Vehicle trajectory prediction method and device, electronic equipment and storage medium

Abstract

This invention provides a vehicle trajectory prediction method, apparatus, electronic device, and storage medium. The method includes: predicting first driving state information of the first vehicle when it is no longer detected by a target radar; the first driving state information indicates whether the first vehicle tends to remain stationary or move laterally away after disappearing from the target radar's detection results; if the first driving state information indicates that the first vehicle tends to remain stationary, then determining first motion state information of the first vehicle; the first motion state information indicates the first position and first speed of the first vehicle queuing in the target lane when it begins to disappear from the target radar's detection results; and predicting second motion state information of the first vehicle based on the first motion state information for trajectory tracking prediction. This solution addresses the problem of low vehicle detection rate when starting in queuing scenarios at intersections.

Description

Technical Field

[0001] This invention relates to the field of road traffic technology, and in particular to a vehicle trajectory prediction method, device, electronic device, and storage medium. Background Technology

[0002] In the crucial field of intelligent transportation, millimeter-wave radar demonstrates unique application value due to its outstanding characteristics. It boasts low energy consumption, achieves long detection distances, and maintains strong anti-interference capabilities even in harsh environments such as rain, snow, fog, and nighttime. Millimeter-wave radar holds immense application potential in areas such as precise target trajectory tracking and effective traffic flow detection. Compared to cameras and integrated radar-visual systems, single-radar products offer significant cost advantages, making them a focal point of attention within the industry.

[0003] However, when a single radar is applied to intersection scenarios, some significant drawbacks are exposed. Intersection environments are unique, often containing numerous stationary targets. The current processing strategy is to assume a target is stationary once its speed decreases to a certain threshold, and then attempt to associate it with a new moving target. However, due to the limitations of millimeter-wave radar's angular resolution, in situations where multiple lanes are activated simultaneously, if the lane width is narrow and the distance between vehicles is close, adjacent lanes with similar distances and speeds may be incorrectly classified as the same target during clustering. For example, when queuing vehicles start moving, it's possible that two stationary targets might exist, but only one is detected, leaving one stationary target behind in the system. Furthermore, as the number of vehicles in the queue increases, this target loss phenomenon occurs frequently in various lanes, ultimately having a severe negative impact on traffic flow detection indicators, causing significant deviations in the flow detection data and failing to accurately reflect the actual traffic situation. Summary of the Invention

[0004] This invention provides a vehicle trajectory prediction method, device, electronic device, and storage medium to solve the problem of low detection rate when vehicles start in intersection queuing scenarios.

[0005] In a first aspect, embodiments of the present invention provide a vehicle trajectory prediction method, the method comprising:

[0006] When the first vehicle is no longer detected by the target radar, the first driving state information of the first vehicle is predicted. The first vehicle is a vehicle queuing in the target driving lane and is within the detection range of the target radar. The first driving state information is used to indicate whether the first vehicle tends to be stationary or tends to move laterally away after disappearing from the detection results of the target radar. The lateral departure state is the state in which the vehicle moves away from the driving lane to one side of the driving lane.

[0007] If the first driving state information indicates that the first vehicle is approaching a stationary state, then the first motion state information of the first vehicle is determined. The first motion state information is used to indicate the first position and first speed of the first vehicle in the target driving lane when it begins to disappear from the detection result of the target radar.

[0008] Based on the first motion state information, the second motion state information of the first vehicle is predicted to predict the trajectory of the first vehicle in the target driving lane. Each second motion state information is used to indicate the second position and second speed of the first vehicle at each deceleration moment during the deceleration process phase. The deceleration process phase is the driving phase in which the first vehicle gradually decelerates from the moment it disappears from the detection result of the target radar until the first vehicle is stationary. Each deceleration moment is the detection time corresponding to each frame generated by the target radar in performing the detection task during the deceleration process phase.

[0009] Secondly, embodiments of the present invention also provide a vehicle trajectory prediction device, the device comprising:

[0010] The first prediction module is used to predict the first driving state information of the first vehicle when the first vehicle is no longer present in the detection results of the target radar. The first vehicle is a vehicle queuing in the target driving lane and is within the detection range of the target radar. The first driving state information is used to indicate whether the first vehicle tends to be stationary or tends to be laterally driven away after disappearing from the detection results of the target radar. The lateral driving away state is the state in which the vehicle moves away from the driving lane to one side of the driving lane.

[0011] The determination module is used to determine the first motion state information of the first vehicle if the first driving state information indicates that the first vehicle is tending to be stationary. The first motion state information is used to indicate the first position and first speed of the first vehicle in the target driving lane when it begins to disappear from the detection result of the target radar.

[0012] The second prediction module is used to predict the second motion state information of the first vehicle based on the first motion state information, and to perform trajectory tracking prediction of the first vehicle in the target driving lane. Each second motion state information is used to indicate the second position and second speed of the first vehicle at each deceleration moment during the deceleration process phase. The deceleration process phase is the driving phase in which the first vehicle gradually decelerates from the moment it disappears from the detection result of the target radar until the first vehicle is stationary. Each deceleration moment is the detection time corresponding to each frame generated by the target radar in performing the detection task during the deceleration process phase.

[0013] Thirdly, this invention also provides an electronic device, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the vehicle trajectory prediction method described in any of the above embodiments.

[0017] Fourthly, this invention also provides a computer-readable medium storing computer instructions that, when executed by a processor, implement the vehicle trajectory prediction method described in any of the above embodiments.

[0018] The technical solution of this invention, by predicting the first driving state information of the first vehicle after it disappears from the target radar detection result, can determine whether the first vehicle tends to stand still or move laterally away after disappearing from the target radar detection result. This helps to grasp the driving trend of the first vehicle in a timely manner when the radar can no longer detect the first vehicle due to deceleration to a low speed. Furthermore, when it is determined that the first vehicle tends to stand still, the first position and first speed of the first vehicle queuing in the target driving lane when it begins to disappear from the target radar detection result can also be determined as the first motion state information of the first vehicle. The first motion state information can be used as a basis to realize the second position and second speed of the first vehicle at each deceleration processing moment during the deceleration processing phase after it disappears from the target radar detection result, thereby realizing the first vehicle's second motion state information. The system predicts the trajectory of a vehicle as it gradually decelerates from disappearing from the target radar's detection until it comes to a standstill. This approach can adapt to various complex dynamic changes when the first vehicle is queuing in the target lane. When a vehicle can no longer be detected by the target radar due to low speed, whether it is a temporary stop caused by a malfunction of the vehicle ahead or a vehicle intending to laterally leave the lane, the system can update the vehicle's trajectory information in a timely manner through prediction and analysis of its driving and motion status. To a certain extent, this compensates for the insufficient detection effect of a single target radar on the first vehicle when it is slow and stationary in the target lane, improving the accuracy and reliability of detecting the first vehicle in low-speed and stationary states in the target lane. This provides strong technical support and data assurance for many fields such as traffic flow monitoring, intelligent driving assistance, and road safety management.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0021] Figure 1 This is a schematic flowchart of a vehicle trajectory prediction method provided in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of multiple vehicles queuing in a driving lane, provided by an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of another vehicle trajectory prediction method provided in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of another vehicle trajectory prediction method provided in an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of a vehicle trajectory prediction device provided in an embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram of the structure of an electronic device for implementing a vehicle trajectory prediction method provided in an embodiment of the present invention. Detailed Implementation

[0027] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0028] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0029] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0030] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0031] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0032] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0033] Figure 1 This is a flowchart illustrating a vehicle trajectory prediction method provided in an embodiment of the present invention. The present invention is applicable to situations where vehicles queuing in a driving lane can no longer be detected by radar due to deceleration to a lower speed. The method can be executed by a vehicle trajectory prediction device, which can be implemented in the form of software and / or hardware, and is generally integrated into any electronic device with network communication capabilities, such as a mobile terminal, PC, or server.

[0034] like Figure 1 As shown, the vehicle trajectory prediction method of this invention may include the following process:

[0035] S110. When the first vehicle is no longer present in the detection results of the target radar, the first driving state information of the first vehicle is predicted. The first vehicle is a vehicle that is queuing in the target driving lane and is within the detection range of the target radar. The first driving state information is used to indicate whether the first vehicle tends to be stationary or tends to move laterally away after disappearing from the detection results of the target radar. The lateral departure state is the state in which the vehicle moves away from the driving lane to one side of the driving lane.

[0036] The target radar can cover and detect multiple parallel lanes. This radar can be a millimeter-wave radar, a radar technology that uses millimeter-wave frequencies (typically between 30 GHz and 300 GHz). Millimeter-wave radar has wide applications in autonomous vehicles, drones, security systems, industrial automation, and communications. For each lane within the target radar's detection range, lane position information, lane attribute information, and lane-specific information for each lane are pre-configured and stored.

[0037] Lane attribute information can include lane direction (direction of travel) and lane driving direction (left turn, right turn, straight ahead). Lane direction can be set based on the road layout in geographical space, mainly reflecting the macroscopic direction of the road. It distinguishes the relative positional relationship of lanes from the overall perspective, such as whether it is a north-south lane or an east-west lane, or the direction of a road relative to the starting point and the ending point. This direction division is relatively fixed and is a basic attribute of road infrastructure planning. Lane driving direction focuses more on the specific dynamic operation direction of vehicles within the lane. It guides vehicles to turn left, turn right, or go straight at intersections, ramps, etc., according to traffic rules and actual traffic needs. It is the direction of travel chosen by vehicles in a short period of time during the journey.

[0038] Lane-specific information includes lateral departure area information (location of the lateral departure area) and left-turn waiting area information (length of the left-turn waiting area and its associated lane). The lateral departure area can include locations such as intersections and U-turn areas within the driving lane. It is an area designated for vehicles to move laterally away from the driving lane at specific locations within the lane. In essence, the lateral departure area refers to the specific spatial range involved when a vehicle (or other moving target) departs from its current lane (or path) in a traffic scenario or other target movement scenario. This area is a relative concept, and its boundaries are typically determined by the target's starting position, possible lateral movement direction, and distance.

[0039] See Figure 2 , Figure 2 The two diagrams on the left and right show the distribution of vehicles queuing in the driving lanes. Figure 2 The first vehicle shown in the middle right figure is the vehicle in the middle of the queue (that is, there must be a vehicle in front of the first vehicle and there may be a vehicle behind the first vehicle). At this time, the first vehicle will be close to the stop line of the lane in which the first vehicle is located. Figure 2 The first vehicle shown in the middle left figure is the vehicle at the very front of the queue (i.e., there are no vehicles in front of the first vehicle), and at this time, the first vehicle is adjacent to the vehicle in front of it.

[0040] See Figure 2For vehicles queuing in a driving lane, each vehicle will slow down to avoid collisions. Under these circumstances, the first vehicle is positioned in the target lane and is waiting in line, such as at a red light or in a congested area. Simultaneously, the first vehicle is within the detection range of the target radar, meaning that under normal circumstances, the target radar can obtain relevant information about the first vehicle, such as its position and speed. However, when using target radar for detection, low-speed vehicles may fail to be detected, causing the first vehicle to disappear from the target radar's detection results.

[0041] See Figure 2 When the first vehicle detected by the target radar can no longer be detected by the target radar, although the first vehicle has disappeared from the target radar's detection results, it was previously queuing in the target driving lane and within the radar's detection range. Therefore, the first vehicle is very likely still in the driving lane, but cannot be detected because the radar has low sensitivity to low-speed targets. Thus, it is necessary to determine the first vehicle's initial driving state after it disappears from the target radar's detection results. This is a predictive information about the first vehicle's possible subsequent behavior, with two possible outcomes: either the first vehicle tends to enter a stationary state in the target driving lane, or the first vehicle tends to move laterally away from the target driving lane (i.e., move to one side from its current driving lane and leave the driving lane). This is based on reasonable assumptions about the first vehicle's subsequent behavior, because in actual traffic scenarios, vehicles usually enter one of these two states after losing detection.

[0042] The first driving state information can be a descriptive description of the first vehicle's driving tendency after it disappears from the target radar's detection field of view. Specifically, it can be categorized as either tending towards a stationary state or tending towards a lateral departure state. When the first vehicle disappears from the target radar's detection results for various reasons (such as slowing down or preparing to change lanes), algorithms and data analysis are needed to predict whether the first vehicle is more likely to gradually decelerate until it stops (tending towards a stationary state) or move towards one side of the lane and eventually leave the lane completely (tending towards a lateral departure state). Predicting the first driving state information helps in further judging and processing the first vehicle's behavior, such as in intelligent transportation systems, deciding whether to issue warnings to surrounding vehicles or adjust traffic signal strategies.

[0043] Lateral departure is a specific definition of a vehicle's driving state, indicating that the vehicle no longer travels in a straight line along its current lane but begins to move laterally to one side of the lane, ultimately leaving the lane completely. For example, a vehicle preparing to turn into another road at an intersection is in a lateral departure state. Accurate identification of lateral departure is crucial for traffic management and safety, as it involves changes in vehicle trajectory and interactions with vehicles in other lanes.

[0044] Specifically, for a state approaching stillness: the vehicle is gradually slowing down, its kinetic energy is continuously decreasing, the rotational speed of the wheels is approaching zero, and the body is trembling slightly as it stabilizes. In the near future, it will come to a steady stop in its current position, achieving a state of stillness. For a state approaching lateral departure: the vehicle is now showing an intention to move laterally. The steering wheel begins to turn slowly, the body posture gradually tilts and adjusts, and the front of the car is slowly pointing to the side, gradually generating a significant lateral component. It is about to begin a new displacement trajectory along a lateral path, leaving the straight-line extension range of the current driving lane.

[0045] S120. If the first driving state information indicates that the first vehicle is approaching a stationary state, then the first motion state information of the first vehicle is determined. The first motion state information is used to indicate the first position and first speed of the first vehicle in the target driving lane when it begins to disappear from the detection result of the target radar.

[0046] If the initial driving state information indicates that the first vehicle is approaching a stationary state, then the initial motion state information of the first vehicle needs to be further determined. This initial motion state information mainly describes the first position and first velocity of the first vehicle when it begins to disappear from radar detection. This is crucial state information, providing a starting point for subsequent trajectory tracking of the vehicle. The first position and first velocity are fundamental physical quantities describing vehicle motion. This set of data at the instant of disappearance from radar detection can help reconstruct the vehicle's subsequent trajectory because, based on this initial information and combined with the physical laws governing vehicle motion, possible subsequent changes in the vehicle's position and velocity can be inferred.

[0047] The first position refers to the coordinates of the last location of the first vehicle detected by the target radar in the target lane within the entire road coordinate system. This first position helps determine the specific location of the first vehicle in the target lane when it disappears from the target radar's detection results, such as its distance from the intersection of the target lane and its lateral position within the target lane. The first speed can be the speed of the first vehicle at the instant it disappears from the target radar's detection results. This first speed value is crucial for predicting the deceleration process and final stopping position of the first vehicle, because the deceleration of the first vehicle is based on its initial speed and some physical laws (such as friction and air resistance). Knowing the initial speed allows for a more accurate simulation and prediction of the vehicle's subsequent trajectory.

[0048] S130. Based on the first motion state information, predict the second motion state information of the first vehicle to perform trajectory tracking prediction of the first vehicle in the target driving lane. Each second motion state information is used to indicate the second position and second speed of the first vehicle at each deceleration moment during the deceleration process phase. The deceleration process phase is the driving phase in which the first vehicle gradually decelerates from the moment it disappears from the detection results of the target radar until the first vehicle is stationary. Each deceleration moment is the detection time corresponding to each frame generated by the target radar in performing the detection task during the deceleration process phase.

[0049] The deceleration phase can be the entire driving process from when the first vehicle begins to disappear from the target radar's detection results until it comes to a complete stop. During this phase, the vehicle's speed gradually decreases due to factors such as reduced power or active braking, until it reaches zero. This deceleration phase is crucial for traffic analysis and vehicle trajectory prediction because it involves the vehicle's final stopping position on the road and its potential interactions with other vehicles or road infrastructure during deceleration.

[0050] Because the target radar performs detection tasks at certain time intervals (frame rates), each detection generates one frame of data, and the detection time corresponding to each frame corresponds to a deceleration processing moment. These deceleration processing moments divide the deceleration process into discrete time points, facilitating the analysis and recording of the vehicle's motion state at each time point. For example, if the target radar's detection frame rate is 10Hz, then there will be a deceleration processing moment every 0.1 seconds. At each such moment, the position and speed of the first vehicle can be predicted and recorded, thereby constructing the vehicle's complete motion trajectory during the deceleration processing phase.

[0051] Based on the first motion state information, at least one second motion state information of the first vehicle can be predicted. This second motion state information is time-series data covering the position and speed of the first vehicle at multiple deceleration moments within a deceleration phase. The deceleration phase begins when the first vehicle disappears from radar detection and continues until it finally comes to a standstill. Each deceleration moment corresponds to the detection time of each frame generated by the target radar within that deceleration phase. In this way, detailed motion information of the vehicle throughout the entire continuous driving process, from its initial disappearance from the target radar's detection to its final standstill, can be obtained. This information helps to more comprehensively understand the dynamic changes of the vehicle during this process, rather than being limited to the state at the moment of initial disappearance.

[0052] Finally, based on multiple second motion state information, the trajectory of the first vehicle in the target driving lane from the moment it disappears from the target radar detection results to its final standstill can be tracked and predicted. By analyzing the position and speed of the first vehicle at different deceleration moments during the deceleration process, relevant mathematical and physical models can be used, such as the formula for uniformly accelerated linear motion or more complex kinematic equations, to predict the trajectory of the first vehicle. This prediction is very important for traffic management, autonomous driving, and other fields, as it can help to anticipate vehicle actions in advance and avoid potential collisions or traffic congestion.

[0053] After a vehicle is no longer detected by the target radar, this technical solution can proactively predict the vehicle's driving state, whether it is approaching a standstill or moving laterally away, which greatly expands the perception range of traffic scenarios. In real traffic environments, vehicles often fail to be detected by radar for various reasons, such as weakened radar echoes due to deceleration, making them difficult to detect. This solution avoids the loss of traffic information caused by vehicles "disappearing," providing traffic management systems with more comprehensive traffic situation information. By determining the first motion state information and further predicting the second motion state information, accurate trajectory tracking of the vehicle from disappearance to standstill can be achieved. This accurate trajectory prediction is of great value in many traffic application scenarios. Furthermore, this solution provides an effective compensation for the shortcomings of single-target radar in detecting low-speed and stationary vehicles. When the vehicle speed is low or it is stationary, the detection accuracy and reliability of the radar often decrease, potentially leading to missed or false detections. By performing predictive analysis based on the vehicle's previous motion state information, a relatively accurate judgment of the vehicle's state and trajectory can still be made even when radar detection fails. This is especially important in certain special scenarios, such as in severe weather conditions (e.g., heavy fog, heavy rain), where radar detection capabilities are limited. This technology can utilize the vehicle's previous motion information to maintain vehicle tracking and management to a certain extent, ensuring the normal operation of traffic.

[0054] As an optional but not limited implementation, the second motion state information of the first vehicle is predicted based on the first motion state information, including the following steps A1-A2:

[0055] Step A1: Determine the third position corresponding to the first vehicle. The third position is the predicted position of the first vehicle when it is stationary after disappearing from the detection results of the target radar.

[0056] Step A2: Based on the third position corresponding to the first vehicle and the first position and first speed indicated by the first motion state information, predict and determine the second motion state information of the first vehicle.

[0057] The third position is a crucial predicted position information when tracking and predicting the first vehicle. It represents the expected spatial location of the first vehicle after it disappears from the target radar's detection, undergoing a series of deceleration movements to finally come to a stop. The third position can be determined based on the position of vehicles stationary in front of the first vehicle, or the stop line position of the target lane where the first vehicle is located when there are no other vehicles in front of it. Specifically, if there are other vehicles in front of the first vehicle (but the first vehicle is not the first vehicle in the target lane), it is predicted that the first vehicle will decelerate uniformly, and its final third position will be 1 meter behind the rear of the vehicle in front. This setting ensures a reasonable safe distance between vehicles in traffic scenarios, avoiding collisions and conforming to normal parking queuing regulations. If the first vehicle is the first vehicle (no other vehicles in front of it), then the final predicted third position of the first vehicle's front end is 1 meter behind the stop line, and its speed will just reach 0 when it reaches this position.

[0058] Alternatively, other factors can be considered when determining the third position, such as environmental factors like the slope of the vehicle's lane and the coefficient of road surface friction, as well as mechanical characteristics like the vehicle's braking performance. For example, if the road is downhill, the vehicle will slide a certain distance after losing power due to gravity before stopping, so the third position will be farther than on a flat road; if the vehicle has good braking performance, the distance required to stop will be relatively shorter, and the third position will be closer to the first position when it disappeared.

[0059] See Figure 2The first position and first velocity represent the motion state of the first vehicle when it begins to disappear from the target radar detection results. The first position describes the starting point of the first vehicle in the road coordinate system when it begins to disappear from the target radar detection results, while the first velocity indicates the initial speed of the first vehicle when it begins to disappear from the target radar detection results. The third position is the predicted position of the first vehicle from the start of disappearance from the target radar detection results until it finally comes to a stop. The first, second, and third positions constitute a complete motion process of the first vehicle. By applying kinematic principles and related mathematical models, such as the formula for uniformly decelerated linear motion (considering that the vehicle deceleration process can usually be approximated as uniformly decelerated motion), the instantaneous velocity and corresponding position of the first vehicle at each deceleration moment during the deceleration process from the start of disappearance from the target radar detection results to a stop can be calculated based on the three key pieces of information: the first position, the first velocity, and the third position. This velocity and position information at each deceleration moment constitutes the second motion state information.

[0060] For example, see Figure 2 Given the position s1 when the first vehicle begins to disappear from the target radar detection results, we need to predict the position s2 when the first vehicle begins to disappear from the target radar detection results until it finally comes to a stop. Figure 2 In the left figure, S2 represents the preset distance position corresponding to the rear of the vehicle in front of the first vehicle (or the preset distance position corresponding to the rear of the adjacent stop line in front of the first vehicle). It can be seen that the displacement of the first vehicle, from the moment it disappears from the target radar detection result until it finally comes to a stop, is Δs = abs(s1-s2). Given that the velocity of the first vehicle when it begins to disappear from the target radar detection result is v1, and the velocity of the first vehicle when it disappears from the target radar detection result until it comes to a stop is 0, it can be determined that the acceleration of the first vehicle at each deceleration moment during this deceleration process is a = -(v1)² / (2*Δs). If the time interval between two adjacent detection frames during target radar detection is Δt, then the position and velocity information of the first vehicle at each detection time corresponding to the detection task generated by the target radar during the deceleration process can be calculated until the first vehicle stops.

[0061] Based on the above embodiments, the vehicle trajectory prediction method of the present invention may optionally include the following process:

[0062] If the first driving status information indicates that the first vehicle is tending to move laterally away, then the first vehicle that has disappeared from the detection results of the target radar is tracked and predicted according to the lateral departure state.

[0063] See Figure 2When analysis and prediction reveal that the first vehicle is trending towards lateral departure, it means the vehicle is likely to shift to one side from its target lane and eventually leave the lane. For example, the vehicle might be preparing to change lanes into an adjacent lane, move to a roadside parking space, enter a ramp, or head towards a specific exit area. In this lateral departure scenario, subsequent processing will rely on the premise that the vehicle is in a lateral departure state to track and predict its movement. This involves a series of algorithms and models specifically designed for lateral departure scenarios. For instance, it will combine lane geometry (such as curve curvature and lane width changes), traffic rules (such as whether turning is permitted at specific locations or if there are traffic sign restrictions), and the typical lateral departure patterns of vehicles (such as turning radius and speed change curves) to predict the vehicle's likely position and speed at each subsequent time point. By continuously updating this predictive information, even after the target radar temporarily loses direct detection of the first vehicle, it is still possible to effectively simulate and track the vehicle's trajectory. This ensures that traffic management systems, intelligent driving assistance systems, and other systems can keep abreast of the vehicle's dynamics and make appropriate decisions, such as providing early warning information to surrounding vehicles, adjusting traffic signal strategies to adapt to changes in vehicle flow, or planning reasonable avoidance paths for autonomous vehicles.

[0064] Based on the above embodiments, optionally, the vehicle trajectory prediction method of the present invention further includes the following steps:

[0065] If a vehicle is detected by the target radar within the third distance area corresponding to the first vehicle, the vehicle detected by the target radar within the third distance area will be bound to the first vehicle, which is predicted to be located at the third position, as the same vehicle. The third distance area extends from the third position of the first vehicle toward the front of the first vehicle and is within the third distance from the third position. The third position is the predicted position of the first vehicle when it is stationary after disappearing from the detection result of the target radar.

[0066] See Figure 2 The third distance region is defined as the third predicted stationary position of the first vehicle after it disappears from the target radar detection result, extending forward from the original direction of travel of the first vehicle. The extended range is within a third distance (the size of the third distance is determined based on the vehicle's length and the safe distance between vehicles when parked). For example, if the third position is a specific coordinate point on a road, and the third distance is set to 5 meters, then the third distance region is the portion of the road covered by extending 5 meters forward from that coordinate point along the original direction of travel of the first vehicle.

[0067] When a vehicle is detected by the target radar within the third distance area, the detected vehicle is bound to the first vehicle previously predicted to be at the third position. This is based on a logical judgment that the vehicle detected in this specific area is highly likely to be the first vehicle that disappeared and was predicted to be stationary at the third position. This binding is based on multiple factors, including positional relationships, the continuity and logic of vehicle movement. For example, in a traffic scenario, if a vehicle disappears from the radar's field of view at a certain location, it is predicted to be stationary at a certain position ahead based on its previous motion state. When another vehicle is detected in a specific area near this predicted stationary position, after a series of verifications (such as the reasonableness of speed and direction changes), if the conditions are met, it is considered the same vehicle as the previously disappeared vehicle, thus achieving the association and unified management of vehicle identity across different detection stages and states.

[0068] This mechanism of binding vehicle detection and prediction of stationary vehicles within a third distance region effectively maintains the continuity of vehicle identity throughout the entire driving process. Once the detected vehicle is successfully bound to the predicted vehicle, the newly detected vehicle information can be further utilized to correct and improve the previous trajectory prediction of the first vehicle.

[0069] The technical solution of this invention, by predicting the first driving state information of the first vehicle after it disappears from the target radar detection result, can determine whether the first vehicle tends to stand still or move laterally away after disappearing from the target radar detection result. This helps to grasp the driving trend of the first vehicle in a timely manner when the radar can no longer detect the first vehicle due to deceleration to a low speed. Furthermore, when it is determined that the first vehicle tends to stand still, the first position and first speed of the first vehicle queuing in the target driving lane when it begins to disappear from the target radar detection result can also be determined as the first motion state information of the first vehicle. The first motion state information can be used as a basis to realize the second position and second speed of the first vehicle at each deceleration processing moment during the deceleration processing phase after it disappears from the target radar detection result, thereby realizing the first vehicle's second motion state information. The system predicts the trajectory of a vehicle as it gradually decelerates from disappearing from the target radar's detection until it comes to a standstill. This approach can adapt to various complex dynamic changes when the first vehicle is queuing in the target lane. When a vehicle can no longer be detected by the target radar due to low speed, whether it is a temporary stop caused by a malfunction of the vehicle ahead or a vehicle intending to laterally leave the lane, the system can update the vehicle's trajectory information in a timely manner through prediction and analysis of its driving and motion status. To a certain extent, this compensates for the insufficient detection effect of a single target radar on the first vehicle when it is slow and stationary in the target lane, improving the accuracy and reliability of detecting the first vehicle in low-speed and stationary states in the target lane. This provides strong technical support and data assurance for many fields such as traffic flow monitoring, intelligent driving assistance, and road safety management.

[0070] Figure 3 This is a flowchart illustrating another vehicle trajectory prediction method provided by an embodiment of the present invention. The technical solution of this embodiment further optimizes the process of predicting the first driving state information of the first vehicle in the foregoing embodiments based on the technical solution of the above embodiments. This embodiment can be combined with various optional solutions in one or more of the above embodiments.

[0071] like Figure 3 As shown, the vehicle trajectory prediction method of this invention may include the following process:

[0072] S310. When the first vehicle is no longer present in the detection results of the target radar, detect whether the first position of the first vehicle is located in the lateral departure area of ​​the target driving lane. The first position is the position where the first vehicle begins to disappear from the detection results of the target radar. The lateral departure area is the area on the driving lane that provides the vehicle with the position required to move to one side of the driving lane and leave the driving lane.

[0073] S320. Detect whether there is a stationary second vehicle within a first distance area of ​​the first vehicle. The first distance area is a position area that extends from the first position of the first vehicle toward the front of the first vehicle and is within a first distance from the first position.

[0074] See Figure 2 The first distance region is defined as the area extending a first distance forward from the target vehicle's original position within the same lane as the target lane where the first vehicle was initially located, starting from the first vehicle's initial position when it disappears from the target radar's detection results. This region is designed to search for other stationary vehicles within a reasonable spatial range that might affect the judgment of the first vehicle's driving status. A second vehicle can be a stationary vehicle detected within the first distance region. The presence or absence of a second vehicle and its relative position to the first vehicle significantly impacts the judgment of the first vehicle's subsequent driving status; for example, it might become the vehicle ahead of the first vehicle in a stop queue, or affect whether the first vehicle can laterally leave the target lane. If the first vehicle is traveling in the target lane and within the target radar's detection range, but disappears from the target radar's detection results at some point, the system will detect whether the first vehicle's initial position is within the lateral departure area of ​​the target lane and whether a stationary second vehicle exists within the first distance region of the first vehicle.

[0075] Optionally, when the second vehicle is the closest vehicle to the stop line of the target driving lane in the target driving lane where the first vehicle is located, it is detected whether the position of the second vehicle when it begins to disappear from the detection results of the target radar is within a preset distance of the stop line of the target driving lane where the second vehicle is located. If it is within the preset distance of the stop line of the target driving lane where the second vehicle is located, it is determined that the second vehicle is stationary. Alternatively, when the second vehicle is the closest vehicle to the stop line of the target driving lane in the target driving lane where the first vehicle is located, it is detected whether the position of the second vehicle when it begins to disappear from the detection results of the target radar has crossed the stop line of the target driving lane where the second vehicle is located. If it has not crossed the stop line of the target driving lane where the second vehicle is located, it is determined that the second vehicle is stationary.

[0076] Optionally, when the second vehicle is not the vehicle closest to the stop line of the target driving lane in the target driving lane where the first vehicle is located (there are still vehicles in front of the second vehicle), it is detected whether the position where the second vehicle begins to disappear from the detection result of the target radar is located in the lateral departure area of ​​the target driving lane, and whether there are stationary vehicles in the position area that extends forward from the position where the second vehicle begins to disappear from the detection result of the target radar and is within a first distance from the position where the second vehicle begins to disappear from the detection result of the target radar, thereby determining whether the second vehicle is approaching a stationary state. For specific implementation, please refer to the judgment process of the first vehicle.

[0077] S330. If the first position is located in the lateral departure area of ​​the target driving lane and there is no stationary second vehicle in the first distance area, then the first driving state information of the first vehicle is determined to be tending to the lateral departure state.

[0078] When the first position falls within the lateral departure area of ​​the target driving lane, and no stationary second vehicle is found within the first distance area, it can be reasonably inferred from these conditions that the first vehicle is likely to tend to leave laterally. This is because the first vehicle is in a lateral departure area and there are no vehicles in front of it that would obstruct its continued progress. According to the characteristics of vehicles queuing in the driving lane, the distance between each vehicle should be relatively close. However, if there is no stationary second vehicle within the first distance area, or if there is no stationary vehicle slightly in front of the first vehicle, this does not conform to the characteristics of vehicles queuing. Therefore, from the perspective of traffic logic and common sense of vehicle driving, the first vehicle is more likely to perform a lateral departure action in this situation. Thus, its first driving state information is determined to be tending to leave laterally.

[0079] S340. If the first position is not located in the lateral departure area of ​​the target driving lane and there is a second vehicle in a stationary state within the first distance area, then the driving state of the first vehicle is tracked by multiple hypotheses to determine whether the first driving state information corresponding to the first vehicle is tending towards a lateral departure state or a stationary state.

[0080] If the first position is not within the lateral departure area, and there is a stationary second vehicle within the first distance area, it is not possible to determine whether the first vehicle is heading towards a stationary state (perhaps because it is stopping and queuing due to a vehicle in front) or towards a lateral departure state (perhaps it is moving laterally around the stationary vehicle in front) based solely on these two conditions. Therefore, a multi-hypothesis tracking technique is adopted, which simultaneously assumes that the vehicle may have both of these driving state trends. Further information is collected (such as possible speed changes during the vehicle's brief disappearance, further changes in the surrounding environment, etc.) or more complex algorithm models are used to gradually verify and determine the most likely driving state information of the first vehicle.

[0081] S350. If the first position is not located in the lateral departure area of ​​the target driving lane and there is no stationary second vehicle in the first distance area, or if the first position is located in the lateral departure area of ​​the target driving lane and there is a stationary second vehicle in the first distance area, then the first driving state information corresponding to the first vehicle is determined to be tending to be stationary.

[0082] The first position is not located in the lateral departure area of ​​the target lane, meaning the vehicle is unlikely to directly perform a lateral departure maneuver. If a second vehicle is stationary within the first distance area, even if the first position is near the lateral departure area, the stationary vehicle will obstruct the vehicle's lateral departure. Considering both scenarios, the first driving state information corresponding to the first vehicle can be determined as tending towards a stationary state, meaning the vehicle will most likely stop nearby or slowly decelerate until it comes to a complete stop.

[0083] For the above solution, by comprehensively analyzing the relationship between the first vehicle's first position and the lateral departure area, as well as the situation of stationary vehicles within the first distance area, the driving trend of the first vehicle after disappearing from the target radar detection result can be determined relatively accurately. This technical solution can effectively determine the driving trend of a vehicle even after it has briefly left radar detection, enabling the traffic flow prediction model to more completely and accurately cover the movement of all vehicles and reduce traffic flow prediction errors caused by vehicle "disappearance" or misjudgment of status.

[0084] As an optional but not limited implementation, the driving state of the first vehicle is tracked using multiple hypotheses, including the following steps B1-B2:

[0085] Step B1: If a third vehicle is detected to be traveling into the second distance area of ​​the first vehicle, the first driving state information corresponding to the first vehicle is determined to be a state of tending to drive laterally away. The second distance area is the position area that extends from the first position of the first vehicle toward the rear of the first vehicle and is within the second distance from the first position.

[0086] Step B2: If a fourth vehicle located behind the first vehicle is determined to be approaching a stationary state and has not yet entered the second distance area of ​​the first vehicle, then the first driving state information corresponding to the first vehicle is determined to be approaching a stationary state.

[0087] See Figure 2 The second distance region is an area defined by extending a second distance behind the first vehicle, starting from its initial position when it begins to disappear from the target radar detection results. For example, if the initial position is a point on the roadway and the second distance is set to 1 meter, then the second distance region is the portion of the roadway extending 1 meter behind the vehicle from that initial position. This second distance region is set based on a traffic scenario logic: when a third vehicle enters this second distance region from behind the first vehicle, it may be affected by the first vehicle's driving state, such as prompting the third vehicle to slow down and maintain a certain distance to avoid a collision. The third vehicle is another vehicle that needs to be considered during the multi-hypothesis tracking of the first vehicle; its trajectory is associated with the first vehicle, and when the third vehicle enters a specific second distance region, it will affect the judgment of the first vehicle's driving state.

[0088] In one alternative example, the target radar continuously detects when a third vehicle may pass through the second distance area of ​​the first vehicle at a certain speed. In this case, the target radar can determine that the target radar has detected the third vehicle entering the second distance area. Based on common traffic behavior patterns and logical inferences, the third vehicle may be traveling at a normal speed and thus be detected by the target radar. It is very likely that the first vehicle is not tending to stop, but rather tending to move laterally away.

[0089] In another alternative example, although the target radar continuously detects, if it detects a third vehicle entering the second distance zone, based on common traffic behavior patterns and logical inferences, the third vehicle may slow down to avoid the first vehicle. At this point, the target radar will detect the third vehicle disappearing from the second distance zone. Similarly, it will determine that the third vehicle has entered the first vehicle's second distance zone, thus confirming that the first vehicle's first driving state is trending towards lateral departure. Predicting whether the first vehicle's first driving state is trending towards lateral departure by determining whether a third vehicle has entered the first vehicle's second distance zone is based on the actual situation of multiple vehicles queuing in the driving lane and influencing each other. This means it fully considers the need for vehicles to maintain a safe distance and avoid collisions during driving, and that when a vehicle approaches from behind, it may trigger an evasive maneuver in front.

[0090] The fourth vehicle is also associated with the first vehicle in a multi-hypothesis tracking scenario. A fourth vehicle located behind the first vehicle is determined to be stationary and not within the first vehicle's second distance zone. When such a fourth vehicle is detected, it indicates that no vehicle is approaching the first vehicle from behind or that no vehicle is causing the first vehicle to make a lateral avoidance maneuver. Combining this with the first vehicle's previous position information (not in the lateral departure zone and the possibility of stationary vehicles ahead), from the perspective of the overall traffic scenario, the first vehicle is more likely to be stationary, such as waiting for vehicles ahead to move or stopping due to traffic congestion. Therefore, the first driving state information corresponding to the first vehicle is determined to be tending towards a stationary state. In an optional example, after predicting the first driving state information of the first vehicle, the driving state information of the fourth vehicle behind the first vehicle is also predicted using the same method. By predicting the driving state information of the fourth vehicle behind the first vehicle, it can be determined whether the fourth vehicle is tending towards a stationary state.

[0091] In complex traffic environments, the interactions between vehicles are multifaceted. By introducing the detection of third and fourth vehicles and analyzing their relative positions to the first vehicle, various factors that may affect the first vehicle's driving state can be considered more comprehensively. Compared to relying solely on the first vehicle's own position information and limited surrounding environmental information, this multi-hypothesis tracking method can significantly improve the accuracy of judging the first vehicle's driving state. Obtaining more accurate first vehicle driving state data through this multi-hypothesis tracking method allows traffic flow analysis models to more realistically reflect actual traffic conditions.

[0092] S360. If the first driving state information indicates that the first vehicle is approaching a stationary state, then the first motion state information of the first vehicle is determined. The first motion state information is used to indicate the first position and first speed of the first vehicle in the target driving lane when it begins to disappear from the detection result of the target radar.

[0093] S370. Based on the first motion state information, predict the second motion state information of the first vehicle to perform trajectory tracking prediction of the first vehicle in the target driving lane. Each second motion state information is used to indicate the second position and second speed of the first vehicle at each deceleration moment during the deceleration process phase. The deceleration process phase is the driving phase in which the first vehicle gradually decelerates from the moment it disappears from the detection results of the target radar until the first vehicle is stationary. Each deceleration moment is the detection time corresponding to each frame generated by the target radar in performing the detection task during the deceleration process phase.

[0094] The technical solution of this invention, by predicting the first driving state information of the first vehicle after it disappears from the target radar detection result, can determine whether the first vehicle tends to stand still or move laterally away after disappearing from the target radar detection result. This helps to grasp the driving trend of the first vehicle in a timely manner when the radar can no longer detect the first vehicle due to deceleration to a low speed. Furthermore, when it is determined that the first vehicle tends to stand still, the first position and first speed of the first vehicle queuing in the target driving lane when it begins to disappear from the target radar detection result can also be determined as the first motion state information of the first vehicle. The first motion state information can be used as a basis to realize the second position and second speed of the first vehicle at each deceleration processing moment during the deceleration processing phase after it disappears from the target radar detection result, thereby realizing the first vehicle's second motion state information. The system predicts the trajectory of a vehicle as it gradually decelerates from disappearing from the target radar's detection until it comes to a standstill. This approach can adapt to various complex dynamic changes when the first vehicle is queuing in the target lane. When a vehicle can no longer be detected by the target radar due to low speed, whether it is a temporary stop caused by a malfunction of the vehicle ahead or a vehicle intending to laterally leave the lane, the system can update the vehicle's trajectory information in a timely manner through prediction and analysis of its driving and motion status. To a certain extent, this compensates for the insufficient detection effect of a single target radar on the first vehicle when it is slow and stationary in the target lane, improving the accuracy and reliability of detecting the first vehicle in low-speed and stationary states in the target lane. This provides strong technical support and data assurance for many fields such as traffic flow monitoring, intelligent driving assistance, and road safety management.

[0095] Figure 4 This is a flowchart illustrating another vehicle trajectory prediction method provided in an embodiment of the present invention. The technical solution of this embodiment further optimizes the process of the technical solution provided in the foregoing embodiments based on the technical solutions of the above embodiments. This embodiment can be combined with various optional solutions in one or more of the above embodiments.

[0096] like Figure 4 As shown, the vehicle trajectory prediction method of this invention may include the following process:

[0097] S410. When the first vehicle is no longer present in the detection results of the target radar, the first driving state information of the first vehicle is predicted. The first vehicle is a vehicle that is queuing in the target driving lane and is within the detection range of the target radar. The first driving state information is used to indicate whether the first vehicle tends to be stationary or tends to be laterally driven away after disappearing from the detection results of the target radar. The lateral driving away state is the state in which the vehicle moves away from the driving lane to one side of the driving lane.

[0098] S420. If the first driving state information indicates that the first vehicle is approaching a stationary state, then the first motion state information of the first vehicle is determined. The first motion state information is used to indicate the first position and first speed of the first vehicle in the target driving lane when it begins to disappear from the detection result of the target radar.

[0099] S430. Based on the first motion state information, predict the second motion state information of the first vehicle to perform trajectory tracking prediction of the first vehicle in the target driving lane. Each second motion state information is used to indicate the second position and second speed of the first vehicle at each deceleration moment during the deceleration process phase. The deceleration process phase is the driving phase in which the first vehicle gradually decelerates from the moment it disappears from the detection results of the target radar until the first vehicle is stationary. Each deceleration moment is the detection time corresponding to each frame generated by the target radar in performing the detection task during the deceleration process phase.

[0100] S440. After the target radar detects that a vehicle has started moving ahead of the first vehicle for a preset time, the third motion state information of the first vehicle is predicted based on the third position corresponding to the first vehicle and according to a preset acceleration, so as to perform trajectory tracking prediction of the first vehicle in the target driving lane.

[0101] The third position is the predicted position of the first vehicle when it is stationary after disappearing from the detection results of the target radar. Each third motion state information is used to indicate the fourth position and fourth speed of the first vehicle at each startup processing moment during the startup processing phase. The startup processing phase is the driving phase from when the first vehicle starts to accelerate from a stationary state until the first vehicle is detected again by the target radar. Each startup processing moment is the detection time corresponding to each frame generated by the target radar in performing the detection task during the startup processing phase.

[0102] By utilizing the characteristics of electromagnetic wave transmission and reception by the target radar, the system detects and senses the first vehicle and its surrounding environment to obtain relevant information, such as the vehicle's position, speed, and relative positional relationship with other vehicles. The presence of a vehicle ahead of the first vehicle for a preset duration indicates that another vehicle ahead of the first vehicle has begun to move within the target radar's detection range, and the movement has lasted for the preset duration. This information reflects a change in the traffic scenario in which the first vehicle is located. The start of the vehicle ahead may affect the subsequent behavior of the first vehicle; for example, the first vehicle may correspondingly start, accelerate, or adjust its trajectory to maintain a safe distance or follow traffic rules. The third position is the predicted location of the first vehicle after it has disappeared from the target radar's detection results and is stationary.

[0103] The preset acceleration is a pre-defined value used to describe the possible acceleration of the first vehicle in a specific scenario (i.e., after the vehicle in front has started for a preset period). This acceleration value is likely determined based on a large amount of traffic data statistical analysis, vehicle dynamics models, and factors such as actual traffic rules and driving habits. For example, in typical urban road driving, a vehicle's acceleration may be limited by traffic flow, road speed limits, vehicle performance, and other factors. The preset acceleration is a relatively reasonable value determined after comprehensively considering these factors to simulate the acceleration behavior of the first vehicle in the current scenario.

[0104] This information is obtained by combining a third position and a preset acceleration to predict the motion state of the first vehicle in a new traffic scenario (the vehicle ahead starts moving). Specifically, the third motion state information may include the position and velocity of the first vehicle at various subsequent time points. This information can be calculated and predicted using kinematic formulas (such as displacement formulas and velocity formulas) to depict the predicted trajectory of the first vehicle on the target lane.

[0105] As an optional but not limited implementation, at least one third motion state information of the first vehicle is determined based on the third position corresponding to the first vehicle according to a preset acceleration prediction, including the following steps C1-C2:

[0106] Step C1: If the third motion state information of the first vehicle at the first startup processing moment in the startup processing phase indicates that the fourth speed of the first vehicle is greater than the speed of the vehicle in front of the first vehicle, then the speed of the first vehicle at the second startup processing moment is adjusted to the speed of the vehicle in front of the first vehicle; and, the uniform acceleration time and uniform speed time of the first vehicle between the first startup processing moment and the second startup processing moment are determined, and the position of the first vehicle at the second startup processing moment is determined based on the uniform acceleration time and uniform speed time between the first startup processing moment and the second startup processing moment, wherein the second startup processing moment is the next startup processing moment adjacent to the first startup processing moment.

[0107] Step C2: If the third motion state information of the first vehicle at the first start-up processing time indicates that the fourth speed of the first vehicle is equal to the speed of the vehicle in front of the first vehicle, then the speed of the first vehicle at the second start-up processing time is adjusted to the speed of the vehicle in front of the first vehicle; and the position of the first vehicle at the second start-up processing time is determined based on the speed between the first start-up processing time and the second start-up processing time.

[0108] The prediction of vehicles queuing in the driving lane is based on the following principles: 1) The following vehicle starts after a certain time after the preceding vehicle starts, and the prediction is performed by uniform acceleration after starting; 2) The speed of the following vehicle will not exceed that of the preceding vehicle, so the vehicle moves at a uniform speed when the speeds are the same; 3) Adjacent vehicles traveling in the same direction start together. Therefore, in order to prevent the prediction mechanism of queuing vehicles from being falsely triggered, it is necessary to ensure that the vehicle starts moving when all adjacent driving lanes traveling in the same direction detect starting vehicles. Otherwise, no vehicle trajectory prediction is performed; 4) If there is a left-turn waiting area, when a vehicle going straight starts, the vehicles in the left-turn lane must move to the position of the waiting area in turn, that is, first accelerate and then decelerate to stop; 5) If a vehicle is bound to a newly detected vehicle during the prediction process, in order to ensure the overall presentation effect, it is still presented according to the predicted position and speed. However, if the newly detected vehicle disappears and is determined to be stationary, the vehicle is predicted according to the model retained by the stationary vehicle, and the subsequent vehicles stop in turn.

[0109] If the third motion state information of the first vehicle at the first start-up processing moment in the start-up processing phase indicates that the fourth speed of the first vehicle is greater than the speed of the vehicle in front of the first vehicle, that is to say, when predicting the motion of the vehicle, there is such a situation: when it is found after prediction that the speed of the first vehicle will exceed the speed of the vehicle in front of the first vehicle, it can be considered that the motion process of the first vehicle is not a single motion mode, but consists of two parts: uniform acceleration motion and uniform speed motion.

[0110] In the uniform acceleration motion, let vfront represent the speed of the vehicle ahead of the first vehicle, vi-1 represent the speed of the first vehicle at the first start-up moment, and a represent the acceleration of the first vehicle during uniform acceleration. Then, according to the formula for the change of velocity in uniformly accelerated linear motion (final velocity = initial velocity + acceleration × time), the duration Δt1 of the uniform acceleration phase can be derived, and its calculation formula is Δt1 = (vfront - vi-1) / a. This formula means that the time required for the first vehicle to reach the speed vfront of the vehicle ahead from its speed vi-1 at the first start-up moment through uniform acceleration is determined by the ratio of the speed difference vfront - vi-1 to the acceleration a. During the uniform motion, the total time for the first vehicle to move from the first start-up moment to the second start-up moment is denoted as Δt. Since the duration of the uniform acceleration phase is known, the duration of the uniform motion phase can be obtained by subtracting the uniform acceleration time from the total time, i.e., Δt2 = Δt - Δt1. This means that after completing the uniform acceleration motion and reaching the speed of the vehicle in front, the target will continue to move at this speed for a period of time, which is Δt2.

[0111] The displacement during the uniform acceleration phase is calculated as follows: For the uniform acceleration phase, the displacement Δs1 can be calculated using the displacement formula for uniformly accelerated linear motion. The formula is Δs1 = aΔt1² / 2. This utilizes the relationship between displacement, acceleration, and motion time to determine the distance traveled by the target during the uniform acceleration phase. The displacement during the uniform velocity phase is calculated as follows: During the uniform velocity phase, since the velocity remains constant, the displacement Δs2 is equal to vfront multiplied by the uniform velocity time Δt2, i.e., Δs2 = vfrontΔt2.

[0112] The position of the first vehicle at the second start-up moment is obtained by adding the displacement during the uniform acceleration phase and the displacement during the uniform speed phase to the position at the first start-up moment. This can be expressed by the formula si = s(i-1) + Δs1 + Δs2. By gradually accumulating displacements in this way, the positional changes of the first vehicle at different start-up moments can be accurately tracked. After the preceding motion process, at the end of the second start-up moment, the final speed of the first vehicle is equal to the speed of the vehicle in front of it. This is because the target first accelerates uniformly to reach the speed of the vehicle in front, and then maintains this speed for a period of time at a uniform speed, so the final speed is the speed of the vehicle in front.

[0113] Optionally, when predicting the target in the left-turn waiting area, if there is no vehicle in front of the first vehicle (i.e., no vehicle in front affects the speed of the first vehicle), then the speed of the vehicle in front involved in the above calculation process cannot exceed the maximum speed that the target has experienced in its historical movement. This is to take into account the limitations of the first vehicle's own movement capabilities; even without obstruction from a vehicle in front, it cannot accelerate indefinitely, and its speed will be constrained by the maximum speed reflected in its past movement performance, thus allowing for a more reasonable and accurate prediction of the first vehicle's position and speed.

[0114] Optionally, when the first vehicle starts moving and then approaches a stop again, it will detect whether the speed of the first vehicle is greater than the speed of the vehicle in front of it. When the speed of the first vehicle is greater than the speed of the vehicle in front of it, the first vehicle prepares to decelerate. It is required that when the speed of the first vehicle is the same as that of the vehicle in front of it, the distance between the two vehicles is 1 meter. Assuming that the braking acceleration of the first vehicle is a0 = -2m / s2, the distance to move when decelerating to the same speed as the vehicle in front of it can be calculated based on the speed difference between the current speed of the first vehicle and the speed of the vehicle in front of it. This distance is recorded as the safety distance, Δs0 = (vi - vfront)2 / (-2a). If the safe distance is greater than the distance between the two vehicles, the first vehicle continues to move at a constant speed, vi = v(i-1), si = s(i-1) + viΔt; if the safe distance is less than or equal to the distance between the two vehicles, the first vehicle decelerates uniformly. When the speeds of the two vehicles are the same, the distance is 1 meter. Calculate the required braking acceleration at this time a = (v(i-1) - v_front)2 / (-2(s – s_front)), and the current speed is vi = v(i-1) + aΔt, si = s(i-1) + aΔt2 / 2.

[0115] Based on the above embodiments, optionally, after performing trajectory tracking prediction on the first vehicle in the target driving lane, the following steps D1-D5 are also included:

[0116] Step D1: Determine the predicted trajectory position of the first vehicle in the target driving lane for trajectory tracking prediction.

[0117] Step D2: Determine the point cloud data associated with the predicted trajectory position from the target radar detection pre-clustering point cloud data. The point cloud data associated with the predicted trajectory position is the pre-clustering point cloud data within the first clustering radius with the predicted trajectory position as the midpoint.

[0118] Step D3: Determine the cluster to which the point cloud data associated with the predicted trajectory location belongs after clustering.

[0119] Step D4: If the cluster to which the point cloud data associated with the predicted trajectory location belongs is formed by clustering point cloud data before clustering that is larger than the first clustering radius, then it is determined that the predicted trajectory location corresponds to a real vehicle target.

[0120] Step D5: If the cluster to which the point cloud data associated with the predicted trajectory position belongs is formed by clustering the point cloud data associated with the predicted trajectory position, then detect whether there is a real vehicle target at the predicted trajectory position that cannot be detected by the target radar due to constant false alarm rate (CFAR) processing.

[0121] The predicted trajectory location is the estimated spatial coordinates or range of the first vehicle's possible location at a future moment, obtained through the aforementioned trajectory tracking and prediction methods. The pre-clustering point cloud data is a collection of numerous discrete points acquired by radar in three-dimensional space. Each point contains information such as spatial coordinates and reflection intensity. "Pre-clustering" indicates that this point cloud data has not yet undergone clustering algorithm processing; it is in its raw, randomly distributed state and requires further analysis and integration. The first cluster radius is a pre-defined value with a specific length unit (e.g., meters) used to define a spatial range. Within this first cluster radius, using the predicted trajectory location as the midpoint, pre-clustering point cloud data related to it are filtered out. The aim is to focus on point cloud data with a strong correlation to the predicted trajectory location, facilitating subsequent analysis to determine the presence of a real vehicle.

[0122] After clustering, we examine which cluster the previously filtered point cloud data associated with the predicted trajectory location was ultimately assigned to. This information is used to determine the presence of a real vehicle based on the clustering patterns. When the cluster to which the point cloud data associated with the predicted trajectory location belongs is formed by clustering point cloud data within a radius larger than the first clustering radius, it means the source of the point cloud data forming that cluster is not limited to the first clustering radius centered on the predicted trajectory location, but also includes point cloud data from a wider area. When it is detected that the cluster to which the point cloud data associated with the predicted trajectory location belongs is formed by such a large-scale clustering of point cloud data, logically, it is highly likely that a real vehicle is present. This is because a vehicle, being an object with a certain volume and spatial extent, will reflect radar waves over a wide area, forming multiple point cloud data points that collectively constitute a cluster representing it. Based on the above clustering patterns, a judgment is made that a real vehicle exists at the predicted trajectory location.

[0123] Constant False Alarm Rate (CFAR) processing is a commonly used signal processing technique in fields such as radar signal detection. It aims to maximize target detection capability while maintaining a certain false alarm probability. However, due to the characteristics of this processing method and the complexity of the actual environment, situations may arise where real vehicle targets exist but are not detected by the target radar. Clustering based solely on point cloud data associated with predicted trajectory positions indicates that the point cloud data forming clusters are primarily within the first cluster radius centered on the predicted trajectory position, resulting in a relatively narrow range. This may mean that real vehicle targets were not detected by the radar due to some reason (such as the influence of CFAR processing), thus requiring further detection to determine if any such missed real vehicles exist.

[0124] By following the steps outlined above, starting from the predicted trajectory location and analyzing the clustering of point cloud data, it is possible to determine with relatively high accuracy whether a vehicle target actually exists at the predicted trajectory location. Compared to relying solely on direct radar detection, this method considers more data details and potential complexities (such as the impact of CFAR processing), effectively reducing misjudgments caused by various interference factors and improving the accuracy of vehicle target detection. Whether in traffic flow monitoring, vehicle trajectory analysis, or autonomous driving scenarios, it can provide more reliable foundational data for subsequent decision-making.

[0125] Detecting whether the first vehicle's predicted trajectory position corresponds to a real vehicle target that cannot be detected by the target radar due to constant false alarm rate (CFAR) processing includes the following steps E1-E5:

[0126] Step E1: Obtain the background noise value calculated by the first CFAR.

[0127] Step E2: Select the original CFAR data within a certain range of the target to be predicted. This range should exceed the cluster radius, but if it is too large, it will increase the amount of computation. Therefore, this range can be set to 3 cluster radii.

[0128] Step E3: Find the high signal-to-noise ratio points found in the data selected in Step E2 according to the initial CFAR, and remove these points. This will give you the information after removing the high signal-to-noise ratio targets.

[0129] Step E4: Re-perform CFAR on the data from Step E3. Here, it is necessary to reselect the reference cell and calculate the background noise. Since high signal-to-noise ratio targets have been removed at this time, a new threshold factor is obtained using the data from Step E3. However, even if there is a real target, the target energy and background noise may not be significantly different. It is not possible to find the target using only a threshold factor. Therefore, starting from this newly obtained threshold factor, the threshold factor is gradually reduced (by 10% each time) until a new high signal-to-noise ratio point is found within the cluster radius of the predicted target location.

[0130] Step E5: Verify the authenticity of the points obtained in Step E4. Assuming the noise follows a Gaussian distribution, calculate the mean and standard deviation of the energy of all points selected in Step E3. If the energy of the point obtained in Step E4 is greater than three times the standard deviation of the mean, then the point obtained in Step E4 is considered a real target (according to the theory of Gaussian distribution, the authenticity is approximately 99.73%), and the predicted target is reliable. Otherwise, it is considered a false target.

[0131] If the target still cannot be detected, it means there is no target here, or the target is too close to the background noise. In this case, the target will not be predicted in this frame, and the prediction and detection will be repeated in the next frame.

[0132] The technical solution of this invention, by predicting the first driving state information of the first vehicle after it disappears from the target radar detection result, can determine whether the first vehicle tends to stand still or move laterally away after disappearing from the target radar detection result. This helps to grasp the driving trend of the first vehicle in a timely manner when the radar can no longer detect the first vehicle due to deceleration to a low speed. Furthermore, when it is determined that the first vehicle tends to stand still, the first position and first speed of the first vehicle queuing in the target driving lane when it begins to disappear from the target radar detection result can also be determined as the first motion state information of the first vehicle. The first motion state information can be used as a basis to realize the second position and second speed of the first vehicle at each deceleration processing moment during the deceleration processing phase after it disappears from the target radar detection result, thereby realizing the first vehicle's second motion state information. The system predicts the trajectory of a vehicle as it gradually decelerates from disappearing from the target radar's detection until it comes to a standstill. This approach can adapt to various complex dynamic changes when the first vehicle is queuing in the target lane. When a vehicle can no longer be detected by the target radar due to low speed, whether it is a temporary stop caused by a malfunction of the vehicle ahead or a vehicle intending to laterally leave the lane, the system can update the vehicle's trajectory information in a timely manner through prediction and analysis of its driving and motion status. To a certain extent, this compensates for the insufficient detection effect of a single target radar on the first vehicle when it is slow and stationary in the target lane, improving the accuracy and reliability of detecting the first vehicle in low-speed and stationary states in the target lane. This provides strong technical support and data assurance for many fields such as traffic flow monitoring, intelligent driving assistance, and road safety management.

[0133] Figure 5 This is a schematic diagram of a vehicle trajectory prediction device provided in an embodiment of the present invention. The present invention is applicable to situations where vehicles queuing in a driving lane can no longer be detected by radar due to deceleration to a lower speed. The vehicle trajectory prediction device can be implemented in the form of software and / or hardware, and is generally integrated into any electronic device with network communication function, such as a mobile terminal, PC, or server.

[0134] like Figure 5 As shown, the vehicle trajectory prediction device of this invention embodiment may include the following:

[0135] The first prediction module 510 is used to predict the first driving state information of the first vehicle when the first vehicle is no longer present in the detection result of the target radar. The first vehicle is a vehicle queuing in the target driving lane and the first vehicle is within the detection range of the target radar. The first driving state information is used to indicate whether the first vehicle tends to be stationary or tends to be laterally driven away after disappearing from the detection result of the target radar. The lateral driving away state is the state in which the vehicle moves away from the driving lane to one side of the driving lane.

[0136] The determining module 520 is used to determine the first motion state information of the first vehicle if the first driving state information indicates that the first vehicle is tending to be stationary. The first motion state information is used to indicate the first position and first speed of the first vehicle in the target driving lane when it begins to disappear from the detection result of the target radar.

[0137] The second prediction module 530 is used to predict the second motion state information of the first vehicle based on the first motion state information, and to perform trajectory tracking prediction of the first vehicle in the target driving lane. Each second motion state information is used to indicate the second position and second speed of the first vehicle at each deceleration processing moment during the deceleration processing phase. The deceleration processing phase is the driving phase in which the first vehicle gradually decelerates from the moment it disappears from the detection result of the target radar until the first vehicle is stationary. Each deceleration processing moment is the detection time corresponding to each frame generated by the target radar in performing the detection task during the deceleration processing phase.

[0138] Based on the above embodiments, optionally, the prediction of the first driving state information of the first vehicle includes:

[0139] The system detects whether the first position of the first vehicle is located in the lateral departure area of ​​the target driving lane. The first position is the position where the first vehicle begins to disappear from the detection result of the target radar. The lateral departure area is the area on the driving lane that provides the vehicle with the position required to move to one side of the driving lane and leave the driving lane.

[0140] Detect whether there is a stationary second vehicle within a first distance area of ​​the first vehicle. The first distance area is a position area that extends from the first position of the first vehicle toward the front of the first vehicle and is within a first distance from the first position.

[0141] If the first position is located in the lateral departure area of ​​the target driving lane and there is no second vehicle in a stationary state within the first distance area, then the first driving state information of the first vehicle is determined to be tending to the lateral departure state.

[0142] If the first position is not located in the lateral departure area of ​​the target driving lane and there is a second vehicle in a stationary state within the first distance area, then the driving state of the first vehicle is tracked by multiple hypotheses to determine whether the first driving state information corresponding to the first vehicle is tending towards a lateral departure state or a stationary state.

[0143] If the first position is not located in the lateral departure area of ​​the target driving lane and there is no stationary second vehicle within the first distance area, or if the first position is located in the lateral departure area of ​​the target driving lane and there is a stationary second vehicle within the first distance area, then the first driving state information corresponding to the first vehicle is determined to be tending towards a stationary state.

[0144] Based on the above embodiments, optionally, multiple hypothesis tracking is performed on the driving state of the first vehicle, including:

[0145] If a third vehicle is detected to be driving into the second distance area of ​​the first vehicle, then the first driving state information corresponding to the first vehicle is determined to be a state of tending to drive laterally away. The second distance area is the position area that extends from the first position of the first vehicle toward the rear of the first vehicle and is within the second distance from the first position.

[0146] If a fourth vehicle located behind the first vehicle is detected and determined to be approaching a stationary state and has not entered the second distance area of ​​the first vehicle, then the first driving state information corresponding to the first vehicle is determined to be approaching a stationary state.

[0147] Based on the above embodiments, optionally, based on the first motion state information, predicting the second motion state information of the first vehicle includes:

[0148] Determine the third position corresponding to the first vehicle, wherein the third position is the predicted position of the first vehicle when it is stationary after disappearing from the detection result of the target radar;

[0149] Based on the third position corresponding to the first vehicle and the first position and first speed indicated by the first motion state information, the second motion state information of the first vehicle is predicted and determined.

[0150] Optionally, based on the above embodiments, the device further includes:

[0151] If a vehicle is detected by the target radar within the third distance area corresponding to the first vehicle, the vehicle detected by the target radar within the third distance area will be bound to the first vehicle, which is predicted to be located at the third position, as the same vehicle. The third distance area extends from the third position of the first vehicle toward the front of the first vehicle and is within the third distance from the third position. The third position is the predicted position of the first vehicle when it is stationary after disappearing from the detection result of the target radar.

[0152] Based on the above embodiments, optionally, after predicting the second motion state information of the first vehicle based on the first motion state information, the method includes:

[0153] After the target radar detects that a vehicle has started moving ahead of the first vehicle for a preset time, the third motion state information of the first vehicle is predicted based on the third position corresponding to the first vehicle and according to a preset acceleration. This prediction is used to track and predict the trajectory of the first vehicle in the target driving lane. The third position is the predicted position of the first vehicle when it is stationary after disappearing from the detection result of the target radar. Each piece of third motion state information is used to indicate the fourth position and fourth speed of the first vehicle at each start processing moment during the start processing phase. The start processing phase is the driving phase from when the first vehicle starts to accelerate from a stationary state until the first vehicle is detected again by the target radar. Each start processing moment is the detection time corresponding to each frame generated by the target radar during the start processing phase.

[0154] Based on the above embodiments, optionally, at least one third motion state information of the first vehicle is determined according to a preset acceleration prediction based on the third position corresponding to the first vehicle, including:

[0155] If the third motion state information of the first vehicle at the first startup processing moment in the startup processing phase indicates that the fourth speed of the first vehicle is greater than the speed of the vehicle in front of the first vehicle, then the speed of the first vehicle at the second startup processing moment is adjusted to the speed of the vehicle in front of the first vehicle; and, the uniform acceleration time and uniform speed time of the first vehicle between the first startup processing moment and the second startup processing moment are determined, and the position of the first vehicle at the second startup processing moment is determined based on the uniform acceleration time and uniform speed time between the first startup processing moment and the second startup processing moment, wherein the second startup processing moment is the next startup processing moment adjacent to the first startup processing moment;

[0156] If the third motion state information of the first vehicle at the first start-up processing time indicates that the fourth speed of the first vehicle is equal to the speed of the vehicle in front of the first vehicle, then the speed of the first vehicle at the second start-up processing time is adjusted to the speed of the vehicle in front of the first vehicle; and the position of the first vehicle at the second start-up processing time is determined based on the speed between the first start-up processing time and the second start-up processing time.

[0157] Based on the above embodiments, optionally, after performing trajectory tracking prediction on the first vehicle in the target driving lane, the method further includes:

[0158] Determine the predicted trajectory position for the first vehicle in the target driving lane;

[0159] The point cloud data associated with the predicted trajectory position is determined from the point cloud data before clustering detected by the target radar. The point cloud data associated with the predicted trajectory position is the point cloud data before clustering within the first clustering radius with the predicted trajectory position as the midpoint.

[0160] Determine the cluster to which the point cloud data associated with the predicted trajectory location belongs after clustering;

[0161] If the cluster to which the point cloud data associated with the predicted trajectory location belongs is formed by clustering point cloud data before clustering that is within a radius greater than the first clustering radius, then it is determined that the predicted trajectory location corresponds to a real vehicle target.

[0162] If it is detected that the cluster to which the point cloud data associated with the predicted trajectory position belongs is formed by clustering only the point cloud data associated with the predicted trajectory position, then it is detected whether the first vehicle has a real vehicle target at the predicted trajectory position that cannot be detected by the target radar due to constant false alarm rate (CFAR) processing.

[0163] The technical solution of this invention, by predicting the first driving state information of the first vehicle after it disappears from the target radar detection result, can determine whether the first vehicle tends to stand still or move laterally away after disappearing from the target radar detection result. This helps to grasp the driving trend of the first vehicle in a timely manner when the radar can no longer detect the first vehicle due to deceleration to a low speed. Furthermore, when it is determined that the first vehicle tends to stand still, the first position and first speed of the first vehicle queuing in the target driving lane when it begins to disappear from the target radar detection result can also be determined as the first motion state information of the first vehicle. The first motion state information can be used as a basis to realize the second position and second speed of the first vehicle at each deceleration processing moment during the deceleration processing phase after it disappears from the target radar detection result, thereby realizing the first vehicle's second motion state information. The system predicts the trajectory of a vehicle as it gradually decelerates from disappearing from the target radar's detection until it comes to a standstill. This approach can adapt to various complex dynamic changes when the first vehicle is queuing in the target lane. When a vehicle can no longer be detected by the target radar due to low speed, whether it is a temporary stop caused by a malfunction of the vehicle ahead or a vehicle intending to laterally leave the lane, the system can update the vehicle's trajectory information in a timely manner through prediction and analysis of its driving and motion status. To a certain extent, this compensates for the insufficient detection effect of a single target radar on the first vehicle when it is slow and stationary in the target lane, improving the accuracy and reliability of detecting the first vehicle in low-speed and stationary states in the target lane. This provides strong technical support and data assurance for many fields such as traffic flow monitoring, intelligent driving assistance, and road safety management.

[0164] The vehicle trajectory prediction device provided in this embodiment of the invention can execute the vehicle trajectory prediction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the vehicle trajectory prediction method.

[0165] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of the present invention.

[0166] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Refer to the following... Figure 6 It illustrates an electronic device suitable for implementing embodiments of the present invention (e.g., Figure 6The diagram below shows the structure of the terminal device (or server) 600. The terminal device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0167] like Figure 6 As shown, electronic device 600 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of electronic device 600. The processing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. An edit / output (I / O) interface 605 is also connected to bus 604.

[0168] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 An electronic device 600 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0169] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the vehicle trajectory prediction method shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by a processing device 601, it performs the functions defined in the vehicle trajectory prediction method of the embodiments of the present invention.

[0170] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0171] The electronic device provided in this embodiment of the invention and the vehicle trajectory prediction method provided in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0172] This invention provides a computer storage medium storing a computer program that, when executed by a processor, implements the vehicle trajectory prediction method provided in the above embodiments.

[0173] It should be noted that the computer-readable medium described above in this invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0174] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0175] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0176] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to:

[0177] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: predict the first driving state information of the first vehicle when the first vehicle is no longer detected by the target radar, wherein the first vehicle is a vehicle queuing in the target driving lane and is within the detection range of the target radar; the first driving state information is used to indicate whether the first vehicle tends to be stationary or tends to be laterally leaving the driving lane after disappearing from the detection result of the target radar, wherein the lateral leaving the driving lane is a state in which the vehicle moves away from the driving lane to one side of the driving lane; if the first driving state information indicates that the first vehicle tends to be stationary, then determine the first motion state information of the first vehicle. The first motion state information is used to indicate the first position and first speed of the first vehicle queuing in the target driving lane when it begins to disappear from the detection results of the target radar; based on the first motion state information, the second motion state information of the first vehicle is predicted to predict the trajectory of the first vehicle in the target driving lane. Each piece of the second motion state information is used to indicate the second position and second speed of the first vehicle at each deceleration moment during the deceleration process phase. The deceleration process phase is the driving phase in which the first vehicle gradually decelerates from the moment it begins to disappear from the detection results of the target radar until the first vehicle is stationary. Each deceleration moment is the detection time corresponding to each frame generated by the target radar during the deceleration process phase.

[0178] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0179] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0180] The units described in the embodiments of the present invention can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0181] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0182] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0183] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0184] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0185] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A vehicle trajectory prediction method, characterized in that, The method includes: When the first vehicle is no longer detected by the target radar, the first driving state information of the first vehicle is predicted. The first vehicle is a vehicle queuing in the target driving lane and is within the detection range of the target radar. The first driving state information is used to indicate whether the first vehicle tends to be stationary or tends to move laterally away after disappearing from the detection results of the target radar. The lateral departure state is the state in which the vehicle moves away from the driving lane to one side of the driving lane. If the first driving state information indicates that the first vehicle is approaching a stationary state, then the first motion state information of the first vehicle is determined. The first motion state information is used to indicate the first position and first speed of the first vehicle in the target driving lane when it begins to disappear from the detection result of the target radar. Based on the first motion state information, the second motion state information of the first vehicle is predicted to predict the trajectory of the first vehicle in the target driving lane. Each second motion state information is used to indicate the second position and second speed of the first vehicle at each deceleration moment during the deceleration process phase. The deceleration process phase is the driving phase in which the first vehicle gradually decelerates from the moment it disappears from the detection result of the target radar until the first vehicle is stationary. Each deceleration moment is the detection time corresponding to each frame generated by the target radar in performing the detection task during the deceleration process phase.

2. The method according to claim 1, characterized in that, Predicting the first driving state information of the first vehicle includes: The system detects whether the first position of the first vehicle is located in the lateral departure area of ​​the target driving lane. The first position is the position where the first vehicle begins to disappear from the detection result of the target radar. The lateral departure area is the area on the driving lane that provides the vehicle with the position required to move to one side of the driving lane and leave the driving lane. Detect whether there is a stationary second vehicle within a first distance area of ​​the first vehicle. The first distance area is a position area that extends from the first position of the first vehicle toward the front of the first vehicle and is within a first distance from the first position. If the first position is located in the lateral departure area of ​​the target driving lane and there is no second vehicle in a stationary state within the first distance area, then the first driving state information of the first vehicle is determined to be tending to the lateral departure state. If the first position is not located in the lateral departure area of ​​the target driving lane and there is a second vehicle in a stationary state within the first distance area, then the driving state of the first vehicle is tracked by multiple hypotheses to determine whether the first driving state information corresponding to the first vehicle is tending towards a lateral departure state or a stationary state. If the first position is not located in the lateral departure area of ​​the target driving lane and there is no stationary second vehicle within the first distance area, or if the first position is located in the lateral departure area of ​​the target driving lane and there is a stationary second vehicle within the first distance area, then the first driving state information corresponding to the first vehicle is determined to be tending towards a stationary state.

3. The method according to claim 2, characterized in that, The driving state of the first vehicle is tracked using multiple hypotheses, including: If a third vehicle is detected to be driving into the second distance area of ​​the first vehicle, then the first driving state information corresponding to the first vehicle is determined to be a state of tending to drive laterally away. The second distance area is the position area that extends from the first position of the first vehicle toward the rear of the first vehicle and is within the second distance from the first position. If a fourth vehicle located behind the first vehicle is detected and determined to be approaching a stationary state and has not entered the second distance area of ​​the first vehicle, then the first driving state information corresponding to the first vehicle is determined to be approaching a stationary state.

4. The method according to claim 1, characterized in that, Based on the first motion state information, the second motion state information of the first vehicle is predicted, including: Determine the third position corresponding to the first vehicle, wherein the third position is the predicted position of the first vehicle when it is stationary after disappearing from the detection result of the target radar; Based on the third position corresponding to the first vehicle and the first position and first speed indicated by the first motion state information, the second motion state information of the first vehicle is predicted and determined.

5. The method according to claim 1, characterized in that, After predicting the second motion state information of the first vehicle based on the first motion state information, the process includes: After the target radar detects that a vehicle has started moving ahead of the first vehicle for a preset time, the third motion state information of the first vehicle is predicted based on the third position corresponding to the first vehicle and according to a preset acceleration. This prediction is used to track and predict the trajectory of the first vehicle in the target driving lane. The third position is the predicted position of the first vehicle when it is stationary after disappearing from the detection result of the target radar. Each piece of third motion state information is used to indicate the fourth position and fourth speed of the first vehicle at each start processing moment during the start processing phase. The start processing phase is the driving phase from when the first vehicle starts to accelerate from a stationary state until the first vehicle is detected again by the target radar. Each start processing moment is the detection time corresponding to each frame generated by the target radar during the start processing phase.

6. The method according to claim 5, characterized in that, Based on the third position corresponding to the first vehicle, at least one third motion state information of the first vehicle is determined according to a preset acceleration prediction, including: If the third motion state information of the first vehicle at the first startup processing moment in the startup processing phase indicates that the fourth speed of the first vehicle is greater than the speed of the vehicle in front of the first vehicle, then the speed of the first vehicle at the second startup processing moment is adjusted to the speed of the vehicle in front of the first vehicle; and, the uniform acceleration time and uniform speed time of the first vehicle between the first startup processing moment and the second startup processing moment are determined, and the position of the first vehicle at the second startup processing moment is determined based on the uniform acceleration time and uniform speed time between the first startup processing moment and the second startup processing moment, wherein the second startup processing moment is the next startup processing moment adjacent to the first startup processing moment; If the third motion state information of the first vehicle at the first start-up processing time indicates that the fourth speed of the first vehicle is equal to the speed of the vehicle in front of the first vehicle, then the speed of the first vehicle at the second start-up processing time is adjusted to the speed of the vehicle in front of the first vehicle; and the position of the first vehicle at the second start-up processing time is determined based on the speed between the first start-up processing time and the second start-up processing time.

7. The method according to claim 1 or 5, characterized in that, After performing trajectory tracking and prediction on the first vehicle in the target lane, the process also includes: Determine the predicted trajectory position for the first vehicle in the target driving lane; The point cloud data associated with the predicted trajectory position is determined from the point cloud data before clustering detected by the target radar. The point cloud data associated with the predicted trajectory position is the point cloud data before clustering within the first clustering radius with the predicted trajectory position as the midpoint. Determine the cluster to which the point cloud data associated with the predicted trajectory location belongs after clustering; If the cluster to which the point cloud data associated with the predicted trajectory location belongs is formed by clustering point cloud data before clustering that is within a radius greater than the first clustering radius, then it is determined that the predicted trajectory location corresponds to a real vehicle target. If it is detected that the cluster to which the point cloud data associated with the predicted trajectory position belongs is formed by clustering only the point cloud data associated with the predicted trajectory position, then it is detected whether the first vehicle has a real vehicle target at the predicted trajectory position that cannot be detected by the target radar due to constant false alarm rate (CFAR) processing.

8. A vehicle trajectory prediction device, characterized in that, The device includes: The first prediction module is used to predict the first driving state information of the first vehicle when the first vehicle is no longer present in the detection results of the target radar. The first vehicle is a vehicle queuing in the target driving lane and is within the detection range of the target radar. The first driving state information is used to indicate whether the first vehicle tends to be stationary or tends to be laterally driven away after disappearing from the detection results of the target radar. The lateral driving away state is the state in which the vehicle moves away from the driving lane to one side of the driving lane. The determination module is used to determine the first motion state information of the first vehicle if the first driving state information indicates that the first vehicle is tending to be stationary. The first motion state information is used to indicate the first position and first speed of the first vehicle in the target driving lane when it begins to disappear from the detection result of the target radar. The second prediction module is used to predict the second motion state information of the first vehicle based on the first motion state information, and to perform trajectory tracking prediction of the first vehicle in the target driving lane. Each second motion state information is used to indicate the second position and second speed of the first vehicle at each deceleration moment during the deceleration process phase. The deceleration process phase is the driving phase in which the first vehicle gradually decelerates from the moment it disappears from the detection result of the target radar until the first vehicle is stationary. Each deceleration moment is the detection time corresponding to each frame generated by the target radar in performing the detection task during the deceleration process phase.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle trajectory prediction method according to any one of claims 1-7.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the vehicle trajectory prediction method according to any one of claims 1-7.

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