Method, system, equipment and medium for longitudinal motion model calibration of low-distance start-stop vehicles

Abstract

The present invention provides a method, system, device, and medium for calibrating the longitudinal motion model of low-distance start-stop vehicles. The method divides vehicle driving scenarios in road traffic and categorizes the driving scenarios into driving scenarios corresponding to the vehicle approach zone and the vehicle following zone according to the longitudinal operating conditions of the vehicle. The minimum stopping distance of the vehicle is determined based on the analysis of measured data. Based on the minimum stopping distance of the vehicle and the speed optimization model, a scene driving trajectory data set of the approach zone and the following zone is obtained. The longitudinal motion model is calibrated based on the trajectory data. Based on the measured data, the present application uses a speed optimization model to obtain the corresponding vehicle low-distance parking and starting motion trajectory data, and constructs a simulation system based on the relevant parameters of the obtained data calibration model to support the development of a low-distance parking and starting assisted driving system for vehicles. This system can shorten the stopping distance of vehicles and effectively solve the problems of long queues and overflow of vehicles at intersections.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent vehicle assisted driving technology simulation, and specifically relates to a method, system, equipment and medium for calibrating a longitudinal motion model of a low-distance start-stop vehicle. Background Art

[0002] With the rapid development of the economy and the continuous improvement of the level of motorization, traffic problems are becoming increasingly acute, and urban road congestion is getting worse. Improving the traffic efficiency and traffic capacity of urban road networks is an issue that urgently needs attention for the further development of urban roads. The current application of intelligent network technology and vehicle assisted driving technology in transportation has provided new technologies and research directions for solving traffic congestion and achieving efficient vehicle traffic.

[0003] In urban traffic, especially at signalized intersections, there is a phenomenon of large numbers of vehicles queuing. The queues of vehicles will spread to upstream intersections, resulting in a waste of traffic capacity at upstream intersections, causing queue lock problems on road sections, and then paralyzing the entire traffic system. In the existing signalized intersection management and control practices, the first is to achieve spatial optimization of traffic flow through lane channeling, such as setting up variable left-turn lanes, increasing left-turn lanes and the number of lanes at intersections; the second is to achieve temporal optimization of traffic flow through signal timing optimization, such as green wave coordinated control optimization and regional intersection group signal control optimization; the third is to use technologies such as wireless communication and the Internet to implement dynamic real-time information exchange between vehicles and roads, carry out active vehicle control and signal and road collaborative management, to adjust vehicle driving behavior and arrival patterns, and achieve dynamic coordinated optimization of vehicles in time and space.

[0004] Analysis of measured data shows that compared with vehicles with larger parking spacing on urban roads, vehicles with low parking spacing and a wide field of view do not significantly increase the start interval time, affecting the normal traffic behavior of the vehicle. This provides a basis for the research on low-pitch parking and starting assisted driving technology for vehicles. The existing Chinese patent CN202211611172.3 can realize the low-pitch parking and starting control technology of vehicles, but the development of the actual system requires a simulation test platform. At present, no patent has been found that takes into account the motion model and model parameter calibration method during the low-pitch parking and starting process of vehicles. Summary of the Invention

[0005] In response to the problems existing in the prior art, the present invention provides a method, system, equipment and medium for calibrating the longitudinal motion model of low-distance start-stop vehicles. Based on the measured data, the speed optimization model is used to obtain the corresponding vehicle low-distance parking and starting motion trajectory data. Based on the relevant parameters of the data calibration model, a simulation system is constructed to provide support for the development of low-distance parking and starting assisted driving systems for vehicles.

[0006] The present invention is achieved through the following technical solutions:

[0007] The longitudinal motion model calibration method for low-distance start-stop vehicles includes the following steps:

[0008] S1: Divide the vehicle driving scenarios in road traffic and categorize them into driving scenarios corresponding to the vehicle approaching zone and driving scenarios corresponding to the vehicle following zone based on the longitudinal operation status of the vehicle. Determine the minimum stopping distance between vehicles based on the analysis of measured data.

[0009] S2: Based on the vehicle minimum parking distance and speed optimization model, obtain the scenario driving trajectory dataset of the approaching zone and the following zone;

[0010] S3: Calibrate the longitudinal motion model based on trajectory data.

[0011] Furthermore, the driving scenarios corresponding to the vehicle approaching zone include:

[0012] When there is no vehicle ahead or the vehicle ahead has completely stopped, the following vehicle will decelerate within the set initial speed within the deceleration time to the vehicle ahead and stop within the minimum stopping distance.

[0013] The vehicle ahead has stopped. The following vehicle first starts to decelerate within the set initial speed within the deceleration time. Before the vehicle ahead stops completely, it restarts and the following vehicle changes its parking position according to the speed.

[0014] The vehicle following zone corresponds to the following driving scenarios: the vehicle in front does not stop, and the front and rear vehicles first follow each other within a set initial speed. Then the vehicle in front starts to decelerate, and the following vehicle also decelerates, and the two vehicles stop within the minimum stopping distance.

[0015] Furthermore, the step S1 of determining the minimum parking distance of vehicles based on the measured data analysis includes the following steps:

[0016] Collect vehicle stopping distance data from different lanes on actual roads at multiple signalized intersections;

[0017] Conduct statistical analysis on the data characteristics and distribution characteristics of the collected vehicle parking distances to obtain the optimal parking distance and the extreme value of the parking distance endpoints;

[0018] The extreme value of the parking distance endpoint is taken as the minimum parking distance of the vehicle.

[0019] Furthermore, when the vehicle is in the approaching zone, the speed change formula of the following vehicle during the deceleration phase based on the speed optimization model is:

[0020]

[0021] The calculation formula of the target vehicle's total displacement is:

[0022]

[0023] Where, v c is the initial speed when deceleration begins, v c =(20,70)km / h, v0 is the vehicle's final speed, in m / s; u2 is the vehicle's deceleration coefficient; t d is the moment when the vehicle starts to decelerate, t1 is the time used in the cruising stage, and t2 is the time used in the deceleration stage, the unit is s.

[0024] Furthermore, when the vehicle is in the following zone, the speed change formula of the following vehicle during the deceleration phase is:

[0025]

[0026]

[0027] During the deceleration process, the total displacement of the target vehicle is calculated as follows:

[0028]

[0029] ((sin(u4*t4)) / u4+t4)+v0*t 42 (5)

[0030] Where a max is the maximum deceleration of the vehicle, in m / s 2 ;v t-1 is the velocity at the previous moment, is the expected speed in m / s; is the expected spacing, in meters; t e is the moment when the preceding vehicle stops or the speed of the following vehicle fluctuates greatly; i is the vehicle number, i-1 represents the preceding vehicle, and i represents the following vehicle; t e is the time it takes for the preceding vehicle to decelerate and stop, in seconds; jam is the blocking distance; is the expected headway; Δv is the speed difference between the front and rear vehicles; a comf Deceleration for comfort;

[0031] Among them, the initial state of the leading vehicle is set to: v c =(20,70)km / h, the speed change is shown in formula (1); the initial state of the following vehicle is set to: Δv c =(-2.5,2.5)km / h;ΔS0=(7,80)m, where Δv c is the relative initial velocity between the following vehicle and the leading vehicle, and ΔS0 is the relative initial distance between the following vehicle and the leading vehicle.

[0032] Furthermore, the vehicle start process is a process where the vehicle gradually increases its speed from a stopped state to a normal driving speed state. When there is no vehicle ahead, its speed change curve is opposite to that of a vehicle stopping in an approaching zone. When there is a vehicle ahead, its speed change curve is opposite to that of a vehicle stopping in a following zone. The vehicle start process is a secondary start-stop state of the vehicle, and its speed change curve is opposite to that of a vehicle in a following zone at a low speed.

[0033] Based on the set vehicle initial state and actual vehicle operating conditions, the speed curves in the approaching and following zones are optimized and solved. The obtained speed and displacement curves are screened to establish a scenario trajectory dataset for the vehicle in the approaching and following zones.

[0034] Furthermore, calibrating the longitudinal motion model based on the trajectory data includes the following steps:

[0035] The leading vehicle trajectory data is used as input. When the vehicle is approaching, the leading vehicle trajectory data is set to the stop-acceleration state; when the vehicle is following, the leading vehicle trajectory data is set to the cruise-deceleration-stop-acceleration state, and the trailing vehicle trajectory data is output by the calibrated longitudinal motion model.

[0036] Compare the output data with the optimized trajectory data to determine the optimization target;

[0037] Solve the model parameters that minimize the optimization objective and calibrate the obtained model parameters.

[0038] The longitudinal motion model calibration system for low-distance start-stop vehicles includes:

[0039] A pre-processing module is used to divide vehicle driving scenarios in road traffic and classify them into driving scenarios corresponding to the vehicle approaching zone and driving scenarios corresponding to the vehicle following zone based on the longitudinal operation status of the vehicle. The minimum stopping distance between vehicles is determined based on the analysis of measured data.

[0040] A calculation module is used to obtain a driving trajectory dataset for the approaching and following zones based on a vehicle minimum stopping distance and speed optimization model;

[0041] The calibration module is used to calibrate the longitudinal motion model based on trajectory data.

[0042] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, steps of a method for calibrating a longitudinal motion model of a low-distance start-stop vehicle are implemented.

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for calibrating a longitudinal motion model of a low-distance start-stop vehicle.

[0044] Compared with the prior art, the present invention has the following beneficial technical effects:

[0045] The present invention provides a method, system, device, and medium for calibrating a longitudinal motion model for a low-spacing start-stop vehicle. The method divides vehicle driving scenarios in road traffic and categorizes them into driving scenarios corresponding to the approaching zone and the following zone based on the longitudinal operating conditions of the vehicle. The method then determines the minimum stopping distance between vehicles based on measured data analysis. Based on the minimum stopping distance and a speed optimization model, a driving trajectory dataset for the approaching zone and the following zone is obtained. The method then calibrates the longitudinal motion model based on the trajectory data. The method utilizes a speed optimization model based on measured data to obtain corresponding low-spacing stop and start motion trajectory data for the vehicle. The method calibrates the relevant parameters of the model based on the obtained data and constructs a simulation system to support the development of a low-spacing stop and start assisted driving system for the vehicle. The method fills a gap in existing micro-simulation software for low-spacing stop and start simulations. The calibrated motion model better matches the actual low-spacing stop and start process, stabilizes driving speeds, and effectively reduces fuel consumption during the vehicle start-stop process. The method also shortens the stopping distance of vehicles, effectively resolving the issues of excessively long and overflowing vehicle queues at intersections, increasing the space utilization rate of signalized intersections, and significantly improving the road's traffic capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of the method for calibrating the longitudinal motion model of a low-distance start-stop vehicle according to the present invention;

[0047] Figure 2 This is a comparison diagram of the following distance after calibration of the Wiedemann74 model parameters in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described in detail below with reference to specific embodiments, which are intended to explain the present invention rather than to limit it.

[0049] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0051] The present invention provides a method for calibrating the longitudinal motion model of a low-distance start-stop vehicle. Figure 1 As shown, the following steps are included:

[0052] S1: Divide the vehicle driving scenarios in road traffic and categorize them into driving scenarios corresponding to the vehicle approaching zone and driving scenarios corresponding to the vehicle following zone based on the longitudinal operation status of the vehicle. Determine the minimum stopping distance between vehicles based on the analysis of measured data.

[0053] S2: Based on the vehicle minimum parking distance and speed optimization model, obtain the scenario driving trajectory dataset of the approaching zone and the following zone;

[0054] S3: Calibrate the longitudinal motion model based on trajectory data.

[0055] Preferably, in this application, the traffic simulation software uses a physiological-psychological model and a safety distance model to simulate the longitudinal following behavior of the vehicle. Among them, the Vissim software divides the longitudinal following area into more detailed areas: mainly divided into a free driving area, a vehicle approach area, a vehicle following area, and an emergency braking area. Each following area is distinguished by speed difference and displacement difference, and the acceleration and deceleration models used are not exactly the same. Therefore, this application corresponds the above-mentioned different driving scenarios to the following areas in Vissim based on the vehicle driving motion characteristics, facilitating the acquisition of vehicle driving trajectory data. The driving scenarios corresponding to the vehicle approach area include:

[0056] When there is no vehicle ahead or the vehicle ahead has completely stopped, the following vehicle will decelerate within the set initial speed within the deceleration time to the vehicle ahead and stop within the minimum stopping distance.

[0057] The vehicle ahead has stopped. The following vehicle first starts to decelerate within the set initial speed within the deceleration time. Before the vehicle ahead stops completely, it restarts and the following vehicle changes its parking position according to the speed.

[0058] The vehicle following zone corresponds to the following driving scenarios: the vehicle in front does not stop, and the front and rear vehicles first follow each other within a set initial speed. Then the vehicle in front starts to decelerate, and the following vehicle also decelerates, and the two vehicles stop within the minimum stopping distance.

[0059] It should be noted that the vehicle driving scenario also includes the vehicle driving and parking scenario, the vehicle driving and starting scenario, and the driving scenario corresponding to the emergency braking area, among which the vehicle driving and parking scenario includes:

[0060] When there is no vehicle ahead, as the first vehicle to arrive at the signalized intersection, it will decelerate within the deceleration time at the set initial speed and stop at the set stop position in front of the stop line;

[0061] When the vehicle ahead has come to a complete stop, the following vehicle begins to decelerate within the deceleration time at the set initial speed to the set parking position behind the vehicle ahead and stops;

[0062] The vehicle ahead has stopped. The following vehicle first starts to decelerate within the set initial speed within the deceleration time. Before the vehicle ahead stops completely, it restarts and the following vehicle changes its parking position according to the speed.

[0063] The vehicle in front does not stop. The front and rear vehicles first follow each other within the set initial speed. Then, the vehicle in front starts to decelerate, and the following vehicle decelerates accordingly. Both vehicles stop at the set stopping distance.

[0064] Vehicle driving start scenarios include: both the front and rear vehicles accelerate from a standstill, and as the speed increases, the distance between the vehicles gradually increases from the lower stopping distance to reach a normal following state; and both the front and rear vehicles have stopped, and the leading vehicle needs to start again due to external factors and stop and drive forward for a certain distance. The following vehicle determines whether to start and stop again based on the distance traveled by the leading vehicle.

[0065] It should be further explained that the initial speed set in this application is 20-70 km / h; preferably, determining the minimum parking distance of vehicles based on the measured data analysis in step S1 includes the following steps:

[0066] Collect vehicle stopping distance data from different lanes on actual roads at multiple signalized intersections;

[0067] Conduct statistical analysis on the data characteristics and distribution characteristics of the collected vehicle parking distances to obtain the optimal parking distance and the extreme value of the parking distance endpoints;

[0068] The extreme value of the parking distance endpoint is taken as the minimum parking distance of the vehicle.

[0069] Specifically, a statistical analysis was performed on the data characteristics and distribution characteristics of the collected parking spacing data. The analysis results showed that among all vehicle types, the parking spacing of vehicles was widely distributed, with more data distributed in the range of 0.5-2 meters. At the same time, some data were distributed below 0.5 meters and reached 0.1 meters. Therefore, in order to obtain vehicle trajectory data as completely as possible in the future, the parking spacing range was set to (0.1, 2.0) meters. However, based on actual vehicle driving conditions and to ensure safety, the optimal parking spacing was used as the preferred actual parking spacing. Those skilled in the art can adjust the parking spacing range to the range of (0.2, 2.0) meters or (0.5, 2.0) meters according to actual needs.

[0070] Preferably, when the vehicle is in the approaching zone, the speed change formula of the following vehicle during the deceleration phase based on the speed optimization model is:

[0071]

[0072] The calculation formula of the target vehicle's total displacement is:

[0073]

[0074] Where, c is the initial speed when deceleration begins, v c =(20,70)km / h, 0 is the vehicle's final speed, in m / s; u2 is the vehicle's deceleration coefficient; t d is the moment when the vehicle starts to decelerate, t1 is the time used in the cruising stage, and t2 is the time used in the deceleration stage, the unit is s.

[0075] Furthermore, when the vehicle is in the following zone, the speed change formula of the following vehicle during the deceleration phase is:

[0076]

[0077]

[0078] During the deceleration process, the total displacement of the target vehicle is calculated as follows:

[0079]

[0080] Where a max is the maximum deceleration of the vehicle, in m / s 2 ;v t-1 is the velocity at the previous moment, is the expected speed in m / s; is the expected spacing, in meters; t eis the moment when the preceding vehicle stops or the speed of the following vehicle fluctuates greatly; i is the vehicle number, i-1 represents the preceding vehicle, and i represents the following vehicle; t e is the time it takes for the preceding vehicle to decelerate and stop, in seconds; jam is the blocking distance; is the expected headway; Δv is the speed difference between the front and rear vehicles; a comf Deceleration for comfort;

[0081] Among them, the initial state of the leading vehicle is set to: v c =(20, 70) km / h, the speed change is shown in formula (1); the initial state of the following vehicle is set to: Δv c =(-2.5, 2.5) km / h; ΔS0 =(7, 80) m, where Δv c is the relative initial velocity between the following vehicle and the leading vehicle, and ΔS0 is the relative initial distance between the following vehicle and the leading vehicle.

[0082] Furthermore, the vehicle start process is a process where the vehicle gradually increases its speed from a stopped state to a normal driving speed state. When there is no vehicle ahead, its speed change curve is opposite to that of a vehicle stopping in an approaching zone. When there is a vehicle ahead, its speed change curve is opposite to that of a vehicle stopping in a following zone. The vehicle start process is a secondary start-stop state of the vehicle, and its speed change curve is opposite to that of a vehicle in a following zone at a low speed.

[0083] Based on the set vehicle initial state and actual vehicle operating conditions, the speed curves in the approaching and following zones are optimized and solved. The obtained speed and displacement curves are screened to establish a scenario trajectory dataset for the vehicle in the approaching and following zones.

[0084] Preferably, calibrating the longitudinal motion model based on the trajectory data comprises the following steps:

[0085] The leading vehicle trajectory data is used as input. When the vehicle is approaching, the leading vehicle trajectory data is set to the stop-acceleration state; when the vehicle is following, the leading vehicle trajectory data is set to the cruise-deceleration-stop-acceleration state, and the trailing vehicle trajectory data is output by the calibrated longitudinal motion model.

[0086] Compare the output data with the optimized trajectory data to determine the optimization target;

[0087] Solve the model parameters that minimize the optimization objective and calibrate the obtained model parameters.

[0088] Example 1: Obtain vehicle trajectory data and perform parameter calibration on the Wiedemann74 model;

[0089] Obtain vehicle trajectory data and, based on the function curves applied to the approaching and following zones, optimize the vehicle speed curve based on the three sub-goals of minimizing target distance, speed, and acceleration deviations, minimizing fuel consumption, and maximizing operating efficiency.

[0090] Set the initial state of vehicle movement in the approach zone: the initial speed is set to (20, 70) km / h. At each initial speed, the vehicle deceleration time is set to (15, 22) seconds. Output vehicle trajectory data for each case with a deceleration time ranging from 15 to 22 seconds, and different initial speeds. The obtained vehicle trajectory data includes multiple different parking distances, and ultimately establishes the approach zone vehicle movement trajectory dataset. For example, when the vehicle initial speed is 50 km / h and the deceleration time is 18 seconds, the optimized vehicle speed and displacement trajectory data are shown in Table 1:

[0091] Table 1

[0092]

[0093] Set the initial state of vehicle movement in the following zone: the initial speed of the leading vehicle is set to (20, 70) km / h, the deceleration time is (15, 22) seconds, the speed difference between the following vehicle and the leading vehicle is set to (-2.5, 2.5) km / h, and the distance difference is set to (7, 80) m. Under each combination of initial speed and deceleration time, the speed difference and displacement difference between the following vehicle and the leading vehicle also correspond to a set of optimized trajectory data. Output the vehicle trajectory data for different initial speeds in each case to establish a vehicle trajectory dataset for the following zone. For example, when the initial speed of the leading vehicle is 50 km / h, the deceleration time is 22 seconds, the speed difference is 1.5 km / h, and the distance difference is 20 m, the optimized speed and displacement trajectory data of the following vehicle are shown in Table 2:

[0094] Table 2

[0095]

[0096] The Wiedemann74 model requires parameter calibration. While the Wiedemann74 model requires numerous parameters, this invention focuses on longitudinal vehicle motion. Combining the model structure with relevant literature, and to simplify the calibration process, the following parameters are shown in the following examples: a desired following distance additive coefficient, a desired following distance multiplication coefficient, and an integrated parameter of a random number related to the parking gap; and a desired following distance additive coefficient, a desired following distance multiplication coefficient, and an integrated parameter of a random number related to the speed of the following gap. The least squares method is used to calibrate the following parameters related to low-gap parking and starting based on acquired trajectory data from the approach and following zones.

[0097] The basic principle of the Wiedemann74 model is to convert the stimulus received by the driver during the following process into the relative motion of the vehicle. Based on the relationship between the stimulus received by the driver and the model threshold, the following driver will take corresponding reaction measures. The specific formula is as follows:

[0098] When the vehicle is following the car, the following distance d after parameter integration f The calculation formula is:

[0099]

[0100] Where, L n-1 is the length of the front vehicle; a s b is the integration parameter of the addend and multiplier of the minimum stopping distance and the random number; v is the addition part of the addend and multiplier of the speed-related following distance and the integration parameter of the random number; v is the speed of the following vehicle.

[0101] Furthermore, the particle swarm optimization algorithm is used to calibrate the parameters of the Wiedemann74 model. The obtained trajectory data of the following zone and the trajectory data of the approaching zone are substituted into the model to solve the relevant parameters a. s =-3.9212, b v =7.7596. The error index of this parameter is verified using the validation data set.

[0102] The calculation formula of the error indicator vehicle spacing is as follows:

[0103]

[0104] Where i is the time series; s i The headway distance output by the model; is the headway distance obtained by actual optimization; N is the total number of samples.

[0105] After calculation, the error index E of the Wiedemann74 model after the parameters of the following zone are calibrated is f =1.5539, error index E after calibration of approach zone parameters a =1.4032.

[0106] Embodiment 2: Obtain vehicle trajectory data and calibrate parameters of a safe distance following model.

[0107] Step 1: The method for obtaining the car-following data is the same as that in Example 1. The data required for calibrating the safety distance model is only the driving trajectory data of the car-following zone.

[0108] Set the initial state of vehicle movement in the following zone: the initial speed of the leading vehicle is set to (20, 70) km / h, the deceleration time is (15, 22) seconds, the speed difference between the following vehicle and the leading vehicle is set to (-2.5, 2.5) km / h, and the distance difference is set to (7, 80) m. Under each combination of initial speed and deceleration time, the speed difference and displacement difference between the following vehicle and the leading vehicle also correspond to a set of optimized trajectory data. Output the vehicle trajectory data for different initial speeds in each case to establish a vehicle trajectory dataset for the following zone. For example, when the vehicle initial speed is 50 km / h, the deceleration time is 22 seconds, the speed difference is 1.5 km / h, and the distance difference is 30 m, the optimized vehicle speed and displacement trajectory data are shown in Table 3:

[0109] Table 3

[0110]

[0111] Step 2: Calibrate the safety distance model parameters. Parameters to be calibrated are selected based on relevant literature. The following examples demonstrate calibration parameters for headway and driving sensitivity. The following parameters for low-headway stopping and starting are calibrated using the least squares method, with vehicle headway error as the performance metric. Based on the acquired following zone trajectory data, the following parameters are calibrated.

[0112] When the vehicle is driving in a car-following mode, the calculation formula for the car-following distance h is as follows:

[0113] h=L+P+c3v+bc3(Δv) 2 ;

[0114] Where L is the length of the vehicle in front, which is generally 4.5m; P is the distance between the vehicles; Δv is the speed difference between the front and rear vehicles; b is a constant. If the speed of the rear vehicle is greater than that of the front vehicle, b = 0.1, otherwise b = 0; c3 is the driver sensitivity coefficient.

[0115] Furthermore, a particle swarm optimization algorithm was used to calibrate the parameters of the safety distance model. Substituting the obtained training set of trajectory data from the following zone into the model, the relevant parameters P = 7.2924 and c3 = 0.6033 were solved. The error indicators of these parameters were then verified using a validation dataset.

[0116] The calculation formula of the error indicator vehicle spacing is as follows:

[0117]

[0118] Where i is the time series; s i is the output headway; is the headway distance of the optimized data; N is the total number of samples.

[0119] After calculation, the error index E after the safety distance model parameters are calibrated f=0.8507.

[0120] The car-following data obtained by the present invention are all trajectory data of the vehicle deceleration and parking process. Therefore, Figure 2 The following distances shown all show a trend of decreasing from large to small, consistent with the actual vehicle deceleration process. It should be noted that the calculated following distance error index shows that there is a certain error between the vehicle following data output by the calibrated following model and the actual following data. This is due to the limited data used in the examples and parameter optimization issues. Subsequent additions to the data training set and parameter optimization can further reduce the error to a certain extent. The following model data acquisition method and parameter calibration method shown in the examples are feasible.

[0121] It should be noted that although the data set division method proposed in the specific embodiment of the present invention is based on the Wiedemann74 model, the established data set and the calibration method are not only applicable to the physiological-psychological model and safety distance model mentioned in the embodiment, but also to the vehicle following model used in other software.

[0122] The present invention provides a longitudinal motion model calibration system for low-distance start-stop vehicles, comprising:

[0123] A pre-processing module is used to divide vehicle driving scenarios in road traffic and classify them into driving scenarios corresponding to the vehicle approaching zone and driving scenarios corresponding to the vehicle following zone based on the longitudinal operation status of the vehicle. The minimum stopping distance between vehicles is determined based on the analysis of measured data.

[0124] A calculation module is used to obtain a driving trajectory dataset for the approaching and following zones based on a vehicle minimum stopping distance and speed optimization model;

[0125] The calibration module is used to calibrate the longitudinal motion model based on trajectory data.

[0126] In another embodiment of the present invention, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the longitudinal motion model calibration method for low-distance start-stop vehicles.

[0127] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for calibrating the longitudinal motion model of a low-distance start-stop vehicle in the above embodiment.

[0128] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0129] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0130] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calibrating the longitudinal motion model of a low-distance start-stop vehicle, characterized by: The following steps are involved: S1: Divide the vehicle driving scenarios in road traffic and categorize them into driving scenarios corresponding to the vehicle approaching zone and driving scenarios corresponding to the vehicle following zone based on the longitudinal operation status of the vehicle. Determine the minimum stopping distance between vehicles based on the analysis of measured data. S2: Based on the vehicle minimum parking distance and speed optimization model, obtain the scenario driving trajectory dataset of the approaching zone and the following zone; When the vehicle is in the approaching zone, the speed change formula of the following vehicle during the deceleration phase based on the speed optimization model is: （1） The calculation formula of the target vehicle's total displacement is: （2） Where, is the initial speed when deceleration begins, =(20,70)km / h, is the vehicle's terminal velocity, in m / s; is the vehicle deceleration coefficient; is the moment when the vehicle starts to decelerate, The time spent in the cruising phase, The time used in the deceleration stage, in seconds; When the vehicle is in the following zone, the speed change formula of the following vehicle during the deceleration phase is: （3） （4） During the deceleration process, the total displacement of the target vehicle is calculated as follows: （5） Where, is the maximum deceleration of the vehicle, in units of ; is the velocity at the previous moment, is the expected speed in m / s; is the expected spacing, in m; The time when the preceding vehicle stops or the speed of the following vehicle exceeds the set fluctuation; i is the vehicle number, i-1 represents the preceding vehicle, and i represents the following vehicle; is the deceleration and stopping time of the preceding vehicle, in seconds; is the blocking distance; is the expected headway; is the speed difference between the front and rear vehicles; Deceleration for comfort; Among them, the initial state of the leading vehicle is set as: =(20,70)km / h, the speed change is shown in formula (1); the initial state of the following vehicle is set as: = (-2.5, 2.5) km / h; =(7,80)m, where is the relative initial velocity between the following vehicle and the leading vehicle, is the relative initial distance between the following vehicle and the leading vehicle; S3: Calibrate the longitudinal motion model based on trajectory data.

2. The method for calibrating the longitudinal motion model of a low-distance start-stop vehicle according to claim 1, characterized in that: The driving scenarios corresponding to the vehicle approaching area include: When there is no vehicle ahead or the vehicle ahead has completely stopped, the following vehicle will decelerate within the set initial speed within the deceleration time to the vehicle ahead and stop within the minimum stopping distance. The vehicle ahead has stopped. The following vehicle first starts to decelerate within the set initial speed within the deceleration time. Before the vehicle ahead stops completely, it restarts and the following vehicle changes its parking position according to the speed. The vehicle following zone corresponds to the following driving scenarios: the vehicle in front does not stop, and the front and rear vehicles first follow each other within a set initial speed. Then the vehicle in front starts to decelerate, and the following vehicle also decelerates, and the two vehicles stop within the minimum stopping distance.

3. The method for calibrating the longitudinal motion model of a low-distance start-stop vehicle according to claim 1, characterized in that: Determining the minimum parking distance of vehicles based on the measured data analysis in step S1 includes the following steps: Collect vehicle stopping distance data from different lanes on actual roads at multiple signalized intersections; Conduct statistical analysis on the data characteristics and distribution characteristics of the collected vehicle parking distances to obtain the optimal parking distance and the extreme value of the parking distance endpoints; The extreme value of the parking distance endpoint is taken as the minimum parking distance of the vehicle.

4. The method for calibrating the longitudinal motion model of a low-distance start-stop vehicle according to claim 1, characterized in that: The vehicle start process is when the vehicle speed gradually increases from a stopped state to a normal driving speed. When there is no vehicle ahead, the speed change curve is opposite to that of the vehicle stopping process in the approaching zone. When there is a vehicle ahead, the speed change curve is opposite to that of the vehicle stopping process in the following zone. The vehicle startup process is a secondary start-stop state, and its speed change curve is the opposite of the process of the vehicle in the following zone at low speed; Based on the set vehicle initial state and actual vehicle operating conditions, the speed curves in the approaching and following zones are optimized and solved. The obtained speed and displacement curves are screened to establish a scenario trajectory dataset for the vehicle in the approaching and following zones.

5. The method for calibrating the longitudinal motion model of a low-distance start-stop vehicle according to claim 1, characterized in that: Calibrating the longitudinal motion model based on trajectory data includes the following steps: The leading vehicle trajectory data is used as input. When the vehicle is approaching, the leading vehicle trajectory data is set to the stop-acceleration state; when the vehicle is following, the leading vehicle trajectory data is set to the cruise-deceleration-stop-acceleration state, and the trailing vehicle trajectory data is output by the calibrated longitudinal motion model. Compare the output data with the optimized trajectory data to determine the optimization target; Solve the model parameters that minimize the optimization objective and calibrate the obtained model parameters.

6. A longitudinal motion model calibration system for low-distance start-stop vehicles, characterized by: A method for calibrating a longitudinal motion model of a low-distance start-stop vehicle based on any of claims 1-5, comprising: A pre-processing module is used to divide vehicle driving scenarios in road traffic and classify them into driving scenarios corresponding to the vehicle approaching zone and driving scenarios corresponding to the vehicle following zone based on the longitudinal operation status of the vehicle. The minimum stopping distance between vehicles is determined based on the analysis of measured data. A calculation module is used to obtain a driving trajectory dataset for the approaching and following zones based on a vehicle minimum stopping distance and speed optimization model; When the vehicle is in the approaching zone, the speed change formula of the following vehicle during the deceleration phase based on the speed optimization model is: （1） The calculation formula of the target vehicle's total displacement is: （2） Where, is the initial speed when deceleration begins, =(20,70)km / h, is the vehicle's terminal velocity, in m / s; is the vehicle deceleration coefficient; is the moment when the vehicle starts to decelerate, The time spent in the cruising phase, The time used in the deceleration stage, in seconds; When the vehicle is in the following zone, the speed change formula of the following vehicle during the deceleration phase is: （3） （4） During the deceleration process, the total displacement of the target vehicle is calculated as follows: （5） Where, is the maximum deceleration of the vehicle, in units of ; is the velocity at the previous moment, is the expected speed in m / s; is the expected spacing, in m; The time when the preceding vehicle stops or the speed of the following vehicle exceeds the set fluctuation; i is the vehicle number, i-1 represents the preceding vehicle, and i represents the following vehicle; is the deceleration and stopping time of the preceding vehicle, in seconds; is the blocking distance; is the expected headway; is the speed difference between the front and rear vehicles; Deceleration for comfort; Among them, the initial state of the leading vehicle is set as: =(20,70)km / h, the speed change is shown in formula (1); the initial state of the following vehicle is set as: = (-2.5, 2.5) km / h; =(7,80)m, where is the relative initial velocity between the following vehicle and the leading vehicle, is the relative initial distance between the following vehicle and the leading vehicle; The calibration module is used to calibrate the longitudinal motion model based on trajectory data.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for calibrating the longitudinal motion model of a low-distance start-stop vehicle as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for calibrating a longitudinal motion model for a low-distance start-stop vehicle as described in any one of claims 1 to 5 are implemented.

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