wheel operation state of the front vehicle according to the mean value and the standard deviation; when the operation state of the steering wheel of the current vehicle is recognized as continuous deviation, track fitting is conducted according to the GPS position signal of the current vehicle and the GPS position signal of the front vehicle, and whether the front vehicle has a lane changing behavior or not and the lane changing degree of the front vehicle are judged according to the distance between the GPS position of the front vehicle and the fitting track of the current vehicle and the spatial position relation of the two fitting tracks; and according to the judgment result of the lane changing behavior and the lane changing degree of the front vehicle, a braking electric control instruction of the front vehicle is generated, and obstacle avoidance is conducted according to the braking electric control instruction.

Description

Technical Field

[0001] This invention belongs to the field of vehicle cooperative technology, and in particular relates to a vehicle cooperative obstacle avoidance method based on the Internet of Vehicles. Background Technology

[0002] Vehicle driving safety is of paramount importance. Among numerous traffic accidents, rear-end collisions frequently occur when a sudden situation arises ahead (such as a vehicle breaking down, a road collapse, or a fallen obstacle), causing the vehicle in front to abruptly change lanes to avoid it, resulting in the following vehicle not having enough time to brake. Most of these accidents happen because the following vehicle is obstructed by the vehicle in front, unable to see the road conditions ahead. When the vehicle in front notices the sudden situation ahead and changes lanes abruptly, the following vehicle is unable to react in time and a collision occurs.

[0003] With the rapid development and commercial application of vehicle-to-everything (V2X) technology, collaborative obstacle avoidance based on V2X is a novel solution. Therefore, this invention proposes a collaborative obstacle avoidance method based on V2X, which quickly predicts the lane-changing behavior of the vehicle in front by acquiring the driving data of both the preceding and current vehicles, and then takes braking measures to collaboratively avoid obstacles. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a vehicle cooperative obstacle avoidance method based on vehicle-to-everything (V2X) technology. This method acquires vehicle driving data of the preceding vehicle and the current vehicle, quickly predicts the lane-changing behavior of the preceding vehicle, and then takes braking measures to achieve cooperative obstacle avoidance.

[0005] To achieve the above objectives, the present invention provides a vehicle cooperative obstacle avoidance method based on vehicle-to-everything (V2X) communication, comprising: The GPS position signal of this vehicle, the GPS position signal of the vehicle in front, the steering wheel angle signal of the vehicle in front, and the turning signal of the vehicle in front are acquired respectively. When the steering signal of the vehicle in front is no steering, calculate the mean and standard deviation of the steering wheel angle signal of the vehicle in front, and identify the steering wheel operation state of the vehicle in front based on the mean and standard deviation. When the steering wheel operation status of the vehicle in front is identified as continuous deviation, the vehicle performs trajectory fitting based on the GPS position signals of both the vehicle in front and the vehicle in front. The vehicle in front is then judged to have changed lanes and to what extent, based on the distance between the GPS position of the vehicle in front and the fitted trajectory of the vehicle in front, as well as the spatial relationship between the two fitted trajectories. Based on the judgment of the preceding vehicle's lane-changing behavior and the degree of lane-changing, the vehicle generates a braking electronic control command and performs obstacle avoidance according to the braking electronic control command.

[0006] Optionally, calculating the mean and standard deviation of the steering wheel angle signal of the preceding vehicle, and identifying the steering wheel operation state of the preceding vehicle based on the mean and standard deviation, includes: The steering wheel angle signal and GPS position signal of the vehicle and the vehicle in front are converted into digital sampled values ​​and then stored sequentially in the sliding window vector of the corresponding electronic control unit; Based on the steering wheel angle sampling values ​​of the preceding vehicle in the sliding window vector, the mean and standard deviation of the steering wheel angle sampling values ​​of the preceding vehicle are calculated, and the deviation of the newly collected sampling values ​​from the mean is compared with three times the standard deviation to identify the steering wheel operation state of the preceding vehicle.

[0007] Optionally, identifying the steering wheel operation status of the vehicle in front based on the mean and standard deviation includes: When the absolute difference between the newly collected sampling point and the mean is not less than three times the standard deviation, it is determined that the steering wheel of the vehicle in front has deviated; otherwise, the sliding window vector is updated. After the steering wheel of the preceding vehicle deviates from its position, a second and third sampling point are collected. The deviation is determined based on the second and third sampling points, and the steering wheel operation status of the preceding vehicle is identified based on the deviation determination result.

[0008] Optionally, the deviation determination for the second and third sampling points includes: If the absolute difference between at least one of the second and third sampling points and the mean is not less than three times the standard deviation, then the steering wheel state of the vehicle in front is identified as continuously deviating; otherwise, the sliding window vector is updated.

[0009] Optionally, updating the sliding window vector includes: When the absolute difference between at least two of the three consecutive sampling points and the mean is less than three times the standard deviation, the corresponding sampling points are added to the end of the sliding window vector, and a preset number of sampling points at the beginning of the vector are deleted to maintain a constant vector length.

[0010] Optionally, determining whether the preceding vehicle changed lanes and the degree of lane change based on the distance between the preceding vehicle's GPS location and the fitted trajectory of this vehicle, as well as the spatial relationship between the two fitted trajectories, includes: The least-squares straight-line fitting is performed on the GPS position sampled values ​​in the vehicle's sliding window vector to obtain the vehicle's driving reference straight-line equation. Calculate the perpendicular distances from the three most recent GPS location sampling points of the preceding vehicle to the reference straight line equation of the current vehicle. When any one of the perpendicular distances is not less than half of the preset lane width, it is determined that the preceding vehicle has changed lanes and deviated from its original lane.

[0011] Optionally, determining whether the vehicle in front has changed lanes and the extent of the lane change also includes: The equation of the straight line in the direction of travel of the vehicle ahead is obtained by performing least-squares straight-line fitting on the three most recent GPS position samples of the vehicle ahead. Calculate the angle between the straight line equation of the preceding vehicle's driving direction and the straight line equation of the current vehicle's driving reference. When the angle is greater than a preset angle threshold, it is determined that the preceding vehicle has changed lanes but has not yet deviated from its original lane. When the included angle is not greater than a preset angle threshold, it is determined that the vehicle in front is driving normally and there is no lane change behavior.

[0012] Optionally, if it is determined that the vehicle has changed lanes and deviated from its original lane, the vehicle shall take emergency braking measures, with an acceleration of generally -5 to -7 m / s². If it is determined that the vehicle has changed lanes but has not deviated from its original lane, the vehicle will take normal braking measures, with an acceleration of -3 to -5 m / s². If it is determined that the vehicle is driving normally and there is no lane-changing behavior, then the sliding window vector is updated.

[0013] Compared with the prior art, the present invention has the following advantages and technical effects: This invention proposes a novel solution to the common problem of collisions caused by a following vehicle suddenly changing lanes without using its turn signal. Leveraging vehicle-to-everything (V2X) technology, this invention analyzes vehicle data from both the current vehicle and the vehicle in front. Using only a small number of sampling points, it can quickly identify two types of behavior: a vehicle changing lanes and deviating from its original lane, and a vehicle changing lanes but not yet deviating from its original lane. This allows the following vehicle to take timely emergency braking or normal braking strategies accordingly, preventing collisions caused by sudden lane changes. This invention is particularly important for autonomous vehicles on straight roads, enabling timely braking intervention. Compared to existing solutions based on video and other technologies, this solution is unaffected by weather, obstructions, or lighting conditions, boasts high recognition accuracy, and features a simple, practical, efficient, and fast algorithm, allowing the following vehicle to take immediate braking measures to avoid collisions. Attached Figure Description

[0014] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of a vehicle cooperative obstacle avoidance method based on the Internet of Vehicles according to an embodiment of the present invention. Detailed Implementation

[0015] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0016] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0017] This embodiment proposes a vehicle cooperative obstacle avoidance method based on vehicle-to-everything (V2X) communication, such as... Figure 1 As shown, the specific steps include: The GPS position signal of this vehicle, the GPS position signal of the vehicle in front, the steering wheel angle signal of the vehicle in front, and the turning signal of the vehicle in front are acquired respectively. When the steering signal of the vehicle in front is no steering, calculate the mean and standard deviation of the steering wheel angle signal of the vehicle in front, and identify the steering wheel operation state of the vehicle in front based on the mean and standard deviation. When the steering wheel operation status of the vehicle in front is identified as continuous deviation, the vehicle performs trajectory fitting based on the GPS position signals of both the vehicle in front and the vehicle in front. The vehicle in front is then judged to have changed lanes and to what extent, based on the distance between the GPS position of the vehicle in front and the fitted trajectory of the vehicle in front, as well as the spatial relationship between the two fitted trajectories. Based on the judgment of the preceding vehicle's lane-changing behavior and the degree of lane-changing, the vehicle generates a braking electronic control command and performs obstacle avoidance according to the braking electronic control command.

[0018] Further, calculating the mean and standard deviation of the steering wheel angle signal of the preceding vehicle, and identifying the steering wheel operation state of the preceding vehicle based on the mean and standard deviation includes: The steering wheel angle signal and GPS position signal of the vehicle and the vehicle in front are converted into digital sampled values ​​and then stored sequentially in the sliding window vector of the corresponding electronic control unit; Based on the steering wheel angle sampling values ​​of the preceding vehicle in the sliding window vector, the mean and standard deviation of the steering wheel angle sampling values ​​of the preceding vehicle are calculated, and the deviation of the newly collected sampling values ​​from the mean is compared with three times the standard deviation to identify the steering wheel operation state of the preceding vehicle.

[0019] Furthermore, identifying the steering wheel operation status of the vehicle in front based on the mean and standard deviation includes: When the absolute difference between the newly collected sampling point and the mean is not less than three times the standard deviation, it is determined that the steering wheel of the vehicle in front has deviated; otherwise, the sliding window vector is updated. After the steering wheel of the preceding vehicle deviates from its position, a second and third sampling point are collected. The deviation is determined based on the second and third sampling points, and the steering wheel operation status of the preceding vehicle is identified based on the deviation determination result.

[0020] Furthermore, the deviation determination for the second and third sampling points includes: If the absolute difference between at least one of the second and third sampling points and the mean is not less than three times the standard deviation, then the steering wheel state of the vehicle in front is identified as continuously deviating; otherwise, the sliding window vector is updated.

[0021] Furthermore, updating the sliding window vector includes: When the absolute difference between at least two of the three consecutive sampling points and the mean is less than three times the standard deviation, the corresponding sampling points are added to the end of the sliding window vector, and a preset number of sampling points at the beginning of the vector are deleted to maintain a constant vector length.

[0022] Furthermore, based on the distance between the GPS location of the preceding vehicle and the fitted trajectory of this vehicle, and the spatial relationship between the two fitted trajectories, it is determined whether the preceding vehicle has changed lanes and the degree of lane change, including: The least-squares straight-line fitting is performed on the GPS position sampled values ​​in the vehicle's sliding window vector to obtain the vehicle's driving reference straight-line equation. Calculate the perpendicular distances from the three most recent GPS location sampling points of the preceding vehicle to the reference straight line equation of the current vehicle. When any one of the perpendicular distances is not less than half of the preset lane width, it is determined that the preceding vehicle has changed lanes and deviated from its original lane.

[0023] Furthermore, determining whether the vehicle in front changed lanes and the extent of the lane change also includes: The equation of the straight line in the direction of travel of the vehicle ahead is obtained by performing least-squares straight-line fitting on the three most recent GPS position samples of the vehicle ahead. Calculate the angle between the straight line equation of the preceding vehicle's driving direction and the straight line equation of the current vehicle's driving reference. When the angle is greater than a preset angle threshold, it is determined that the preceding vehicle has changed lanes but has not yet deviated from its original lane. When the included angle is not greater than a preset angle threshold, it is determined that the vehicle in front is driving normally and there is no lane change behavior.

[0024] Furthermore, if it is determined that the vehicle has changed lanes and deviated from its original lane, the vehicle will take emergency braking measures, with an acceleration of generally -5 to -7 m / s². If it is determined that the vehicle has changed lanes but has not deviated from its original lane, the vehicle will take normal braking measures, with an acceleration of -3 to -5 m / s². If it is determined that the vehicle is driving normally and there is no lane-changing behavior, then the sliding window vector is updated.

[0025] like Figure 1 As shown, this embodiment specifically includes the following main steps: Step 1: Both this vehicle and the vehicle in front are equipped with network communication devices. This vehicle can obtain its own GPS location data, as well as the steering wheel angle data, steering signal data and GPS location data of the vehicle in front. The data sampling frequency can be set to 10HZ, that is, 10 sampling points can be collected within one second. Step 2: Combine the sampling data acquired by the vehicle in Step 1 into a vector. For example, the GPS position vector of the vehicle is defined as follows: The steering wheel angle vector of the vehicle in front is defined as follows: The GPS position vector of the vehicle in front is defined as The sampling points are placed in order from 1 to N. The value of N can be set according to actual experience, for example, N can be 5. Step 3: Calculate the average steering wheel angle of the vehicle in front. and standard deviation The calculation formula is as follows: ; ; When collecting the next sampling point, calculate Determine if it is less than If the deviation is less than 3 times the standard deviation, it is considered that the steering wheel angle has not deviated significantly and the vehicle is running stably. Update the vector in step 2. , , Repeat step 3; Step 4, if If the value is greater than or equal to 3 standard deviations, it is considered that there may be a steering action in the steering wheel angle, and the vector in step 2 is not updated for the time being; continue to collect the (N+2)th and (N+3)th sampling points; if , All less than If the N+1th sampling point is considered an outlier, it is removed, and the vector in step 2 is updated. , , ; Step 5, if , At least one of them is greater than or equal to If the vehicle is suspected of lane changing, the vector in step 2 is not updated. Instead, a new vector is created by combining the N+1, N+2, and N+3 sampling points. , , ; For example, ,but , , like , Then update the vector in step 2, that is... , ; like , If so, do not update the vector in step 2 for now, and continue collecting data. and ;like 2, , Then it is believed If it is an outlier, remove it and update the vector in step 2. , ;like 2, If the vehicle is suspected of lane changing, the vector in step 2 will not be updated, and the following steps will continue. Step 6: Move this vehicle The least-squares line fitting is performed on the position points to obtain the equation of the fitted line for the vehicle. The equation of this straight line reflects the current trajectory of the vehicle. Calculation The perpendicular distances from the three points to the fitted line equation are obtained. These three distances correspond to the lateral distances from the following vehicle's trajectory when the steering wheel angle of the preceding vehicle changes significantly. Step 7: Let the lane width be L (for example, L is 3.5 meters). There exists a greater than or equal to If the value is , it is determined that the vehicle has changed lanes and deviated from its original lane, and the vehicle will brake suddenly to avoid the obstacle; if The values ​​are all less than If the vehicle has not deviated from its original lane, it is considered that the vehicle may still be changing lanes (in step 5, the absolute value of the difference between the steering wheel angle and the mean value has at least two values ​​greater than or equal to 3 times the standard deviation). In this case, step 8 is executed to continue the judgment. For example, if , = ,because > If so, it is determined that the vehicle has changed lanes and deviated from its original lane; if 1.4, since all values ​​are less than 1.75, proceed to step 8 to continue the determination.

[0026] Step 8: Move the front vehicle The least-squares line fitting is performed on the position point to obtain the equation of the fitted line of the preceding vehicle. Calculate the angle between it and the fitted straight line equation of the vehicle. The system determines whether the value is less than a preset threshold, such as 10 degrees. If it is greater than the threshold, it indicates that the vehicle in front has changed lanes but has not yet deviated from its original lane, and the vehicle brakes normally to avoid the obstacle. If it is less than or equal to the threshold, it indicates that the vehicle in front is driving normally and has not changed lanes, and the vehicle is removed from the lane. , greater than or equal to The sampling points are used to update the vector in step 2. .

[0027] For example, if 1.4, since all values ​​are less than 1.75, proceed to step 8. If the angle between the fitted line of the preceding vehicle and the fitted line of this vehicle is... If the angle is greater than the preset 10 degrees, it is determined that the vehicle in front has changed lanes but has not yet deviated from its original lane, and this vehicle will brake normally; if the angle between the fitted line of the vehicle in front and the fitted line of this vehicle is greater than the preset 10 degrees, it is determined that the vehicle in front has changed lanes but has not yet deviated from its original lane, and this vehicle will brake normally; If the angle is less than the preset 10 degrees, it is determined that the vehicle in front is driving normally and there is no lane change behavior, and it is removed. , ,reserve 2. Update the vector in step 2, i.e. , .

[0028] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle cooperative obstacle avoidance method based on vehicle-to-everything (V2X) communication, characterized in that, include: The GPS position signal of this vehicle, the GPS position signal of the vehicle in front, the steering wheel angle signal of the vehicle in front, and the turning signal of the vehicle in front are acquired respectively. When the steering signal of the vehicle in front is no steering, calculate the mean and standard deviation of the steering wheel angle signal of the vehicle in front, and identify the steering wheel operation state of the vehicle in front based on the mean and standard deviation. When the steering wheel operation status of the vehicle in front is identified as continuous deviation, the vehicle performs trajectory fitting based on the GPS position signals of both the vehicle in front and the vehicle in front. The vehicle in front is then judged to have changed lanes and to what extent, based on the distance between the GPS position of the vehicle in front and the fitted trajectory of the vehicle in front, as well as the spatial relationship between the two fitted trajectories. Based on the judgment of the preceding vehicle's lane-changing behavior and the degree of lane-changing, the vehicle generates a braking electronic control command and performs obstacle avoidance according to the braking electronic control command.

2. The vehicle cooperative obstacle avoidance method based on vehicle-to-everything (V2X) as described in claim 1, characterized in that, Calculating the mean and standard deviation of the steering wheel angle signal of the preceding vehicle, and identifying the steering wheel operation state of the preceding vehicle based on the mean and standard deviation, includes: The steering wheel angle signal and GPS position signal of the vehicle and the vehicle in front are converted into digital sampled values ​​and then stored sequentially in the sliding window vector of the corresponding electronic control unit; Based on the sampled steering wheel angle values ​​of the preceding vehicle in the sliding window vector, the mean and standard deviation of the sampled steering wheel angle values ​​of the preceding vehicle are calculated, and the deviation of the newly acquired sampled value from the mean is compared with three times the standard deviation to identify the steering wheel operation state of the preceding vehicle.

3. The vehicle cooperative obstacle avoidance method based on vehicle-to-everything (V2X) as described in claim 2, characterized in that, Identifying the steering wheel operation status of the vehicle ahead based on the mean and standard deviation includes: When the absolute difference between the newly collected sampling point and the mean is not less than three times the standard deviation, it is determined that the steering wheel of the vehicle in front has deviated; otherwise, the sliding window vector is updated. After the steering wheel of the preceding vehicle deviates from its position, a second and third sampling point are collected. The deviation is determined based on the second and third sampling points, and the steering wheel operation status of the preceding vehicle is identified based on the deviation determination result.

4. The vehicle cooperative obstacle avoidance method based on vehicle networking according to claim 3, characterized in that, The deviation determination for the second and third sampling points includes: If the absolute difference between at least one of the second and third sampling points and the mean is not less than three times the standard deviation, then the steering wheel state of the vehicle in front is identified as continuously deviating; otherwise, the sliding window vector is updated.

5. A vehicle cooperative obstacle avoidance method based on vehicle networking according to claim 3 or 4, characterized in that, Updating the sliding window vector includes: When the absolute difference between at least two of the three consecutive sampling points and the mean is less than three times the standard deviation, the corresponding sampling points are added to the end of the sliding window vector, and a preset number of sampling points at the beginning of the vector are deleted to maintain a constant vector length.

6. The vehicle cooperative obstacle avoidance method based on vehicle-to-everything (V2X) as described in claim 2, characterized in that, Determining whether the preceding vehicle changed lanes and the extent of the lane change based on the distance between the preceding vehicle's GPS location and the fitted trajectory of this vehicle, as well as the spatial relationship between the two fitted trajectories, includes: The least-squares straight-line fitting is performed on the GPS position sampled values ​​in the vehicle's sliding window vector to obtain the vehicle's driving reference straight-line equation. Calculate the perpendicular distances from the three most recent GPS location sampling points of the preceding vehicle to the reference straight line equation of the current vehicle. When any one of the perpendicular distances is not less than half of the preset lane width, it is determined that the preceding vehicle has changed lanes and deviated from its original lane.

7. A vehicle cooperative obstacle avoidance method based on vehicle-to-everything (V2X) as described in claim 6, characterized in that, Determining whether the vehicle in front has changed lanes and the extent of the lane change also includes: The equation of the straight line in the direction of travel of the vehicle ahead is obtained by performing least-squares straight-line fitting on the three most recent GPS position samples of the vehicle ahead. Calculate the angle between the straight line equation of the preceding vehicle's driving direction and the straight line equation of the current vehicle's driving reference. When the angle is greater than a preset angle threshold, it is determined that the preceding vehicle has changed lanes but has not yet deviated from its original lane. When the included angle is not greater than a preset angle threshold, it is determined that the vehicle in front is driving normally and there is no lane change behavior.

8. A vehicle cooperative obstacle avoidance method based on vehicle networking according to claim 6 or 7, characterized in that, If it is determined that the vehicle has changed lanes and deviated from its original lane, the vehicle will take emergency braking measures, with an acceleration of generally -5 to -7 m / s². If it is determined that the vehicle has changed lanes but has not deviated from its original lane, the vehicle will take normal braking measures, with an acceleration of -3 to -5 m / s². If it is determined that the vehicle is driving normally and there is no lane-changing behavior, then the sliding window vector is updated.

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