CN116991163A

CN116991163A - Track prediction method and device, electronic equipment and automatic driving vehicle

Info

Publication number: CN116991163A
Application number: CN202310869058.9A
Authority: CN
Inventors: 孙灏
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2023-07-14
Filing date: 2023-07-14
Publication date: 2023-11-03

Abstract

The disclosure provides a track prediction method, a track prediction device, electronic equipment and an automatic driving vehicle. The disclosure relates to the technical field of artificial intelligence, in particular to the technical field of intelligent traffic, automatic driving and the like. The specific implementation scheme is as follows: determining a target vehicle of the first vehicle; establishing at least two constraint points based on the position information and the speed information of the target vehicle; and predicting the cut track of the target vehicle based on the at least two constraint points. According to the scheme disclosed by the invention, the accuracy of the predicted vehicle cutting track of the automatic driving vehicle can be improved, so that the safety of the automatic driving vehicle is improved.

Description

Track prediction method and device, electronic equipment and automatic driving vehicle

Technical Field

The disclosure relates to the technical field of artificial intelligence, in particular to the technical field of intelligent traffic, automatic driving and the like.

Background

In the field of autopilot, an autopilot vehicle obtains information of a traffic scene in which the vehicle is located through map road topology, so that an appropriate autopilot strategy is determined according to the traffic scene information. However, in a scene where a social vehicle cuts into a lane where an autonomous vehicle is located, there are still problems that the cut intention of the social vehicle is not recalled according to the map road topology, the recalled cut track is unreasonable, and the like. Therefore, how to improve the accuracy of the automatic driving vehicle prediction cut track to ensure the safety of the automatic driving vehicle is still a need to be solved.

Disclosure of Invention

The disclosure provides a track prediction method, a track prediction device, electronic equipment and an automatic driving vehicle.

According to a first aspect of the present disclosure, there is provided a trajectory prediction method, including:

determining a target vehicle of the first vehicle;

establishing at least two constraint points based on the position information and the speed information of the target vehicle;

and predicting the cut track of the target vehicle based on the at least two constraint points.

According to a second aspect of the present disclosure, there is provided a trajectory prediction apparatus including:

a determination module for determining a target vehicle of the first vehicle;

the first establishing module is used for establishing at least two constraint points based on the position information and the speed information of the target vehicle;

and the prediction module is used for predicting the cut track of the target vehicle based on at least two constraint points.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

at least one processor;

a memory communicatively coupled to the at least one processor;

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the embodiments of the present disclosure.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a method according to any one of the embodiments of the present disclosure.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program stored on a storage medium, which when executed by a processor, implements a method according to any of the embodiments of the present disclosure.

According to a sixth aspect of the present disclosure there is provided an autonomous vehicle comprising an electronic device as described in the third aspect.

According to the scheme disclosed by the application, the cut track of the target vehicle can be predicted without depending on map road topology, and the cut track recall under the common road topology and complex road topology scene is considered, so that the accuracy of the cut track predicted by the automatic driving vehicle can be improved, the automatic driving vehicle can be helped to better plan the path, and the safety of the automatic driving vehicle is improved.

The foregoing summary is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present application will become apparent by reference to the drawings and the following detailed description.

Drawings

In the drawings, the same reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily drawn to scale. It is appreciated that these drawings depict only some embodiments according to the disclosure and are not therefore to be considered limiting of its scope.

FIG. 1 is a schematic illustration of a straight-through, straight-through scene cut in accordance with an embodiment of the present disclosure;

FIG. 2 is a flow diagram of a trajectory prediction method according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a cart-cutting trajectory drawn based on two constraint points according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of determining a validation condition according to an embodiment of the present disclosure;

FIG. 5 is a schematic drawing of a cut-car trajectory based on three constraint points according to an embodiment of the present disclosure;

FIG. 6 is a schematic diagram of an merge scene cut in accordance with an embodiment of the present disclosure;

FIG. 7 is a schematic illustration of a bifurcated scene cut in accordance with an embodiment of the disclosure;

FIG. 8 is a schematic diagram of a track prediction device according to an embodiment of the present disclosure;

FIG. 9 is a schematic view of a scenario of a trajectory prediction method according to an embodiment of the present disclosure;

fig. 10 is a schematic structural diagram of an electronic device for implementing the trajectory prediction method of the embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The terms first, second, third and the like in the description and in the claims and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion, such as a series of steps or elements. The method, system, article, or apparatus is not necessarily limited to those explicitly listed but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.

Before the technical scheme of the embodiments of the present disclosure is described, technical terms possibly used in the present disclosure are further described:

Autonomous vehicle (Autonomous vehicles; self-driving automobile): the intelligent automobile is also called an unmanned automobile, and is an intelligent automobile for realizing unmanned through a computer system;

social vehicle: other vehicles, besides autonomous vehicles, are known as social vehicles.

Fig. 1 shows a schematic view of a straight-through scene cut, as shown in fig. 1, there are autonomous vehicles on lane 1 and lane 2, and social vehicles (non-autonomous vehicles) on lane 3; the intention of the social vehicle is to cut into lane 1 or lane 2 from lane 3 to the autonomous vehicle. The automatic driving vehicle can rely on map road topology to construct a vehicle cutting track of the social vehicle, namely, the vehicle cutting track of the social vehicle is constructed along the center line of a lane by means of road connection relations in a simple road topology environment.

Under a simple road topology environment, the model is relied on to output to obtain the predicted track of the social vehicle. However, excessive dependence on model output can have negative effects such as instability, uncontrollability, unexplainability, etc. In addition, the model is adopted to predict the vehicle cutting track of the social vehicle, so that the vehicle cutting track can only cover a simple road topology environment and is seriously dependent on the labeling of a high-precision map. As shown in fig. 1, when a social vehicle on a lane 3 is cut, the social vehicle is usually cut into its adjacent lane (i.e., lane 2), and a trajectory is drawn so as to converge toward the center line of lane 2, as shown in fig. 1 (1). However, there is also a possibility of cutting a vehicle to its non-adjacent lane (i.e., lane 1), the cut track being shown as (2) in fig. 1. The difficulty of recalling the vehicle cutting track is high in complex topological environments such as crossing, bifurcation and remittance, and the predicted vehicle cutting track is unstable.

In order to at least partially solve one or more of the above-mentioned problems, as well as other potential problems, the present disclosure proposes a trajectory prediction method that establishes a constraint point based on position information and speed information of a target vehicle; the vehicle cutting track of the target vehicle is predicted based on the constraint points, the vehicle cutting track of the target vehicle is predicted without depending on map road topology, the vehicle cutting track recall under the common road topology and complex road topology scene is considered, the vehicle cutting track of the target vehicle is comprehensively analyzed by combining the vehicle cutting track predicted without depending on map road topology and the vehicle cutting track predicted by depending on map road topology, the accuracy of the vehicle cutting track predicted by the automatic driving vehicle can be improved, the automatic driving vehicle can plan a path better, and the safety of the automatic driving vehicle is improved.

The embodiment of the disclosure provides a track prediction method, and fig. 2 is a flow chart of the track prediction method according to the embodiment of the disclosure, and the track prediction method can be applied to a track prediction device. The trajectory prediction device is located in an electronic device. The electronic device includes, but is not limited to, a stationary device and/or a mobile device. For example, the fixed device includes, but is not limited to, a server, which may be a cloud server or a general server. For example, mobile devices include, but are not limited to, cell phones, tablet computers, car terminals, and the like. In some possible implementations, the trajectory prediction method may also be implemented by way of a processor invoking computer readable instructions stored in a memory. As shown in fig. 2, the trajectory prediction method includes:

S201: determining a target vehicle of the first vehicle;

s202: establishing at least two constraint points based on the position information and the speed information of the target vehicle;

s203: and predicting the cut track of the target vehicle based on the at least two constraint points.

In some embodiments, the first vehicle may be an autonomous vehicle; the second vehicle may be a social vehicle. In particular, the social vehicle may be a vehicle other than an autonomous vehicle.

In some embodiments, the target vehicle may be a social vehicle that may interact with the first vehicle within a predetermined distance and within a predetermined time range from the first vehicle.

Here, the preset distance range and the preset time range can be set or adjusted according to the safety requirement and the brake comfort requirement. Here, interaction with the first vehicle may occur within a preset time range, including: and within a preset time range, the included angle between the speed direction of the social vehicle at the sampling time point and the speed direction of the first vehicle is positioned in the preset angle range. Here, the preset angle range is (5 °,90 °). The first vehicle is an automatic driving vehicle, the automatic driving vehicle obtains a plurality of social vehicles located in a preset distance range of the first vehicle based on the information of the social vehicles collected by the information collecting system, and social vehicles with included angles with the speed direction of the first vehicle located in a preset angle range in a preset time range are screened out of the plurality of social vehicles to serve as target vehicles. Taking the schematic diagram of the cut scene shown in fig. 1 as an example, the social vehicle on the lane 3 is a target vehicle of the autonomous vehicle 1 with respect to the autonomous vehicle 1.

In some embodiments, the location information of the target vehicle may include location coordinates of the target vehicle, a distance of the target vehicle from the first vehicle, location information of the target vehicle in a map, and the like. The speed information of the target vehicle may include: speed and direction of the target vehicle, acceleration and direction of the target vehicle, and the like.

In some embodiments, the first vehicle has a perception model (also referred to as a perception module) thereon, and the perception module is configured to obtain perception information of each second vehicle. The perception information may include: speed, speed direction, acceleration direction, position information, position coordinates, shape of the second vehicle, type of the second vehicle, head orientation of the second vehicle, lane angle, etc.

In some embodiments, the position information and the speed information of the target vehicle may be acquired by an information acquisition system configured for an autonomous vehicle. The position information and the speed information of the target vehicle may also be obtained by sensors mounted on the autonomous vehicle. The position information and the speed information of the target vehicle can also be obtained through the Internet of vehicles where the automatic driving vehicle is located, and the Internet of vehicles can obtain the position information and the speed information of the vehicles in each society.

In some embodiments, the first constraint point is used to generate a cut track of the target vehicle, and the first constraint point may be located at a start point of the cut track of the target vehicle.

In some embodiments, the second constraint point is used to generate a cut track of the target vehicle, and the second constraint point may be located at an end point of the cut track of the target vehicle.

In some embodiments, the cut track of the target vehicle is used for planning a driving route for the autonomous vehicle.

According to the technical scheme, a target vehicle of a first vehicle is determined; establishing at least two constraint points based on the position information and the speed information of the target vehicle; and predicting the cut track of the target vehicle based on the at least two constraint points. Therefore, the vehicle cutting track of the target vehicle can be predicted without depending on map road topology, the vehicle cutting track recall under the common road topology and complex road topology scene is considered, the vehicle cutting track of the target vehicle is comprehensively analyzed by combining the vehicle cutting track predicted without depending on map road topology and the vehicle cutting track predicted by depending on map road topology, the accuracy of the vehicle cutting track predicted by the automatic driving vehicle can be improved, the automatic driving vehicle can plan a path better, and the safety of the automatic driving vehicle is improved.

In an embodiment of the present disclosure, determining a target vehicle of a first vehicle may include: determining a candidate vehicle of the first vehicle based on the preset distance range, wherein the candidate vehicle comprises at least one second vehicle; and screening at least one second vehicle based on the preset included angle range to obtain the target vehicle.

In some embodiments, determining a target vehicle for the first vehicle may include a first effective condition and a second effective condition.

In some embodiments, the first effective condition is used to determine a candidate vehicle. Specifically, a candidate vehicle for the first vehicle is determined based on the preset distance range. In this way, focusing only on the second vehicle that is closer to the first vehicle, the calculation amount of the business master kernel of the automatically driven vehicle can be reduced.

In some embodiments, the second validation condition is used to screen the target vehicle from the candidate vehicles. Specifically, the target vehicle is obtained by screening at least one second vehicle based on a preset included angle range. Illustratively, an angle between the speed direction of the candidate vehicle and the speed direction of the first vehicle is calculated, and a second vehicle whose angle is in a range of (5 °,90 °) is taken as the target vehicle. In some special cases, if the first vehicle determines a plurality of target vehicles, weights of cut routes corresponding to the plurality of target vehicles respectively may be determined. The autonomous vehicle plans a travel route of the autonomous vehicle based on weights of the cut routes corresponding to the plurality of target vehicles, respectively.

According to the technical scheme, the plurality of second vehicles in the lane are screened through the first effective condition and the second effective condition to obtain the target vehicle, so that the calculated amount of the main core of the automatic driving vehicle service can be reduced, and the prediction efficiency of the vehicle cutting track is improved.

In an embodiment of the present disclosure, determining a candidate vehicle for a first vehicle based on a preset distance range includes: a second vehicle that appears within a preset distance range of the first vehicle is determined as a candidate vehicle for the first vehicle.

In some embodiments, the predetermined distance range may include a range within F meters forward, B meters rearward, L meters to the left, and R meters rearward of the first vehicle, wherein F, B, L, R is a positive number. The preset distance range is illustratively 60 meters forward, 10 meters rearward, 6 meters left, 6 meters right of the first vehicle. It will be appreciated that the predetermined distance range may be set or adjusted based on the lane width, safety requirements, and brake comfort requirements.

In some embodiments, a coordinate system is constructed centered on the first vehicle, the first vehicle traveling in front of it being longitudinal; the left-right direction of the first vehicle is a lateral direction. Specifically, the position of the first vehicle is considered as the origin (0, 0), and only the second vehicle within 60 meters in front of, 10 meters behind, 6 meters to the left, 6 meters to the right of the first vehicle is considered. If a second vehicle is 70 meters in front of the first vehicle and 8 meters to the left, then the second vehicle is not considered, i.e. the second vehicle is not a candidate vehicle for the first vehicle.

In some embodiments, the sensing module of the first vehicle determines the position of the second vehicle and the first vehicle, the upstream sensing module sends the position of the second vehicle to the track prediction module of the first vehicle, the track prediction module calculates, and when the position of the second vehicle falls into a preset effective range, track prediction for the second vehicle starts to be effective.

According to the technical scheme, the second vehicle which appears in the preset distance range of the first vehicle is determined to be the candidate vehicle of the first vehicle. Therefore, only the second vehicle within the preset distance range is concerned, so that the computing power resource of the business main core can be saved, and the track prediction efficiency is improved.

In an embodiment of the present disclosure, screening a target vehicle from at least one second vehicle based on a preset included angle range includes: determining an included angle between each second vehicle and the first vehicle in the at least one second vehicle; and determining the second vehicle with the included angle meeting the preset included angle range as the target vehicle.

The preset angle range is (5 °,90 °) for example. It will be appreciated that the predetermined distance range may be set or adjusted based on lane width, road topology, safety requirements, and brake comfort requirements.

In some embodiments, determining an angle of the candidate vehicle to the first vehicle includes: the angle between the speed direction of the second vehicle and the speed direction of the first vehicle.

In some embodiments, determining the angle of the second vehicle to the first vehicle comprises: obtaining the speed direction of the candidate vehicle output by the sensing module, and performing linear extrapolation processing in a preset time range based on the speed direction to obtain the speed and direction of a second vehicle in the preset time range; and determining the interaction condition of the second vehicle and the first vehicle based on the speed and the direction of the second vehicle in a preset time range. If the preset time range is 5 seconds, the second vehicle performs linear extrapolation along the speed direction, and cannot invade or pass through the vicinity of the first vehicle within 2 meters, and the second vehicle is considered to have no interaction with the first vehicle.

In some embodiments, determining the angle of the second vehicle to the first vehicle further comprises: obtaining the speed and the direction of the candidate vehicle output by the sensing module, and performing linear extrapolation processing based on the speed and the direction to obtain the speed and the direction of the second vehicle at a preset time point; and determining the interaction condition of the second vehicle and the first vehicle based on the speed and the direction of the second vehicle at the preset time point. The linear extrapolation time sample points may be [1.0,1.5,2.0,2.5,3.0,3.5,4.0,4.5,5.0] (in seconds).

In some embodiments, the sensing module of the first vehicle determines a speed and a direction of the second vehicle, the upstream sensing module sends the speed and the direction of the second vehicle to the track prediction module, and the track prediction module performs linear extrapolation processing based on the speed and the direction of the second vehicle to obtain a second vehicle track or a second vehicle position in a preset time range, so as to obtain an interaction condition of the first vehicle and the second vehicle.

According to the technical scheme, the included angle between each second vehicle in at least one second vehicle and the first vehicle is determined; and determining the second vehicle with the included angle meeting the preset included angle range as the target vehicle. Therefore, the method and the system can determine the target vehicle without depending on map topology, further determine the cut track of the target vehicle, improve the accuracy of cut track prediction, make up for the problem that the cut track accuracy of the model without recalling cut intention or recalling is low under the condition of highly depending on map topology, and are beneficial to improving the safety of the automatic driving vehicle.

In an embodiment of the present disclosure, the at least two constraint points include a first constraint point and a second constraint point. Establishing at least two constraint points based on the position information and the speed information of the target vehicle, including: establishing a first constraint point based on a first position and a first speed direction of the target vehicle; the first position is the position of the target vehicle at a first moment, and the first speed direction is the speed direction of the target vehicle at the first moment; establishing a second constraint point based on a second position and a second speed direction of the target vehicle and whether the target vehicle passes through a first planned track of the first vehicle in a first preset time range; the second position is a position of the target vehicle at a second moment, the second speed direction is a speed direction of the target vehicle at the second moment, the starting point of the first preset time range is the first moment, and the end point is the second moment.

In some embodiments, the first time is a current time; illustratively, the current time is 10:59:25, the first position is the target vehicle 10:59: 25. The first speed direction is the target vehicle at 10:59:25 in the direction of the velocity of the motor.

In some embodiments, if the first time is 10:59:20, the first preset time range is 6 seconds, and the second time may be 10:59:31, the second position is the target vehicle 10:59: 31. The second speed direction is the target vehicle at 10:59:31 in the direction of the velocity.

In some embodiments, the first planned trajectory is a travel trajectory of the first vehicle planned over a first preset time range. In particular, the first planned trajectory may be a route planned in advance by a decision module of the first vehicle.

Fig. 3 shows a schematic drawing of a cut track based on two constraint points, as shown in fig. 3, for an autonomous vehicle in straight line. At a first moment, a first planned track of the autonomous vehicle is straight; the automatic driving vehicle detects that a second vehicle at the right intersection meets the first effective condition and the second effective condition, and the second vehicle at the right intersection is determined to be a target vehicle; establishing a first constraint point and a second constraint point based on the position information and the speed information of the target vehicle; the cut track of the target vehicle is predicted based on the first constraint point and the second constraint point, as shown in (1) of fig. 3.

According to the technical scheme, a first constraint point is established based on a first position and a first speed direction of a target vehicle; a second constraint point is established based on a second position and a second speed direction of the target vehicle and whether the target vehicle traverses a first planned trajectory of the first vehicle in a first preset time range. Therefore, the dependence of track prediction on the map module can be reduced, so that the stability of track prediction is enhanced, and the safety of an automatic driving vehicle is further improved.

In an embodiment of the disclosure, establishing the second constraint point based on the second position and the second speed direction of the target vehicle and whether the target vehicle traverses the first planned trajectory of the first vehicle in the first preset time range may include: under the condition that the target vehicle passes through a first planned track of the first vehicle in a first preset time range, taking a longitudinal position obtained by linearly extrapolating a first target time length based on the longitudinal speed as a longitudinal coordinate of a second constraint point; taking the transverse position of the first vehicle as the transverse coordinate of the second constraint point, and taking the path direction of the first planned track as the speed direction of the second constraint point; under the condition that the target vehicle does not traverse the first planned track of the first vehicle in the first preset time range, taking the longitudinal position obtained by linearly extrapolating the second target duration based on the longitudinal speed as the longitudinal coordinate of the second constraint point; the lateral position of the first vehicle is used as the lateral coordinate of the second constraint point, and the path direction of the first planned track is used as the speed direction of the second constraint point.

In some embodiments, the first target time period is t-t1; where t1 represents the crossing point time. The second target duration is t, which represents the overall track time. The point-of-crossing time is a point in time when the target vehicle crosses the first planned trajectory of the first vehicle.

In some embodiments, if the target vehicle traverses a first planned trajectory of the first vehicle, calculating a traverse point time t1, the overall trajectory time being t=6 seconds, extrapolating (6-t 1) seconds along the first vehicle path direction using the longitudinal speed speed_s, setting the longitudinal position; the lateral position is set as a first vehicle center position; the speed direction is set to be along a path direction of the first vehicle.

In some embodiments, if the target vehicle does not traverse the first planned trajectory of the first vehicle, then extrapolate for 6 seconds directly from speed_s, setting the longitudinal position; transversely selecting the central position of the first vehicle; the speed direction is set to be along a path direction of the first vehicle.

Fig. 4 is a schematic diagram showing a judgment of effective conditions, and as shown in fig. 4, the first vehicle running straight and the target vehicle at the right side road junction are divided into transverse and longitudinal distance limits.

Specifically, in the transverse direction: the extrapolated track crosses the future track of the first vehicle (left blank circle); alternatively, in the case of no traversal, |L _dist |<2 meters (right blank circle); as shown in fig. 4, both the left blank circle and the right blank circle meet the lateral-longitudinal distance limitation, but the right diagonal filled circle does not meet the lateral-longitudinal distance limitation;

in the longitudinal direction: maximum value S of longitudinal distance _max The braking distance of the first vehicle needs to be considered to ensure safety, the acceleration is set to be a= -2 m/s according to the current speed of the first vehicle, the constrained body feeling needs to be considered in the selection of the acceleration, the fact that the acceleration is too small can cause uncomfortable body feeling due to sudden braking is considered, and the braking distance calculation formulas (1) - (4) are shown as follows:

t＝(0-v _adc )/a (1)

dist _stop ＝v _adc *t+0.5*a*t ² (2)

S _max ＝max(dist _stop 60 meters) (3)

S _dist ＝speed _s ×t (4)

Wherein t is the overall track time; v _adc Is the speed of the first vehicle; a is the acceleration of the first vehicle; dist (dist) _stop A stopping distance for the first vehicle; meanwhile, in order to ensure the safety of the first vehicle, a 60-meter protection is added. The final longitudinal distance is limited to S _dist <S _max 。

According to the technical scheme of the embodiment of the disclosure, linear extrapolation is performed according to whether the target vehicle passes through a first planned track of the first vehicle in a first preset time range, and the abscissa and the ordinate of the second constraint point are determined. Thus, the accuracy of track prediction is improved, and the safety of the automatic driving vehicle is improved.

In an embodiment of the present disclosure, the at least two constraint points further include a third constraint point; the trajectory prediction method may further include: and establishing a third constraint point based on a third position and a third speed direction of the target vehicle and whether the target vehicle passes through a second planned track of the first vehicle in a second preset time range, wherein the third position is the position of the target vehicle at a third moment, the third speed direction is the speed direction of the target vehicle at the third moment, and the second preset time range is smaller than the first preset time range.

In some embodiments, the second planned trajectory is a travel trajectory of the first vehicle planned within a second preset time range. In particular, the second planned trajectory may be a route planned in advance by a decision module of the first vehicle.

In some embodiments, the third constraint point is an intermediate constraint point. In some embodiments, if the first time is 10:59:20, the second preset time range is 5 seconds, and the third time may be 10:59:30, the third position being the target vehicle 10:59:30, a position of 30; the third speed direction is where the target vehicle is at 10:59:30, the speed direction of the motor.

Fig. 5 shows a schematic drawing of a cut track based on three constraint points, as shown in fig. 5, of an autonomous vehicle located in a straight lane. At a first moment, a first planned track of the autonomous vehicle is straight; the automatic driving vehicle detects that a second vehicle at the right intersection meets the first effective condition and the second effective condition, and the second vehicle at the right intersection is determined to be a target vehicle; establishing a first constraint point, a second constraint point and a third constraint point based on the position information and the speed information of the target vehicle; and predicting a cut track of the target vehicle based on the first constraint point, the second constraint point and the third constraint point, as shown in (1) of fig. 5. Therefore, compared with the method for predicting the vehicle cutting track based on two constraint points, the method for predicting the vehicle cutting track by three constraint points can further improve the accuracy of track prediction.

Fig. 6 shows a schematic view of an entry scene cut, as shown in fig. 6, when a first vehicle travels straight to a distance from an entry point, it is found that a target vehicle appears at the right entry point. The first vehicle establishes a constraint point 1, a constraint point 2 and a constraint point 3 according to the position information and the speed information of the target vehicle. Based on the constraint points 1, 2 and 3, predicting the vehicle cutting track of the target vehicle, wherein the vehicle cutting track is exactly positioned on the driving lane of the first vehicle, and the first vehicle adjusts the driving speed according to the vehicle cutting track, so that the collision with the target vehicle is avoided, and meanwhile, the driving stability and the passenger comfort are ensured.

According to the technical scheme, a third constraint point is established based on a third position and a third speed direction of the target vehicle and whether the target vehicle passes through a second planned track of the first vehicle in a second preset time range. Therefore, the stability and the accuracy of track drawing can be enhanced under the condition of not depending on map topology, and the safety of an automatic driving vehicle is improved.

In an embodiment of the present disclosure, establishing a third constraint point based on a third position and a third speed direction of the target vehicle and whether the target vehicle traverses a second planned trajectory of the first vehicle in a second preset time range includes: establishing a third constraint point under the condition that the target vehicle passes through a second planned track of the first vehicle in a second preset time range; and under the condition that the target vehicle does not traverse the second planned track of the first vehicle in the second preset time range, a third constraint point is not established.

FIG. 7 shows a schematic view of a bifurcation scene cut, as shown in FIG. 7, with a first vehicle positioned on the left lane of a bifurcation intersection and a second vehicle positioned on the right lane of the same bifurcation intersection; the first planned track of the first vehicle is a leftmost lane entering the bifurcation intersection from the current lane; the first vehicle detects a target vehicle positioned on a right lane, determines key constraint points (constraint point 1, constraint point 2 and constraint point 3) based on the position information and speed information of the target vehicle, draws a cut track of the target vehicle based on the constraint point 1, the constraint point 2 and the constraint point 3, and reschedules the running speed of the first vehicle based on the cut track of the target vehicle, so that the collision with the target vehicle is avoided, and meanwhile, the driving stability and the passenger comfort are ensured.

According to the technical scheme, a third constraint point is established under the condition that a target vehicle passes through a second planned track of a first vehicle in a second preset time range; and under the condition that the target vehicle does not traverse the second planned track of the first vehicle in the second preset time range, a third constraint point is not established. Therefore, whether the third constraint point is established or not is determined according to the actual road conditions, stability and interpretability of track drawing are enhanced, accuracy of track drawing is improved, and safety of an automatic driving vehicle is improved.

In an embodiment of the present disclosure, in a case where the target vehicle traverses a second planned trajectory of the first vehicle in a second preset time range, establishing the third constraint point may include: taking a junction point of the extrapolated track of the target vehicle and the second planned track of the first vehicle as a position of a third constraint point; and taking the path direction of the second planned trajectory as the speed direction of the third constraint point.

According to the technical scheme of the embodiment of the disclosure, the intersection point of the extrapolated track of the target vehicle and the second planned track of the first vehicle is used as the position of the third constraint point. Therefore, the accuracy of the determined third constraint point can be improved, the accuracy of the track prediction of the automatic driving vehicle can be improved, and the safety of the automatic driving vehicle is improved.

In an embodiment of the present disclosure, predicting a cut track of a target vehicle based on at least two constraint points includes: and predicting and obtaining the cut track of the target vehicle by adopting a spline interpolation mode based on at least two constraint points.

In some embodiments, the cut trajectory of the target vehicle is derived based on key constraint points; the cut track of the target vehicle is drawn by means of spline interpolation.

In some embodiments, the cutting track of the target vehicle is drawn in a spline interpolation mode, so that the calculation amount of drawing can be saved, and the stability of drawing is improved.

According to the technical scheme, the cutting track of the target vehicle is predicted and obtained by adopting a spline interpolation mode based on at least two constraint points. Thus, the calculation cost can be saved, and the stability of track drawing can be enhanced.

Embodiments of the present disclosure provide a trajectory prediction apparatus, as shown in fig. 8, which may include: a determining module 801 for determining a target vehicle of a first vehicle; a first establishing module 802, configured to establish at least two constraint points based on the location information and the speed information of the target vehicle; a prediction module 803, configured to predict a cut track of the target vehicle based on at least two constraint points.

In some embodiments, the determining module 801 includes: a determining sub-module for determining a candidate vehicle for the first vehicle based on the preset distance range, the candidate vehicle comprising at least one second vehicle; and the screening sub-module is used for screening the target vehicle from at least one second vehicle based on the preset included angle range.

In some embodiments, the determining submodule is to: a second vehicle that appears within a preset distance range of the first vehicle is determined as a candidate vehicle for the first vehicle.

In some embodiments, the screening submodule is to: determining an included angle between each second vehicle and the first vehicle in the at least one second vehicle; and determining the second vehicle with the included angle meeting the preset included angle range as the target vehicle.

In some embodiments, the at least two constraint points include a first constraint point and a second constraint point, the first set-up module 802 comprising: a first establishing sub-module for establishing a first constraint point based on a first position and a first speed direction of the target vehicle; the first position is the position of the target vehicle at a first moment, and the first speed direction is the speed direction of the target vehicle at the first moment; the second establishing sub-module is used for establishing a second constraint point based on a second position and a second speed direction of the target vehicle and whether the target vehicle passes through a first planned track of the first vehicle in a first preset time range; the second position is a position of the target vehicle at a second moment, the second speed direction is a speed direction of the target vehicle at the second moment, the starting point of the first preset time range is the first moment, and the end point is the second moment.

In some embodiments, the second setup submodule is configured to: under the condition that the target vehicle passes through a first planned track of the first vehicle in a first preset time range, taking a longitudinal position obtained by linearly extrapolating a first target time length based on the longitudinal speed as a longitudinal coordinate of a second constraint point; taking the transverse position of the first vehicle as the transverse coordinate of the second constraint point, and taking the path direction of the first planned track as the speed direction of the second constraint point; under the condition that the target vehicle does not traverse the first planned track of the first vehicle in the first preset time range, taking the longitudinal position obtained by linearly extrapolating the second target duration based on the longitudinal speed as the longitudinal coordinate of the second constraint point; the lateral position of the first vehicle is used as the lateral coordinate of the second constraint point, and the path direction of the first planned track is used as the speed direction of the second constraint point.

In some embodiments, the at least two constraint points further comprise a third constraint point; the trajectory prediction device further includes: a second establishing module (not shown in fig. 8) for establishing a third constraint point based on a third position and a third speed direction of the target vehicle, and whether the target vehicle traverses a second planned trajectory of the first vehicle in a second preset time range, wherein the third position is a position of the target vehicle at a third time, and the third speed direction is a speed direction of the target vehicle at the third time, and the second preset time range is smaller than the first preset time range.

In some embodiments, the second setup module (not shown in fig. 8) includes: and the third establishing sub-module is used for establishing a third constraint point under the condition that the target vehicle passes through a second planned track of the first vehicle in a second preset time range.

In some embodiments, the third setup submodule is configured to: taking a junction point of the extrapolated track of the target vehicle and the second planned track of the first vehicle as a position of a third constraint point; and taking the path direction of the second planned trajectory as the speed direction of the third constraint point.

In some embodiments, the prediction module 803 includes: and the prediction sub-module is used for predicting and obtaining the cut track of the target vehicle by adopting a spline interpolation mode based on at least two constraint points.

It should be understood by those skilled in the art that the functions of each processing module in the trajectory prediction device according to the embodiments of the present disclosure may be understood by referring to the foregoing description of the trajectory prediction method, and each processing module in the trajectory prediction device according to the embodiments of the present disclosure may be implemented by using an analog circuit that implements the functions of the embodiments of the present disclosure, or may be implemented by running software that implements the functions of the embodiments of the present disclosure on an electronic device.

The track prediction device disclosed by the embodiment of the invention can predict the cut track of the target vehicle without depending on map road topology, gives consideration to the cut track recall under the common road topology and complex road topology scene, reduces the influence of the map road topology on the cut track recall, comprehensively analyzes the cut track of the target vehicle by combining the cut track obtained without depending on map road topology prediction and the cut track obtained by depending on map road topology prediction, and can improve the prediction accuracy of the automatic driving vehicle on the cut track of the target vehicle, thereby being beneficial to the automatic driving vehicle to better plan the path and improving the safety of the automatic driving vehicle.

In the above embodiments, the first vehicle may be an autonomous vehicle, and the target vehicle may be a social vehicle.

The embodiment of the disclosure provides a scene diagram of track prediction, as shown in fig. 9.

As described above, the track prediction method provided by the embodiment of the present disclosure is applied to an electronic device. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses.

In particular, the electronic device may specifically perform the following operations:

determining a target vehicle of the first vehicle;

The position information and the speed information of the target vehicle can be acquired from an information acquisition system or a perception module of the automatic driving vehicle.

It should be understood that the scene diagram shown in fig. 9 is merely illustrative and not restrictive, and that various obvious changes and/or substitutions may be made by one skilled in the art based on the example of fig. 9, and the resulting technical solutions still fall within the scope of the disclosure of the embodiments of the present disclosure.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, a computer program product, and an autonomous vehicle.

Fig. 10 shows a schematic block diagram of an example electronic device 1000 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 10, the apparatus 1000 includes a computing unit 1001 that can perform various appropriate actions and processes according to a computer program stored in a Read-Only Memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access Memory (Random Access Memory, RAM) 1003. In the RAM 1003, various programs and data required for the operation of the device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other by a bus 1004. An Input/Output (I/O) interface 1005 is also connected to bus 1004.

Various components in device 1000 are connected to I/O interface 1005, including: an input unit 1006 such as a keyboard, a mouse, and the like; an output unit 1007 such as various types of displays, speakers, and the like; a storage unit 1008 such as a magnetic disk, an optical disk, or the like; and communication unit 1009 such as a network card, modem, wireless communication transceiver, etc. Communication unit 1009 allows device 1000 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks.

The computing unit 1001 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 1001 include, but are not limited to, a central processing unit CPU, a graphics processing unit (Graphics Processing Unit, GPU), various dedicated artificial intelligence (Artificial Intelligence, AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (Digital Signal Processor, DSP), and any suitable processors, controllers, microcontrollers, and the like. The computing unit 1001 performs the respective methods and processes described above, for example, a trajectory prediction method. For example, in some embodiments, the trajectory prediction method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 1000 via ROM 1002 and/or communication unit 1009. When the computer program is loaded into RAM 1003 and executed by computing unit 1001, one or more steps of the trajectory prediction method described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to perform the trajectory prediction method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuitry, field programmable gate arrays (Field Programmable Gate Array, FPGAs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), application-specific standard products (ASSPs), system On Chip (SOC), complex programmable logic devices (Complex Programmable Logic Device, CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access Memory, a read-Only Memory, an erasable programmable read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (Compact Disk Read Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., cathode Ray Tube (CRT) or liquid crystal display (Liquid Crystal Display, LCD) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (Local Area Network, LAN), wide area network (Wide Area Network, WAN) and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. that are within the principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A trajectory prediction method, comprising:

determining a target vehicle of the first vehicle;

predicting a cut track of the target vehicle based on the at least two constraint points.

2. The method of claim 1, wherein the determining the target vehicle of the first vehicle comprises:

determining a candidate vehicle of the first vehicle based on a preset distance range, wherein the candidate vehicle comprises at least one second vehicle;

And determining the target vehicle from the at least one second vehicle based on a preset included angle range.

3. The method of claim 2, wherein the determining the candidate vehicle for the first vehicle based on the preset distance range comprises:

a second vehicle that appears within the preset distance range of the first vehicle is determined as the candidate vehicle for the first vehicle.

4. The method of claim 2, wherein the determining the target vehicle from the at least one second vehicle based on a preset range of angles comprises:

determining an angle between each second vehicle of the at least one second vehicle and the first vehicle;

and determining the second vehicle with the included angle meeting the preset included angle range as the target vehicle.

5. The method of claim 1, wherein the at least two points of constraint include a first point of constraint and a second point of constraint, the establishing at least two points of constraint based on the location information and the speed information of the target vehicle comprising:

establishing a first constraint point based on a first position and a first speed direction of the target vehicle; wherein the first position is a position of the target vehicle at a first time, and the first speed direction is a speed direction of the target vehicle at the first time;

Establishing a second constraint point based on a second position and a second speed direction of the target vehicle and whether the target vehicle traverses a first planned track of the first vehicle in a first preset time range; the second position is a position of the target vehicle at a second moment, the second speed direction is a speed direction of the target vehicle at the second moment, a starting point of the first preset time range is the first moment, and an end point of the first preset time range is the second moment.

6. The method of claim 5, wherein the establishing a second constraint point based on the second location and the second speed direction of the target vehicle and whether the target vehicle traversed a first planned trajectory of the first vehicle over a first preset time range comprises:

under the condition that the target vehicle passes through a first planned track of the first vehicle in the first preset time range, taking a longitudinal position obtained by linearly extrapolating a first target duration based on a longitudinal speed as a longitudinal coordinate of the second constraint point; taking the transverse position of the first vehicle as the transverse coordinate of the second constraint point, and taking the path direction of the first planned track as the speed direction of the second constraint point;

Under the condition that the target vehicle does not traverse a first planned track of the first vehicle in the first preset time range, taking a longitudinal position obtained by linearly extrapolating a second target duration based on the longitudinal speed as a longitudinal coordinate of the second constraint point; and taking the transverse position of the first vehicle as the transverse coordinate of the second constraint point, and taking the path direction of the first planned track as the speed direction of the second constraint point.

7. The method of claim 5, wherein the at least two constraint points further comprise a third constraint point; the method further comprises the steps of:

and establishing the third constraint point based on a third position and a third speed direction of the target vehicle and a second planned trajectory of the target vehicle in a second preset time range of whether the target vehicle passes through the first vehicle, wherein the third position is the position of the target vehicle at a third moment, the third speed direction is the speed direction of the target vehicle at the third moment, and the second preset time range is smaller than the first preset time range.

8. The method of claim 7, wherein the establishing a third constraint point based on a third position and a third speed direction of the target vehicle and whether the target vehicle traverses a second planned trajectory of the first vehicle over a second preset time range comprises:

And establishing the third constraint point under the condition that the target vehicle passes through a second planned track of the first vehicle in the second preset time range.

9. The method of claim 8, wherein the establishing the third constraint point if the target vehicle traverses a second planned trajectory of the first vehicle at the second preset time range comprises:

taking a junction of the extrapolated track of the target vehicle and the second planned track of the first vehicle as a location of the third constraint point;

and taking the path direction of the second planned trajectory as the speed direction of the third constraint point.

10. The method of claim 1, wherein the predicting the cut trajectory of the target vehicle based on the at least two constraint points comprises:

and predicting the cut track of the target vehicle by adopting a spline interpolation mode based on the at least two constraint points.

11. A trajectory prediction device, comprising:

a determination module for determining a target vehicle of the first vehicle;

And the prediction module is used for predicting the cut track of the target vehicle based on the at least two constraint points.

12. The apparatus of claim 11, wherein the means for determining comprises:

a determination sub-module for determining a candidate vehicle for the first vehicle based on a preset distance range, the candidate vehicle comprising at least one second vehicle;

and the screening sub-module is used for determining the target vehicle from the at least one second vehicle based on a preset included angle range.

13. The apparatus of claim 12, wherein the determination submodule is configured to:

14. The apparatus of claim 12, wherein the screening submodule is to:

15. The apparatus of claim 11, wherein the at least two constraint points comprise a first constraint point and a second constraint point, the first establishment module comprising:

A first establishing sub-module for establishing a first constraint point based on a first position and a first speed direction of the target vehicle; wherein the first position is a position of the target vehicle at a first time, and the first speed direction is a speed direction of the target vehicle at the first time;

the second establishing sub-module is used for establishing a second constraint point based on a second position and a second speed direction of the target vehicle and whether the target vehicle passes through a first planned track of the first vehicle in a first preset time range; the second position is a position of the target vehicle at a second moment, the second speed direction is a speed direction of the target vehicle at the second moment, a starting point of the first preset time range is the first moment, and an end point of the first preset time range is the second moment.

16. The apparatus of claim 15, wherein the second setup submodule is to:

17. The apparatus of claim 15, wherein the at least two constraint points further comprise a third constraint point; the apparatus further comprises:

the second establishing module is configured to establish the third constraint point based on a third position and a third speed direction of the target vehicle, and a second planned trajectory of the target vehicle in a second preset time range, where the third position is a position of the target vehicle at a third time, the third speed direction is a speed direction of the target vehicle at the third time, and the second preset time range is smaller than the first preset time range.

18. The apparatus of claim 17, wherein the second setup module comprises:

And the third establishing sub-module is used for establishing the third constraint point under the condition that the target vehicle passes through a second planned track of the first vehicle in the second preset time range.

19. The apparatus of claim 18, wherein the third setup submodule is to:

20. The apparatus of claim 11, wherein the prediction module comprises:

and the prediction sub-module is used for predicting and obtaining the cut track of the target vehicle by adopting a spline interpolation mode based on the at least two constraint points.

21. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

22. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-10.

23. A computer program product comprising a computer program stored on a storage medium, which, when executed by a processor, implements the method according to any of claims 1-10.

24. An autonomous vehicle comprising the electronic device of claim 21.