Vehicle trajectory prediction method, electronic device, vehicle, medium and program product

Abstract

The invention relates to the field of auxiliary driving, in particular to a vehicle track prediction method, electronic equipment, a vehicle, a medium and a program product. The vehicle trajectory prediction method comprises the steps of predicting a target transverse acceleration of a vehicle in a future period of time according to driving parameters of the vehicle in a driving process of the vehicle in a preset mode; and then the driving track of the vehicle in a future period of time is predicted based on the target transverse acceleration, the driving track is the track that the vehicle stops deviating towards the target direction after deviating towards the target direction by a distance, and the target direction is the direction of the target transverse acceleration. The target transverse acceleration is determined according to the driving parameters of the vehicle, so that the transverse acceleration for predicting the vehicle trajectory is more accurate, and the accuracy of the predicted vehicle trajectory is improved.

Description

Technical Field

[0001] This application relates to the field of driver assistance technology, and in particular to a vehicle trajectory prediction method, electronic device, vehicle, medium, and program product. Background Technology

[0002] While the vehicle is in motion, the Automatic Emergency Braking (AEB) system can automatically detect and respond to potential collision risks, helping the driver avoid or mitigate the consequences of a collision, thereby improving vehicle safety. Additionally, the Lane Keeping Assist (LKA) system can be activated to keep the vehicle centered in its lane, reducing the risk of unintentional lane departures due to driver distraction or fatigue.

[0003] The AEB system can use algorithms to analyze data such as vehicle speed and acceleration collected by sensors on the vehicle to predict the vehicle's trajectory and calculate the collision time between the vehicle and obstacles, and then apply emergency braking when there is a risk of collision between the vehicle and an obstacle.

[0004] However, the current AEB system does not predict the vehicle's trajectory accurately enough when the LKA system is activated, which can easily lead to the AEB system erroneously triggering emergency braking, thereby reducing the user's driving experience. Summary of the Invention

[0005] This application provides a vehicle trajectory prediction method, electronic device, vehicle, medium, and program product.

[0006] In a first aspect, embodiments of this application provide a vehicle trajectory prediction method applied to an electronic device. The method includes: during vehicle operation in lane-keeping assist mode, the electronic device predicts, based on vehicle driving parameters, that the vehicle's lateral acceleration will reach a target lateral acceleration within a future period. Then, based on the target lateral acceleration and the constraint imposed on the vehicle's lateral acceleration by the lane-keeping assist mode, the method predicts the vehicle's driving trajectory within the future period. The constraint imposed on the vehicle's lateral acceleration by the lane-keeping assist mode includes: when the lateral acceleration reaches the target lateral acceleration, reducing the vehicle's lateral acceleration to a preset acceleration. The driving trajectory is the trajectory where the vehicle deviates a certain distance in a target direction and then stops deviating in that direction, where the target direction is the direction of the target lateral acceleration.

[0007] Using the methods described above, electronic devices can predict the vehicle's target lateral acceleration over a future period based on the vehicle's current driving parameters. Therefore, the target lateral acceleration better reflects the vehicle's actual driving conditions. By predicting the vehicle's trajectory based on the target lateral acceleration, electronic devices can improve the accuracy of their future trajectory predictions, thereby increasing the accuracy of collision predictions and ultimately enhancing driving safety and the driver's experience.

[0008] In one possible implementation of the first aspect described above, the driving parameters include the vehicle's yaw rate and the vehicle's steering wheel angle.

[0009] In one possible implementation of the first aspect above, predicting that the lateral acceleration of the vehicle will reach a target lateral acceleration over a future period includes: determining a first lateral acceleration based on the yaw rate, and determining a second lateral acceleration based on the steering wheel angle. The target lateral acceleration is determined based on at least one of the arithmetic mean, weighted average, and weighted sum of the first and second lateral accelerations.

[0010] In this implementation, the electronic device can predict the first lateral acceleration and the second lateral acceleration of the vehicle in the future based on the yaw rate and the steering wheel angle, respectively, and determine the target acceleration based on the first lateral acceleration and the second lateral acceleration, which can improve the accuracy of the target lateral acceleration determined by the electronic device.

[0011] In one possible implementation of the first aspect described above, determining the target lateral acceleration based on one of the arithmetic mean, weighted average, and weighted sum of the first and second lateral accelerations includes: using one of the arithmetic mean, weighted average, and weighted sum of the first and second lateral accelerations as a candidate lateral acceleration; approximating the candidate lateral acceleration to a first preset acceleration range to determine the target lateral acceleration.

[0012] In this implementation, the target lateral acceleration can be set within a first preset acceleration range. This first preset acceleration range is the acceleration range that will not cause discomfort to the driver or passengers when the vehicle changes gears. During the LKA (Lane Assist) system's guidance of the vehicle back to center, the vehicle's lateral acceleration is within this first preset acceleration range. Therefore, when the electronic device predicts the target acceleration, it can approximate the target acceleration to the first preset acceleration range, thereby improving the accuracy of the target acceleration predicted by the electronic device.

[0013] In one possible implementation of the first aspect above, when the vehicle is in lane keeping assist mode, the vehicle's driving state includes at least one of the following: the vehicle's turn signal is not activated, the vehicle's speed is within the target speed range, the lane line of the lane where the vehicle is located is identified, the vehicle's steering wheel angle is less than or equal to a preset angle, and the vehicle's lateral acceleration is predicted to be within a second preset acceleration range.

[0014] In one possible implementation of the first aspect above, the lateral acceleration of the vehicle is predicted based on the vehicle's steering wheel angle and / or yaw rate.

[0015] In this implementation, the electronic device can predict the lateral acceleration that the vehicle may reach in the future based on the steering wheel angle and / or yaw rate. If the lateral acceleration exceeds a second preset acceleration range, it may indicate that the user intends to change lanes, in which case the LKA system will be deactivated. Therefore, in this implementation, the LKA system will remain active only if the predicted lateral acceleration of the vehicle is within the second preset acceleration range.

[0016] In one possible implementation of the first aspect described above, the target lateral acceleration is the maximum lateral acceleration of the vehicle over a future period of time.

[0017] Secondly, this application provides an electronic device, comprising: a memory for storing instructions; and at least one processor for executing the instructions to cause the electronic device to implement the vehicle trajectory prediction method provided in the first aspect and any possible implementation of the first aspect. The beneficial effects achievable in the second aspect can be referred to the beneficial effects of the method provided in any embodiment of the first aspect, and will not be repeated here.

[0018] Thirdly, this application provides a vehicle including the electronic equipment described in the second aspect. The beneficial effects achievable through this third aspect are similar to those of the electronic equipment provided in the second aspect, and will not be repeated here.

[0019] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed by a device, cause a computer to implement the vehicle trajectory prediction method provided in the first aspect and any possible implementation of the first aspect. The beneficial effects achievable in the fourth aspect can be found in the beneficial effects of the method provided in any embodiment of the first aspect, and will not be repeated here.

[0020] Fifthly, this application provides a computer program product that stores instructions that, when executed on a device, cause the device to implement the vehicle trajectory prediction method provided in the first aspect and any possible implementation of the first aspect. The beneficial effects achievable in the fifth aspect can be found in the beneficial effects of the method provided in any embodiment of the first aspect, and will not be repeated here. Attached Figure Description

[0021] Figure 1 A schematic diagram of AEB predicting the vehicle's trajectory is shown.

[0022] Figure 2 According to some embodiments of this application, a flowchart of a vehicle trajectory prediction method is shown;

[0023] Figure 3A According to some embodiments of this application, a schematic diagram of a vehicle traveling on a two-way, two-lane road is shown;

[0024] Figure 3B According to some embodiments of this application, a schematic diagram of a vehicle traveling on a three-lane road in the same direction is shown;

[0025] Figure 4A According to an embodiment of this application, a predicted curve of the lateral acceleration of a vehicle is shown;

[0026] Figure 4B According to an embodiment of this application, a prediction curve for the lateral acceleration of another vehicle is shown;

[0027] Figure 5 According to some embodiments of this application, a structural schematic diagram of a vehicle is shown. Detailed Implementation

[0028] The illustrative embodiments of this application include, but are not limited to, vehicle trajectory prediction methods, electronic devices, vehicles, media, and program products.

[0029] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] As shown in the background section, the current AEB system predicts the vehicle's trajectory inaccurately, which can easily lead to the AEB system erroneously triggering emergency braking, thereby reducing the user's driving experience.

[0031] The following describes a process for predicting a vehicle's trajectory when the LKA system is activated.

[0032] For example, Figure 1A schematic diagram of AEB predicting the vehicle's trajectory is shown.

[0033] Reference Figure 1 During vehicle operation, the AEB system on the vehicle can obtain the vehicle's current speed and longitudinal acceleration (α) through multiple sensors. x0 and lateral acceleration a y0 Wait for the data. Then, calculate the vehicle's current longitudinal acceleration 'a'. x0 As the initial longitudinal acceleration, and the vehicle's current lateral acceleration a y0 Used as initial lateral acceleration to predict the vehicle's path over a future period.

[0034] It is understandable that when the vehicle's Lane Keeping Assist (LKA) system is activated, the LKA system can intervene to bring the vehicle back to the center of the lane if it detects that the vehicle's deviation from the current lane center exceeds a certain threshold. Therefore, when the LKA system intervenes, the vehicle's trajectory is one that initially moves away from the lane centerline and then returns to it. In other words, when the LKA system is activated, the Automatic Emergency Braking (AEB) system predicts in real time that the vehicle's lateral acceleration may change from the initial lateral acceleration 'a'. y0 The vehicle accelerates to a1, then decelerates from a1 to 0. Here, a1 represents the maximum lateral acceleration of the vehicle deviating from the centerline of the current lane. When the vehicle's lateral acceleration reaches a1, its deviation from the centerline of the current lane may reach a deviation threshold. At this point, the LKA system intervenes to guide the vehicle back to center. That is, the vehicle's lateral acceleration decreases from a1 to 0. During the process of guiding the vehicle back to center, the LKA system can reverse the lateral acceleration from 0 to a2, and then decelerate from a2 back to 0. The prediction of the vehicle's lateral acceleration from a1 to a1 is pre-set. Therefore, the vehicle's AEB system predicts that the vehicle's lateral acceleration will decrease from a1 to a1 within a future period (e.g., from 0 to T). y0 During the process of accelerating to a1 and then decelerating from a1 to 0, a function curve of the vehicle's lateral acceleration changing with time can be constructed. By integrating the curve of the vehicle's lateral acceleration twice, the lateral position of the vehicle in the future can be determined, so that the vehicle's AEB system can predict the vehicle's trajectory in the future.

[0035] For example, continue to refer to Figure 1After determining the initial longitudinal and lateral acceleration of the vehicle, the AEB system can predict the function curve of the vehicle's lateral acceleration over a future period by constructing a third-order Bézier curve. A third-order Bézier curve is a parametric curve defined by four control points. For example, taking the first Bézier curve constructed by the AEB system as an example, the four control points include P0, P1, P2, and P3. P0 and P3 are the start and end points of the first Bézier curve, respectively, while P1 and P2 are used to adjust the shape of the curve. P0 is typically taken as the magnitude of the vehicle's lateral acceleration at the current moment; for example, the lateral acceleration at P0 might be 'a'. y0 The magnitude of the lateral acceleration at P3 is used to predict the maximum lateral acceleration over a future period. This maximum lateral acceleration over a future period is a preset value a1. After determining the magnitudes of the lateral accelerations at P0 and P3, the shape of the first third-order Bézier curve can be adjusted based on P1 and P2, thus determining the first third-order Bézier curve. That is, the starting position of the first third-order Bézier curve is P0, and the ending position is P3. Then, the AEB system can construct the next third-order Bézier curve. The lateral acceleration at the starting position of the next third-order Bézier curve is a1, and the lateral acceleration at the ending position is 0. It can be understood that P6 can be used as the starting point of the next third-order Bézier curve to ensure the continuity after splicing the first and second third-order Bézier curves. That is, the control point at the ending position of the next third-order Bézier curve is P6, and the lateral acceleration corresponding to P6 is 0. P4 and P5 are used to adjust the shape of the second third-order Bézier curve.

[0036] After determining the second third-order Bézier curve, the two curves can be concatenated to determine the function curve of the vehicle's lateral acceleration over a future period. Integrating this function curve twice yields the curve of the vehicle's lateral position over a future period. The vehicle's AEB system, combined with the curve of the vehicle's longitudinal position over time, can predict the vehicle's path. Then, the AEB system can predict the collision scenario with obstacles based on the vehicle's path, thus determining whether to apply emergency braking. It can be understood that the AEB system predicts that the vehicle's lateral acceleration will decrease to 0 at time T, meaning the LKA system is guiding the vehicle back to center. Between times T and 2T, the LKA system may guide the vehicle back to the center line of the lane.

[0037] However, the aforementioned lateral acceleration a1 is a preset lateral acceleration, which may deviate significantly from the actual lateral acceleration of the vehicle. Therefore, the vehicle's trajectory predicted by the AEB system based on the lateral acceleration a1 is not accurate enough. When the AEB system makes collision predictions based on the vehicle's trajectory, it is prone to prediction errors, which can lead to abnormal braking of the AEB system and affect the user's driving experience.

[0038] To address the aforementioned problems, this application provides a vehicle trajectory prediction method. During vehicle operation in a preset mode, the electronic device can predict the target lateral acceleration of the vehicle deviating from the lane centerline over a future period based on the vehicle's driving parameters (e.g., predicting the maximum lateral acceleration of the vehicle moving away from the lane centerline, corresponding to the aforementioned lateral acceleration a1). It can be understood that the target lateral acceleration is determined in real-time based on the vehicle's driving parameters, and... Figure 1 In the illustrated embodiments, the preset lateral acceleration in lane keeping assist mode is different. Then, the electronic device can predict the vehicle's trajectory over a future period of time based on the target lateral acceleration; the vehicle's trajectory is the path from away from the center line of the lane to closer to the center line of the lane.

[0039] Through the above scheme, electronic devices can predict the target lateral acceleration of the vehicle in the future based on the vehicle's current driving parameters, thereby improving the accuracy of the vehicle in determining the target lateral acceleration, which in turn improves the accuracy of the electronic devices in predicting the vehicle's driving trajectory in the future, and further improves the accuracy of the electronic devices in predicting vehicle collisions, thus enhancing driving safety and ensuring the driver's driving experience.

[0040] The vehicle trajectory prediction method in the embodiments of this application is described below.

[0041] For example, Figure 2 According to some embodiments of this application, a flowchart of a vehicle trajectory prediction method is shown.

[0042] It is understandable that the following processes can be executed by electronic devices, such as vehicle infotainment systems, smart cockpits, controllers, in-vehicle tablets, in-vehicle computers, etc.; or any electronic device with computing capabilities, such as servers.

[0043] like Figure 2 As shown, the process includes:

[0044] S201, when the vehicle is in lane keeping assist mode, predicts the target lateral acceleration of the vehicle in the future based on the vehicle's driving parameters.

[0045] In the embodiments of this application, the vehicle can enter the Lane Keeping Assist (LKA) mode during driving (as an example of a preset mode). For example, the vehicle can enter the LKA mode when the driving state includes at least one of the following: the vehicle does not turn on the turn signal, the vehicle speed is within the target speed range, the lane line of the lane where the vehicle is located is identified, the steering wheel angle of the vehicle is less than or equal to a preset angle, and the lateral acceleration of the vehicle is predicted to be within a second preset acceleration range.

[0046] It's understandable that when the turn signal is not activated, the electronic system can predict that the driver has no intention to steer. If the vehicle deviates from the center line by more than a certain threshold, it may be an unintentional deviation by the driver, requiring the Lane Keeping Assist (LKA) system to intervene and bring the vehicle back to the center line. Therefore, the vehicle can enter LKA mode when the turn signal is not activated.

[0047] In some embodiments of this application, the target speed range can be 60 km / h to 120 km / h. When the vehicle speed is within the target speed range, the electronic equipment can predict that the vehicle will maintain straight-line driving. If the vehicle deviates from the center line of the lane by a greater than the deviation threshold, it may be an unintentional deviation by the driver, requiring the LKA system to intervene and bring the vehicle back to the center line of the lane. Therefore, when the vehicle speed is within the target speed range, the vehicle can enter LKA mode.

[0048] It is understandable that since entering LKA mode requires determining the center line of the lane in which the vehicle is located, the vehicle can enter LKA mode if the electronic equipment can clearly identify the lane line in which the vehicle is located.

[0049] In some embodiments of this application, the preset steering wheel angle can be any value from 0° to 10°. It is understood that when the steering wheel angle is less than or equal to the preset angle, the electronic equipment can predict that the driver has no intention to steer. If the vehicle deviates from the center line of the lane by a greater than a deviation threshold, it may be an unintentional deviation by the driver, requiring the LKA system to intervene and bring the vehicle back to the center line of the lane. Therefore, when the steering wheel angle is less than or equal to the preset angle, the vehicle can enter LKA mode.

[0050] In some embodiments of this application, the electronic device can also predict the vehicle's lateral acceleration. If the predicted lateral acceleration is within a preset acceleration range (as an example of a second preset acceleration range), the electronic device can determine that the driver has no intention to steer. If the vehicle deviates from the center line of the lane by a greater than a deviation threshold, it may be an unintentional deviation by the driver, requiring the LKA system to intervene and bring the vehicle back to the center line of the lane. Therefore, when the predicted lateral acceleration is within the second preset acceleration range, the vehicle can enter LKA mode.

[0051] In some embodiments of this application, the electronic device can predict the lateral acceleration of the vehicle based on the steering wheel angle and / or yaw rate. For example, if the electronic device detects a steering wheel angle of δ, it can predict the lateral acceleration of the vehicle as (v0). 2 / L)×δ. Where v0 is the vehicle's current speed and L is the vehicle's wheelbase. Alternatively, if the electronic device detects a yaw rate of r, the vehicle's lateral acceleration can be predicted as v0×r, where v0 is the vehicle's current speed. In some embodiments of this application, the second preset acceleration range can be -a in ~a in , where a in 0.3 m / s 2 ~0.6m / s 2 Any value in (v0). That is, in (v0) 2 / L)×δ and / or v0×r are in -a in ~a in Under certain conditions, the vehicle can enter or remain in LKA mode.

[0052] In LKA mode, the LKA system can detect the vehicle's deviation from the center line of the lane it is in. If the deviation exceeds a threshold, the LKA system can intervene and guide the vehicle back to a position closer to the center line of the lane, reducing the risk of the vehicle unintentionally deviating from the lane due to driver distraction or fatigue.

[0053] For example, an electronic device can detect the position of the lane lines in the lane where the vehicle is located, determine the center line of the lane based on the position of the lane lines, and then determine a first offset based on the distance of the vehicle's reference point relative to the center line of the lane. In some embodiments of this application, the reference point of the vehicle can be a point mass of the vehicle.

[0054] For example, Figure 3A and Figure 3B A schematic diagram showing a vehicle deviating from the center line of its lane is shown. Figure 3AAccording to some embodiments of this application, a schematic diagram of a vehicle traveling on a two-way, two-lane road is shown. Figure 3B According to some embodiments of this application, a schematic diagram of a vehicle traveling on a three-lane road in the same direction is shown.

[0055] Reference Figure 3A When a vehicle is traveling on a two-lane road, the system can identify the two lane markings and the center line. In this situation, the electronic equipment can determine that the vehicle is in the lane between the center line and the right lane marking, and therefore the center line of the lane is located between the center line and the right lane marking (see reference). Figure 3A (The dashed line in the text). In embodiments of this application, the offset threshold can be any value between 20% and 30% of half the width of the lane. For example, if the width of the lane the vehicle is currently in is d1, and the offset of the vehicle's reference point relative to the center line of the lane is p1, and the offset threshold is taken as 20% of half the width of the lane, then if p1 is greater than (d1 / 2) × 20%, the LKA system will intervene in the vehicle and guide it back to the center line of the lane.

[0056] Reference Figure 3B When a vehicle is traveling on a multi-lane road in the same direction, the electronic equipment can identify the two lane lines of the lane in which the vehicle is located, and thus determine the center line of the lane as the center line between the two lane lines (refer to...). Figure 3B (The dashed line in the image). Furthermore, the electronic equipment can identify the width of the lane where the vehicle is located as d2, and the offset of the vehicle's reference point relative to the center line of the lane as p2. If p2 is greater than (d2 / 2)×20%, the LKA system will intervene in the vehicle and guide it back to the center line of the lane.

[0057] It is understood that if the electronic device detects that the LKA system is activated, it can predict the target lateral acceleration of the vehicle in real time. The electronic device can collect the vehicle's driving parameters and determine the target lateral acceleration based on these parameters. These driving parameters may include the vehicle's yaw rate and / or steering wheel angle. In some embodiments of this application, the process of the electronic device determining the target lateral acceleration is described using yaw rate and steering wheel angle as driving parameters.

[0058] For example, the electronic device can determine a first lateral acceleration based on the yaw rate and a second lateral acceleration based on the steering wheel angle. Then, the electronic device determines a target lateral acceleration based on at least one of the arithmetic mean, weighted average, and weighted sum of the first and second lateral accelerations. For example, the electronic device can use one of the arithmetic mean, weighted average, and weighted sum of the first and second lateral accelerations as a candidate lateral acceleration. The candidate lateral acceleration is then approximated to a first preset acceleration range to determine the target lateral acceleration.

[0059] For example, if the electronic device determines that the vehicle's current yaw rate is r, the steering wheel angle is δ, the current speed is v0, and the vehicle's wheelbase is L, then the electronic device can predict the vehicle's lateral acceleration (v0) based on the steady-state steering model. 2 / L)×δ (as an example of the second lateral acceleration), and the lateral acceleration of the vehicle predicted by the dynamic kinematic model is v0×r (as an example of the first lateral acceleration). Then the electronic device can determine the target lateral acceleration according to the following formula (1):

[0060] a1=clamp(α×(v0 2 / L)×δ+(1-α)×v0×r,-a max a max (1)

[0061] Where a1 is the target lateral acceleration, and clamp(A, B, C) is a constraint function used to limit the value of A within a preset range [B, C]. α is the fusion weight. In some embodiments of this application, α can be any value between 0.3 and 0.7. [-a max a max ] can be the first preset acceleration range, a max It can be 1.5m / s 2 ~2.5m / s 2 Any value between these ranges can be used to avoid predicting excessively large accelerations, thus improving driving comfort for the user. Wherein, α × (v0) 2 / L)×δ+(1-α)×v0×r is the weighted sum of the first lateral acceleration and the second lateral acceleration. That is, in some embodiments of this application, the candidate lateral acceleration is the weighted sum of the first lateral acceleration and the second lateral acceleration.

[0062] It is understandable that the value of a1 is related to the vehicle's current steering wheel angle δ, yaw rate r, and speed v0. This allows for a more accurate prediction of the maximum lateral acceleration that the vehicle is currently deviating from the center line of the lane, and this predicted maximum lateral acceleration is taken as the target lateral acceleration.

[0063] S202, based on the target lateral acceleration and the lane keeping assist mode's constraint on the vehicle's lateral acceleration, predict the vehicle's driving trajectory over a future period of time. The lane keeping assist mode's constraint on the vehicle's lateral acceleration includes: when the lateral acceleration reaches the target lateral acceleration, reducing the vehicle's lateral acceleration to a preset acceleration. The driving trajectory is the trajectory where the vehicle deviates a certain distance in the target direction and then stops deviating in the target direction. The target direction is the direction of the target lateral acceleration.

[0064] In some embodiments of this application, after the electronic device predicts the target lateral acceleration, it can predict the vehicle's trajectory over a future period based on the vehicle's current initial acceleration and / or the target lateral acceleration. In these embodiments, the future period is the time interval during which the vehicle accelerates from its current lateral acceleration to the target lateral acceleration, and then decreases from the target lateral acceleration to zero. In other words, the vehicle will deviate a certain distance in the direction of the target acceleration over the future period, and then stop deviating.

[0065] For example, the initial acceleration of a vehicle is a0, and the target lateral acceleration is a1. In some embodiments of this application, for ease of calculation, the lateral acceleration of the vehicle can be reduced from a0 to 0 first, then accelerated from 0 to a1, and finally reduced from a1 to 0. That is, in the process of predicting the vehicle trajectory, the vehicle's lateral acceleration is always reduced to 0 before accelerating to a1. It can be understood that the moment when the vehicle's lateral acceleration begins to decrease from a1 can be the moment when the electronic device predicts that the vehicle's LKA system will begin to intervene. That is, the lane keeping assist mode can constrain the vehicle's lateral acceleration, reducing it to a preset acceleration (e.g., the preset acceleration can be 0) when the lateral acceleration reaches the target lateral acceleration. For example, after the vehicle's lateral acceleration accelerates to a1, if the vehicle's deviation from the centerline of the current lane is greater than a deviation threshold, the vehicle's LKA system will intervene to guide the vehicle back to center, and therefore, the vehicle's lateral acceleration will decrease from a1 to 0. In other embodiments, the electronic device can also directly predict the vehicle's trajectory over a future period of time as a trajectory in which the vehicle's lateral acceleration increases from a0 to a1 and then decreases from a1 to 0.

[0066] It's understandable that in real-world scenarios, if the target direction is away from the lane centerline, and the vehicle consistently exhibits lateral acceleration deviating in that direction, it will continue to deviate until the deviation from the lane centerline exceeds a threshold. At this point, the vehicle's Lane Keeping Assist (LKA) system will intervene, guiding the vehicle back to center, aligning it with the trajectory predicted by the electronic equipment. If the vehicle's deviation does not exceed the threshold and the LKA system does not intervene, it indicates that the vehicle may be experiencing other forces causing its lateral acceleration to gradually decrease to zero (e.g., wind or uneven road surfaces causing changes in lateral acceleration), again aligning with the trajectory predicted by the electronic equipment.

[0067] In other embodiments, when the vehicle's LKA system intervenes, the vehicle will move towards the center line of the lane to straighten itself. During this straightening process, the electronic equipment can also predict the vehicle's trajectory. That is, when the LKA system guides the vehicle to straighten, the electronic equipment can also predict the target acceleration of the vehicle in the straightening state based on the vehicle's current yaw rate and / or steering wheel angle; the direction of the target acceleration is the direction in which the vehicle moves towards the center line of the lane. It can be understood that during the process of the vehicle straightening to the center line of the lane, the vehicle's lateral acceleration first increases to the target lateral acceleration, and then decreases to 0, thereby keeping the vehicle moving along the center line of the lane.

[0068] The following describes the process of predicting a vehicle's trajectory using target acceleration.

[0069] In some embodiments of this application, a piecewise third-order Bézier curve can be constructed based on the vehicle's initial lateral acceleration and target lateral acceleration as the acceleration variation curve over a future period of time.

[0070] For example, the future time period can be [0, T], where T can be any value from 1.5s to 2.5s. In some embodiments of this application, the change in the vehicle's lateral acceleration over the future time period is as follows: from an initial lateral acceleration decreasing to 0, then increasing from 0 to the target lateral acceleration, and then decreasing from the target lateral acceleration back to 0. Therefore, the change in the vehicle's lateral acceleration over the future time period can be divided into three segments, with each segment of the Bézier curve occupying the same time, that is, each segment of the Bézier curve occupies a time of T / 3, thereby obtaining the vehicle's lateral acceleration 'a' over the future time period. y(t). In other embodiments, the time occupied by each Bézier curve may be different. For example, the weight of the time occupied may be assigned according to the predicted target lateral acceleration and the magnitude of the initial lateral acceleration. The embodiments of this application do not limit the time occupied by each Bézier curve.

[0071] For example, Figure 4A According to an embodiment of this application, a predicted curve of the lateral acceleration of a vehicle is shown.

[0072] It can be understood that each segment of a third-order Bézier curve can be composed of 4 control points. Therefore, the sequence of control points for three segments of a third-order Bézier curve can be: [a0,a0,0,0,0,0,a1,a1,a1,a1,0,0].

[0073] Reference Figure 4A The time interval of the first segment of the third-order Bézier curve is from 0s to T / 3. This segment of the third-order Bézier curve represents the smooth decay of the vehicle's lateral acceleration from a0 to 0, and the corresponding sequence of control points is [a0,a0,0,0], where a0 can represent the vehicle's initial lateral acceleration.

[0074] The second segment of the third-order Bézier curve spans from T / 3 to 2T / 3. This segment represents a smooth increase in the magnitude of the vehicle's lateral acceleration from 0 to a1. The corresponding sequence of control points is [0,0,a1,a1], where a1 can represent the vehicle's target lateral acceleration. It can be understood that the control point at the beginning of the second segment (corresponding to the first control point of the second segment, which is 0) repeats the control point at the end of the first segment (corresponding to the fourth control point of the first segment, which is 0). This ensures the continuity at the connection between the first and second segments of the third-order Bézier curve.

[0075] The third segment of the third-order Bézier curve spans from 2T / 3 to T. This segment represents the smooth decrease in the vehicle's lateral acceleration from a1 to 0. The corresponding sequence of control points is [a1, a1, 0, 0], where a1 represents the target lateral acceleration of the vehicle. It can be understood that the control point at the beginning of the third segment (corresponding to the first control point of the third segment, which is a1) repeats the control point at the end of the second segment (corresponding to the fourth control point of the second segment, which is a1). This ensures the continuity at the connection between the second and third segments. In other words, the three Bézier curves maintain continuity throughout, satisfying Jerk continuity (i.e., the rate of change of acceleration does not change abruptly), thus ensuring smooth vehicle dynamics. After constructing the three segments of the third-order Bézier curve, they can be spliced ​​together in chronological order to determine the curve function a representing the change of the vehicle's lateral acceleration over time in the future. y (t), then the electronic device can be based on a y (t) Predict the lateral position of the vehicle to predict its trajectory over a future period of time.

[0076] In other embodiments of this application, the change in the vehicle's lateral acceleration over a future period is as follows: from an initial lateral acceleration to a target lateral acceleration, and then from the target lateral acceleration to 0. Therefore, the change in the vehicle's lateral acceleration over the future period can be divided into two segments, with the two segments occupying the same time. That is, the time occupied by each segment of the Bézier curve is T / 2, thereby obtaining the vehicle's lateral acceleration 'a' over the future period. y (t).

[0077] For example, Figure 4B According to an embodiment of this application, a predicted curve of the lateral acceleration of another vehicle is shown.

[0078] Reference Figure 4B The sequence of control points for two segments of a third-order Bézier curve can be: [a0,a0,a1,a1,a1,a1,0,0].

[0079] The time frame of the first segment of the third-order Bézier curve is from 0s to T / 2. This segment of the third-order Bézier curve represents the smooth change of the vehicle's lateral acceleration from a0 to a1, and the corresponding sequence of control points is [a0,a0,a1,a1], where a0 can represent the vehicle's initial lateral acceleration.

[0080] The second segment of the third-order Bézier curve spans from T / 2 to T. This segment represents the smooth decrease in the vehicle's lateral acceleration from a1 to 0. The corresponding sequence of control points is [a1, a1, 0, 0], where a1 represents the target lateral acceleration of the vehicle. It can be understood that the control point at the beginning of the second segment (corresponding to the first control point of the second segment, which is a1) repeats the control point at the end of the first segment (corresponding to the fourth control point of the first segment, which is a1). This ensures the continuity at the connection point between the second and first segments. In other words, the two Bézier curves maintain continuity throughout, satisfying Jerk continuity, thus guaranteeing smooth vehicle dynamics. After constructing the two segments of the third-order Bézier curve, they can be spliced ​​together in chronological order to determine the curve function a representing the change in the vehicle's lateral acceleration over a future period. y (t), then the electronic device can be based on a y (t) Predict the lateral position of the vehicle to predict its trajectory over a future period of time.

[0081] For example, the function a can be modified using the following equation (2). y (t) Perform two integrations to determine the relationship between the vehicle's lateral position and time over a future period.

[0082] (2)

[0083] Where y(t) is a function of the vehicle's lateral position and time over a future time period, y0 is the vehicle's lateral position at the initial moment over the future time period, and v0 is the vehicle's lateral velocity at the initial moment over the future time period. Let a be the heading angle of the vehicle at the initial moment within a future time period. y (s) is a function of the lateral velocity of the vehicle over a future period of time, where s is any moment in the future period of time, τ is any moment in the time interval from 0 to s, and t is any moment in the time interval from 0 to τ.

[0084] After obtaining y(t), the electronic device can also predict the vehicle's longitudinal position and time change function x(t) based on the vehicle's longitudinal acceleration, longitudinal velocity, and heading angle. Then, the electronic device can determine the vehicle's driving trajectory based on y(t) and x(t).

[0085] With the vehicle trajectory prediction method in this application embodiment, when the LKA system is activated, the electronic device can more accurately predict the vehicle's driving trajectory, thereby making more precise collision prediction, avoiding false activation of the AEB system, improving driving safety and enhancing the user's driving experience.

[0086] For example, electronic devices can predict the collision situation between a vehicle and an obstacle based on the predicted trajectory of the vehicle over a period of time in the future, or predict whether the vehicle is within a preset path, and determine whether AEB braking is required, thereby improving driving safety.

[0087] The vehicles in the above embodiments will now be described.

[0088] For example, Figure 5 According to some embodiments of this application, a structural schematic diagram of a vehicle 01 is shown.

[0089] Understandable. Figure 5 This is a schematic diagram of a possible functional framework for a vehicle 01 provided in an embodiment of this application. For example... Figure 5 As shown, the functional framework of vehicle 01 may include various subsystems, such as sensor system 10, control system 20, one or more peripheral devices 30 (one is shown as an example in the figure), power supply 40, and computer system 50. Optionally, vehicle 01 may also include other functional systems, such as an engine system that provides power to vehicle 01, etc., which are not limited here. It is understood that the electronic devices in the embodiments of this application may be devices on vehicle 01 that include computer system 50.

[0090] The sensor system 10 may include several detection devices that can sense the measured information and convert the sensed information into electrical signals or other required forms of information output according to a certain rule. As shown in the figure, these detection devices may include a Global Positioning System 11 (GPS), a vehicle speed sensor 12, an Inertial Measurement Unit 13 (IMU), etc., and this application is not limited thereto. The Global Positioning System GPS 11 is a system that uses GPS positioning satellites to perform real-time positioning and navigation globally. In this application, the vehicle speed sensor 12 is used to detect the driving speed of vehicle 01. The Inertial Measurement Unit 13 may include a combination of an accelerometer and a gyroscope, and is a device for measuring the yaw rate and acceleration of vehicle 01. For example, during the movement of vehicle 01, the Inertial Measurement Unit can measure the position and angular changes of the vehicle body based on the inertial acceleration of vehicle 01, such as measuring the acceleration and yaw rate of vehicle 01.

[0091] The control system 20 may include a steering unit 21, a braking unit 22, etc.

[0092] The steering unit 21 can represent a system for adjusting the direction of travel of vehicle 01, which may include, but is not limited to, a steering wheel or other structural devices for adjusting or controlling the direction of travel of vehicle 01. In embodiments of this application, vehicle 01 can determine data such as the steering wheel angle through the steering unit 21. The braking unit 22 can represent a system for slowing down the speed of vehicle 01, and may also be referred to as the vehicle 01 braking system. It may include, but is not limited to, a brake controller, a reducer, or other structural devices for slowing down vehicle 01. In practical applications, the braking unit 22 can use friction to slow down the tires of vehicle 01, thereby slowing down the speed of vehicle 01. For example, the vehicle's AEB system may include the braking unit 22, which can be controlled to brake when a collision with an obstacle is predicted.

[0093] Peripheral device 30 may include several components, such as the communication system 31, touch screen 32, user interface 33, etc., as shown in the figure. The communication system 31 is used to enable network communication between vehicle 01 and other devices besides vehicle 01, such as electronic device 2. In practical applications, the communication system 31 can employ wireless communication technology or wired communication technology to achieve network communication between vehicle 01 and other devices. This wired communication technology can refer to communication between vehicle 01 and other devices via network cable or fiber optic cable, etc. This wireless communication technology includes, but is not limited to, Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Wireless Local Area Networks (WLAN) (such as Wireless Fidelity (Wi-Fi) networks), Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), and Infrared (IR) technology, etc.

[0094] The touchscreen 32 can be used to detect operation commands on the touchscreen 32. For example, the user can perform touch operations on the content data displayed on the touchscreen 32 according to actual needs to achieve the corresponding function, such as playing music, video, or other multimedia files. The user interface 33 can specifically be a touch panel, used to detect operation commands on the touch panel. The user interface 33 can also be a physical button or a mouse. The user interface 33 can also be a display screen, used to output data and display images or data. Optionally, the user interface 33 can also be at least one device belonging to the category of peripheral devices, such as a touchscreen, microphone, and speaker.

[0095] Several functions of vehicle 01 are controlled and implemented by computer system 50. Computer system 50 may include multiple processors such as a general-purpose processor 51, a continuous damping control system (CDC) 52, a mobile data center (MDC) 53, a telematics box (T-BOX) 54, as well as a memory 55 (also referred to as a storage device) and a gateway 56. In practical applications, the memory 55 may be located inside or outside the computer system 50, for example, as a cache within vehicle 01; this application does not impose limitations. The general-purpose processor 51 may be a graphics processing unit (GPU), etc. The general-purpose processor 51, CDC 52, MDC 53, and T-BOX 54 can be used to run relevant programs or corresponding instructions stored in memory 55 to implement the corresponding functions of vehicle 01, such as network switching functions based on service units.

[0096] The memory 55 may include volatile memory, such as RAM; it may also include non-volatile memory, such as ROM, flash memory, HDD, or SSD; or it may include a combination of the above types of memory. The memory 55 can be used to store a set of program code or instructions corresponding to the program code, so that the general-purpose processor 51 can call the program code or instructions stored in the memory 55 to implement the corresponding functions of the vehicle 01. This function includes, but is not limited to, […]. Figure 5 The schematic diagram of the functional framework of vehicle 01 shown includes some or all of the functions. In this application, memory 55 can store a set of program codes for controlling vehicle 01. The general-purpose processor 51, CDC 52, MDC 53, and T-BOX 54 can call this program code to control vehicle 01 to execute the trajectory prediction method in this application.

[0097] Optionally, in addition to storing program code or instructions, the memory 55 may also store information such as road maps, driving routes, and sensor data. The computer system 50 can be combined with other components in the functional framework diagram of the vehicle 01, such as sensors in the sensor system and GPS, to realize the relevant functions of the vehicle 01. For example, the computer system 50 can control the driving direction or speed of the vehicle 01 based on the data input from the sensor system 10; this application does not impose limitations on this.

[0098] In embodiments of this application, the computer system 50, sensor system 10, and control system 20 can constitute the LKA system of vehicle 01. For example, vehicle 01 can acquire road images through sensor system 10, and then the computer system 50 can identify the lane where vehicle 01 is located based on the road images and determine the center line of the lane. Furthermore, the computer system 50 can also guide vehicle 01 back to the correct position through control system 20 when vehicle 01 deviates from the center line of the lane by more than a deviation threshold.

[0099] In the embodiments of this application, the computer system 50, sensor system 10, and control system 20 can constitute the AEB system of vehicle 01. For example, the computer system 50 of vehicle 01 can acquire data such as the speed, acceleration, yaw rate, and steering wheel angle of vehicle 01 through sensor system 10, then predict the driving trajectory of vehicle 01, and predict the collision situation between vehicle 01 and obstacles based on the driving trajectory of vehicle 01. If a collision between vehicle 01 and an obstacle is predicted, the control system 20 controls the vehicle to brake.

[0100] This application also provides a program product that stores instructions. When these instructions are executed on an electronic device, they enable the electronic device to implement the methods provided in the foregoing embodiments.

[0101] This application also provides a readable storage medium storing one or more programs, which, when executed by an electronic device, enable the electronic device to implement the methods provided in the foregoing embodiments.

[0102] It should be noted that the above Figure 5 This is merely a schematic diagram of one possible functional framework for vehicle 01. In practical applications, vehicle 01 may include more or fewer systems or components, and this application is not limiting. Various embodiments of the mechanisms disclosed in this application can be implemented in hardware, software, firmware, or combinations of these implementation methods. Embodiments of this application can be implemented as computer programs or program code executable on a programmable system, which includes at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0103] Program code can be applied to input instructions to execute the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, the processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application-specific integrated circuit (ASIC), or a microprocessor. The program code can be implemented using a high-level procedural language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used to implement the program code when necessary. In fact, the mechanisms described in this application are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0104] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored thereon on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or through other computer-readable media. Therefore, machine-readable media may include any mechanism for storing or transmitting information in a machine-readable (e.g., computer-readable) form, including but not limited to floppy disks, optical disks, CD-ROMs, magneto-optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic cards or optical cards, flash memory, or tangible machine-readable storage for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in the form of electrical, optical, acoustic, or other propagation signals. Therefore, machine-readable media include any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a machine-readable (e.g., computer-readable) form.

[0105] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. Furthermore, the inclusion of structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0106] It should be noted that all units / modules mentioned in the device embodiments of this application are logical units / modules. Physically, a logical unit / module can be a physical unit / module, a part of a physical unit / module, or a combination of multiple physical units / modules. The physical implementation of these logical units / modules themselves is not the most important factor; the combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed in this application. Furthermore, to highlight the innovative aspects of this application, the above-described device embodiments of this application have not introduced units / modules that are not closely related to solving the technical problems proposed in this application. This does not mean that the above-described device embodiments do not contain other units / modules.

[0107] It should be noted that in the examples and description of this patent, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0108] Although this application has been illustrated and described with reference to certain preferred embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made thereto without departing from the scope of this application.

Claims

1. A vehicle trajectory prediction method, characterized in that, Applied to electronic devices, the method includes: While the vehicle is driving in lane keeping assist mode, based on the vehicle's driving parameters, it is predicted that the vehicle's lateral acceleration will reach the target lateral acceleration within a certain period of time in the future. Based on the target lateral acceleration and the lane keeping assist mode's constraint on the vehicle's lateral acceleration, the vehicle's driving trajectory is predicted over the next period of time. The lane keeping assist mode's constraint on the vehicle's lateral acceleration includes: when the lateral acceleration reaches the target lateral acceleration, reducing the vehicle's lateral acceleration to a preset acceleration. The driving trajectory is the trajectory where the vehicle deviates a certain distance in the target direction and then stops deviating in the target direction. The target direction is the direction of the target lateral acceleration.

2. The vehicle trajectory prediction method according to claim 1, characterized in that, The driving parameters include the vehicle's yaw rate and the vehicle's steering wheel angle.

3. The vehicle trajectory prediction method according to claim 2, characterized in that, The prediction that the vehicle's lateral acceleration will reach the target lateral acceleration within a future period includes: The first lateral acceleration is determined based on the yaw rate. The second lateral acceleration is determined based on the steering wheel angle. The target lateral acceleration is determined based on at least one of the arithmetic mean, weighted mean, and weighted sum of the first and second lateral accelerations.

4. The vehicle trajectory prediction method according to claim 3, characterized in that, Determining the target lateral acceleration based on one of the arithmetic mean, weighted average, or weighted sum of the first and second lateral accelerations includes: One of the arithmetic mean, weighted average, and weighted sum of the first lateral acceleration and the second lateral acceleration is used as the candidate lateral acceleration; The candidate lateral acceleration is approximated to a first preset acceleration range to determine the target lateral acceleration.

5. The vehicle trajectory prediction method according to claim 1, characterized in that, When the vehicle is in lane keeping assist mode, the vehicle's driving state includes at least one of the following: The vehicle did not activate its turn signal, the vehicle's speed was within the target speed range, the lane lines of the lane in which the vehicle was located were identified, the vehicle's steering wheel angle was less than or equal to a preset angle, and the lateral acceleration of the vehicle was predicted to be within a second preset acceleration range.

6. The vehicle trajectory prediction method according to claim 5, characterized in that, The lateral acceleration of the vehicle is predicted based on the steering wheel angle and / or yaw rate of the vehicle.

7. The vehicle trajectory prediction method according to claim 1, characterized in that, The target lateral acceleration is the maximum lateral acceleration of the vehicle during the future period.

8. An electronic device, characterized in that, Includes memory for storing instructions; At least one processor is configured to execute the instructions to cause the electronic device to implement the vehicle trajectory prediction method of any one of claims 1 to 7.

9. A vehicle, characterized in that, Includes the electronic device as described in claim 8.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the vehicle trajectory prediction method according to any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product stores instructions, which, when executed on the device, cause the device to perform the vehicle trajectory prediction method according to any one of claims 1 to 7.

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