CN111429754A - Vehicle collision avoidance track risk assessment method under pedestrian crossing working condition - Google Patents
Vehicle collision avoidance track risk assessment method under pedestrian crossing working condition Download PDFInfo
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Abstract
The invention discloses a vehicle collision avoidance track risk assessment method under a pedestrian crossing working condition, which comprises the steps of firstly, obtaining position information of vehicles and pedestrians by using a laser radar and a binocular camera, further calculating speed information, and simultaneously establishing a vehicle coordinate system; then establishing a human-vehicle interaction model, and considering the pedestrian motion as superposition of Markov motion and motion generated by vehicle interference; then carrying out probability prediction on future positions of pedestrians according to the human-vehicle interaction model; and finally, establishing a risk evaluation function to evaluate the collision avoidance track of the vehicle. The invention can realize objective risk degree evaluation under the emergency collision avoidance condition of pedestrian crossing, predict the future movement of the pedestrian, consider the possible movement of the pedestrian under the vehicle interference, and further be used for decision planning of active safety.
Description
Technical Field
The invention relates to an intelligent driving technology, in particular to a vehicle collision avoidance track risk assessment method under a pedestrian crossing working condition.
Background
In recent years, intelligent driving technology has been a popular research area. The automobile companies and college research institutes at home and abroad obtain great achievements in the field of advanced assistant driving. However, as one of the important components of ADAS, the active collision avoidance system is required to determine a track capable of ensuring safety of various traffic bodies and a long road to be taken by coping with various uncertain factors caused by all complex traffic scenes in a real traffic environment. Collision avoidance is the most important function in intelligent driving technology, and the existing research mainly focuses on vehicle-vehicle collision, and less researches on human-vehicle collision under urban working conditions. However, according to the report issued by the World Health Organization (WHO)2013, one fourth of the people WHO die of traffic accidents every year in China are related to pedestrians. Therefore, the research on human-vehicle collision avoidance is also strongly necessary.
The risk assessment aiming at pedestrian collision avoidance, which is taken as a core part of pedestrian collision avoidance, is always a difficult point of research in the industry. The motion of the pedestrian is, by contrast, highly uncertain because of vehicle system dynamics and kinematic constraints. The walking speed and direction of the pedestrian can be changed instantly, particularly under the working condition that the pedestrian crosses a street, the pedestrian does not always move at a simple uniform speed when actually crossing a road, if a vehicle comes, the pedestrian can judge whether the pedestrian has danger according to the distance and the speed of the pedestrian, and then makes a decision of accelerating or decelerating movement, or on-site waiting or even backing. At the moment, the pedestrian and the collision avoidance vehicle serve as two intelligent decision bodies, and the decision of the same-direction movement or the opposite-direction movement can be made for the dangerous situation. This would quickly raise the risk of collision. Therefore, research into vehicle risk assessment for pedestrians is very important.
Disclosure of Invention
The invention aims to solve the technical problem of providing a vehicle collision avoidance track risk assessment method under the pedestrian crossing working condition aiming at the defects in the background technology.
The invention adopts the following technical scheme for solving the technical problems:
a vehicle collision avoidance track risk assessment method under a pedestrian crossing working condition comprises the following steps:
step 1), a laser radar and a binocular camera are arranged on a vehicle to measure position information of the vehicle and a pedestrian in front under urban working conditions in real time, a coordinate system comprising the pedestrian and the vehicle is established, and speed information of the vehicle and the pedestrian is calculated;
step 2), establishing a human-vehicle interaction model to predict the future motion state of the pedestrian, and considering the pedestrian motion into the overlapping of the Markov process without interference and the pedestrian motion under the reflection of collision avoidance conditions:
step 2.1), collecting road pedestrian street-crossing walking data, wherein the road pedestrian street-crossing walking data comprises a speed curve of a pedestrian freely crossing the street on a vehicle-free road and a speed curve of a pedestrian crossing the street on a vehicle-containing road;
step 2.2), expressing the movement of the pedestrian caused by free and interference-free walking;
the free movement of the pedestrian without external interference conforms to the Markov process, at the moment, the movement speed change of the pedestrian under the interference-free condition generally occurs at the step changing moment, so that the pedestrian passes through the street in the direction vertical to the road at the constant speed within the time delta t, and the movement state of the pedestrian can be expressed as follows:
in the formula, vped(t + Δ t) is the speed of the pedestrian at time t + Δ t, vped(t) is the speed of the pedestrian at time t; x is the number ofpedAnd ypedRespectively represent the horizontal and vertical coordinate position information of the pedestrian,
representing the speed function of the pedestrian in the y-direction with respect to time, xped(0) Representing the pedestrian's initial position in the x-axis direction; k is a preset constant; v is a preset random variable representing the average walking speed of the pedestrians, and conforms to normal distribution; is preset Gaussian white noise representing pedestrian speed fluctuation;
expressing the motion generated by the free walking of the pedestrian as a virtual free force F according to Newton's second lawf：
In the formula, vpedf(t) represents a velocity function of the free movement of the pedestrian with respect to time t, m being a preset virtual mass of the pedestrian;
step 2.3), expressing the change of the pedestrian motion state caused by the influence of the motion of the vehicles on the pedestrian on the motion of the pedestrian on the road;
quantifying the disturbance of the vehicle to the pedestrian motion as a virtual reflex force, expressed as:
in the formula, FvpIs the virtual reflection force of the vehicle to the pedestrian, rvIndicates the safe running radius of the vehicle, rpRepresenting the safe walking radius of the pedestrian, dvpRepresenting the real-time distance of the pedestrian from the vehicle, FvpyRepresenting the component of the vehicle disturbance force in the y-direction experienced by the pedestrian,
respectively representing the strength coefficient and the range coefficient of the interaction force of the pedestrian and the vehicle; thetavpThe included angle between the connecting line between the vehicle and the pedestrian and the direction vertical to the road is shown in fig. 3;
step 3), predicting the probability of the future position of the pedestrian:
obtaining each moment of the pedestrian according to the virtual free force and the virtual reflecting force in the step 2)Is subjected to a force FpedThen, calculating the acceleration in each motion step length, further obtaining the pedestrian state at the next moment, and finally obtaining the state at the last moment through iteration so as to complete the probabilistic prediction of the pedestrian position;
in the formula, ypedf(t)、ypedvpy(t) displacement functions related to time t generated by free force borne by the pedestrian and interference force in the y direction are respectively represented; y isped(t) is a displacement function of the pedestrian under vehicle disturbance with respect to time t;
step 4), evaluating the collision risk of the collision avoidance track;
calculating the time to reach the collision zone ttcz and the lateral displacement y of the vehicle after ttczveh(ttcz), establishing a risk assessment function as follows:
wherein J is a risk assessment function,
representing the relative distance between the man and the vehicle at the end-of-collision time n, rvehRepresenting the vehicle lateral radius.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1. the invention can realize the objective risk degree evaluation under the emergency collision avoidance working condition of pedestrian crossing;
2. when the risk degree is evaluated, the future movement of the pedestrian is predicted, and the possible movement of the pedestrian under the vehicle interference is considered;
3. the prediction and risk degree evaluation function established by the invention is further used for decision planning of active safety.
Drawings
FIG. 1 is a general block diagram of the present invention;
FIG. 2 is a schematic view of a vehicle coordinate system for predicting pedestrian motion;
FIG. 3 is a schematic diagram of a human-vehicle interaction model;
fig. 4 is a schematic diagram of pedestrian position prediction and risk assessment. .
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, components are exaggerated for clarity.
As shown in fig. 1, the invention discloses a vehicle collision avoidance track risk assessment method under a pedestrian crossing condition, which is characterized by comprising the following steps:
step 1), a laser radar and a binocular camera are arranged on a vehicle to measure the position information of the vehicle and a pedestrian in front under urban working conditions in real time, a coordinate system comprising the pedestrian and the vehicle as shown in figure 2 is established, and the speed information of the vehicle and the pedestrian is calculated;
step 2), establishing a human-vehicle interaction model to predict the future motion state of the pedestrian, and considering the motion of the pedestrian into the superposition of the Markov process without interference and the motion of the pedestrian under the reflection of the collision avoidance condition, as shown in FIG. 3:
step 2.1), collecting road pedestrian street-crossing walking data, wherein the road pedestrian street-crossing walking data comprises a speed curve of a pedestrian freely crossing the street on a vehicle-free road and a speed curve of a pedestrian crossing the street on a vehicle-containing road;
step 2.2), expressing the movement of the pedestrian caused by free and interference-free walking;
the free movement of the pedestrian without external interference conforms to the Markov process, at the moment, the movement speed change of the pedestrian under the interference-free condition generally occurs at the step changing moment, so that the pedestrian passes through the street in the direction vertical to the road at the constant speed within the time delta t, and the movement state of the pedestrian can be expressed as follows:
in the formula, vped(t + Δ t) is the speed of the pedestrian at time t + Δ t, vped(t) is the speed of the pedestrian at time t; x is the number ofpedAnd ypedRespectively represent the horizontal and vertical coordinate position information of the pedestrian,
representing the speed function of the pedestrian in the y-direction with respect to time, xped(0) Representing the pedestrian's initial position in the x-axis direction; k is a preset constant; v is a preset random variable representing the average walking speed of the pedestrians, and conforms to normal distribution; is preset Gaussian white noise representing pedestrian speed fluctuation;
expressing the motion generated by the free walking of the pedestrian as a virtual free force F according to Newton's second lawf：
In the formula, vpedf(t) represents a speed function of the free movement of the pedestrian with respect to time t, and m is a preset virtual mass of the pedestrian, and 1kg is taken;
step 2.3), expressing the change of the pedestrian motion state caused by the influence of the motion of the vehicles on the pedestrian on the motion of the pedestrian on the road;
quantifying the disturbance of the vehicle to the pedestrian motion as a virtual reflex force, expressed as:
in the formula, FvpIs the virtual reflection force of the vehicle to the pedestrian, rvIndicates the safe running radius of the vehicle, rpRepresenting the safe walking radius of the pedestrian, dvpRepresenting the real-time distance of the pedestrian from the vehicle, FvpyRepresenting the component of the vehicle disturbance force in the y-direction experienced by the pedestrian,
respectively representing the strength coefficient and the range coefficient of the interaction force of the pedestrian and the vehicle; thetavpThe included angle between the connecting line between the vehicle and the pedestrian and the direction vertical to the road is shown in fig. 3;
step 3), predicting the probability of the future position of the pedestrian:
obtaining the stress F of the pedestrian at each moment according to the virtual free force and the virtual reflecting force in the step 2)pedThen, calculating the acceleration in each motion step length, further obtaining the pedestrian state at the next moment, and finally obtaining the state at the last moment through iteration so as to complete the probabilistic prediction of the pedestrian position;
in the formula, ypedf(t)、ypedvpy(t) displacement functions related to time t generated by free force borne by the pedestrian and interference force in the y direction are respectively represented; y isped(t) is a displacement function of the pedestrian under vehicle disturbance with respect to time t;
step 4), as shown in fig. 4, evaluating collision risks of collision avoidance tracks;
calculating the time to reach the collision zone ttcz and the lateral displacement y of the vehicle after ttczveh(ttcz), establishing a risk assessment function as follows:
wherein J is a risk assessment function,
representing the man-vehicle relativity at the end-of-collision time nDistance, rvehRepresenting the vehicle lateral radius.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. A vehicle collision avoidance track risk assessment method under a pedestrian crossing condition is characterized by comprising the following steps:
step 1), a laser radar and a binocular camera are arranged on a vehicle to measure position information of the vehicle and a pedestrian in front under urban working conditions in real time, a coordinate system comprising the pedestrian and the vehicle is established, and speed information of the vehicle and the pedestrian is calculated;
step 2), establishing a human-vehicle interaction model to predict the future motion state of the pedestrian, and considering the pedestrian motion into the overlapping of the Markov process without interference and the pedestrian motion under the reflection of collision avoidance conditions:
step 2.1), collecting road pedestrian street-crossing walking data, wherein the road pedestrian street-crossing walking data comprises a speed curve of a pedestrian freely crossing the street on a vehicle-free road and a speed curve of a pedestrian crossing the street on a vehicle-containing road;
step 2.2), expressing the movement of the pedestrian caused by free and interference-free walking;
the free movement of the pedestrian without external interference conforms to the Markov process, at the moment, the movement speed change of the pedestrian under the interference-free condition generally occurs at the step changing moment, so that the pedestrian passes through the street in the direction vertical to the road at the constant speed within the time delta t, and the movement state of the pedestrian can be expressed as follows:
in the formula, vped(t + Δ t) is the speed of the pedestrian at time t + Δ t, cped(t) is the speed of the pedestrian at time t; x is the number ofpedAnd ypedRespectively represent the horizontal and vertical coordinate position information of the pedestrian,
representing the speed function of the pedestrian in the y-direction with respect to time, xped(0) Representing the pedestrian's initial position in the x-axis direction; k is a preset constant; v is a preset random variable representing the average walking speed of the pedestrians, and conforms to normal distribution; is preset Gaussian white noise representing pedestrian speed fluctuation;
expressing the motion generated by the free walking of the pedestrian as a virtual free force F according to Newton's second lawf：
In the formula, vpedf(t) represents a velocity function of the free movement of the pedestrian with respect to time t, m being a preset virtual mass of the pedestrian;
step 2.3), expressing the change of the pedestrian motion state caused by the influence of the motion of the vehicles on the pedestrian on the motion of the pedestrian on the road;
quantifying the disturbance of the vehicle to the pedestrian motion as a virtual reflex force, expressed as:
in the formula, FvpIs the virtual reflection force of the vehicle to the pedestrian, rvIndicates the safe running radius of the vehicle, rpTo indicate pedestriansSafe walking radius of dvpRepresenting the real-time distance of the pedestrian from the vehicle, FvpyRepresenting the component of the vehicle disturbance force in the y-direction experienced by the pedestrian,
respectively representing the strength coefficient and the range coefficient of the interaction force of the pedestrian and the vehicle; thetavpThe included angle between a connecting line between the vehicle and the pedestrian and the direction vertical to the road is formed;
step 3), predicting the probability of the future position of the pedestrian:
obtaining the stress F of the pedestrian at each moment according to the virtual free force and the virtual reflecting force in the step 2)pedThen, calculating the acceleration in each motion step length, further obtaining the pedestrian state at the next moment, and finally obtaining the state at the last moment through iteration so as to complete the probabilistic prediction of the pedestrian position;
in the formula, ypedf(t)、ypedvpy(t) displacement functions related to time t generated by free force borne by the pedestrian and interference force in the y direction are respectively represented; y isped(t) is a displacement function of the pedestrian under vehicle disturbance with respect to time t;
step 4), evaluating the collision risk of the collision avoidance track;
calculating the time to reach the collision zone ttcz and the lateral displacement y of the vehicle after ttczveh(ttcz), establishing a risk assessment function as follows:
wherein J is a risk assessment function,
representing the relative distance between the man and the vehicle at the end-of-collision time n, rvehRepresenting the vehicle lateral radius.
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| CN112487905A (en) * | 2020-11-23 | 2021-03-12 | 北京理工大学 | Method and system for predicting danger level of pedestrian around vehicle |
| CN113529635A (en) * | 2021-07-09 | 2021-10-22 | 上海熙众新能源技术有限公司 | Sprinkling control method and system and intelligent sprinkling truck |
| CN114067552A (en) * | 2021-11-08 | 2022-02-18 | 山东高速建设管理集团有限公司 | Pedestrian crossing track tracking and predicting method based on roadside laser radar |
| CN114371707A (en) * | 2021-12-31 | 2022-04-19 | 江苏大学 | Pedestrian trajectory prediction and active collision avoidance method and system considering human-vehicle interaction |
| CN114511999A (en) * | 2020-11-17 | 2022-05-17 | 郑州宇通客车股份有限公司 | Pedestrian behavior prediction method and device |
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| CN112487905B (en) * | 2020-11-23 | 2022-02-11 | 北京理工大学 | Method and system for predicting danger level of pedestrian around vehicle |
| CN112487905A (en) * | 2020-11-23 | 2021-03-12 | 北京理工大学 | Method and system for predicting danger level of pedestrian around vehicle |
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| CN114371707A (en) * | 2021-12-31 | 2022-04-19 | 江苏大学 | Pedestrian trajectory prediction and active collision avoidance method and system considering human-vehicle interaction |
| CN114781791A (en) * | 2022-03-11 | 2022-07-22 | 山东高速建设管理集团有限公司 | High-speed service area risk identification method based on holographic sensing data |
| CN114781791B (en) * | 2022-03-11 | 2023-09-29 | 山东高速建设管理集团有限公司 | High-speed service area risk identification method based on holographic perception data |
| CN115482662A (en) * | 2022-09-09 | 2022-12-16 | 湖南大学 | Method and system for predicting collision avoidance behavior of driver under dangerous working condition |
| CN116229725A (en) * | 2023-05-06 | 2023-06-06 | 北京市计量检测科学研究院 | Traffic control method and system based on simulated traffic scene |
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