CN112581756A - Driving risk assessment method based on hybrid traffic - Google Patents

Driving risk assessment method based on hybrid traffic Download PDF

Info

Publication number
CN112581756A
CN112581756A CN202011276318.4A CN202011276318A CN112581756A CN 112581756 A CN112581756 A CN 112581756A CN 202011276318 A CN202011276318 A CN 202011276318A CN 112581756 A CN112581756 A CN 112581756A
Authority
CN
China
Prior art keywords
motor vehicle
vehicle
risk assessment
representing
force
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011276318.4A
Other languages
Chinese (zh)
Other versions
CN112581756B (en
Inventor
耿可可
胡敬宇
殷国栋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southeast University
Original Assignee
Southeast University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southeast University filed Critical Southeast University
Priority to CN202011276318.4A priority Critical patent/CN112581756B/en
Publication of CN112581756A publication Critical patent/CN112581756A/en
Application granted granted Critical
Publication of CN112581756B publication Critical patent/CN112581756B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/015—Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Educational Administration (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Traffic Control Systems (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

A driving risk assessment method based on hybrid traffic relates to the field of unmanned driving risk assessment. The method specifically comprises the following steps: inputting a comprehensive traffic scene comprising three models of motor vehicles, non-motor vehicles and road traffic environments; representing the individual behavior result of any individual in the comprehensive traffic scene as the combination of different acting forces, and synthesizing the combination into a unified expression, wherein the individual is a motor vehicle or a non-motor vehicle; respectively abstracting the motor vehicle and the non-motor vehicle into a motor vehicle driving risk assessment model and a non-motor vehicle driving risk assessment model according to the unified expression; and carrying out the comprehensive traffic scene risk assessment based on the motor vehicle driving risk assessment model, the non-motor vehicle driving risk assessment model and the risk assessment model interacted among other interference items. The comprehensive risk assessment method can simply and efficiently assess the collision risks of different types of individuals in a mixed traffic scene.

Description

Driving risk assessment method based on hybrid traffic
Technical Field
The invention relates to the field of unmanned driving risk assessment, in particular to a driving risk assessment method based on hybrid traffic.
Technical Field
The social force model is an important theoretical basis for simulating the behaviors of people and other simulations based on the behavior, has been successfully applied in various fields such as crowd evacuation, traffic simulation flow and the like, can simulate some common crowd self-organization phenomena, and is also an important method for researching the traffic flow theory. If the social force model is improved, the method can be applied to unmanned road risk assessment, so that the influence rule of each traffic element on the driving safety is revealed, and the dynamic change trend of the driving risk is predicted.
Disclosure of Invention
Aiming at the defects in the field, the invention provides a driving risk assessment method based on mixed traffic, aiming at realizing the collision risk assessment of mixed traffic scenes comprising motor vehicles, non-motor vehicles and road traffic environments, simply and efficiently simulating the traffic behaviors of different individuals in the traffic scenes and providing effective and reliable test data for unmanned driving tests.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a driving risk assessment method based on hybrid traffic comprises the following steps:
(1) inputting a mixed traffic scene comprising three models of motor vehicles, non-motor vehicles and road traffic environment;
(2) the individual behavior results of any individual in the mixed traffic scene are characterized as combinations of different acting forces, the individual comprises a motor vehicle and a non-motor vehicle, and the integrated unified expression is as follows:
Figure BDA0002779162460000011
in the formula:
Figure BDA0002779162460000012
representing the self-driving force of the individual i,
Figure BDA0002779162460000013
representing the current speed, M, of the individual iiRepresenting the equivalent mass of the individual i,
Figure BDA0002779162460000014
representing the desired rate of the individual i at time t,
Figure BDA0002779162460000015
speed direction, τ, representing the desired velocity of individual i at time tiWhich represents the relaxation time of the individual i,
Figure BDA0002779162460000016
indicating that individual i is subjected to a repulsive force from other individuals j;
Figure BDA0002779162460000017
showing the repulsive force between the individual and the road traffic environment, and xi showing the random fluctuation item of the resultant force;
(3) respectively abstracting the motor vehicle and the non-motor vehicle into a motor vehicle driving risk assessment model and a non-motor vehicle driving risk assessment model according to the unified expression;
(4) calculating the interactive collision risk among different types of individuals;
(5) and evaluating the driving risk of the mixed traffic scene based on the motor vehicle driving risk evaluation model, the non-motor vehicle driving risk evaluation model and the collision risk evaluation model interacted among other interference items.
Preferably, in the step (3), the vehicle driving risk assessment model comprises a vehicle self-driving force, a repulsive force of other vehicles within a predetermined range and a repulsive force of a road traffic environment within a predetermined range; due to self-driving force of motor vehicle
Figure BDA0002779162460000021
Influenced by disturbance factors during driving, for which purpose the desired speed of the vehicle a is determined
Figure BDA0002779162460000022
Is modified into
Figure BDA0002779162460000023
Figure BDA0002779162460000024
In the formula:
Figure BDA0002779162460000025
for the corrected desired speed, HsafetyTo a safety factor, LsafetyTo reflect the distance safely, dabIs the distance between motor vehicle a and motor vehicle b, raIs the radius of the motor vehicle a.
It is further preferred that the disturbance factors include speed limit, field of view and vehicle condition of the road environment, for which purpose the actual speed is introduced
Figure BDA0002779162460000026
And corrected desired speed
Figure BDA0002779162460000027
The relational expression of (1):
Figure BDA0002779162460000028
Figure BDA0002779162460000029
in the formula: the actual speed being limited by the maximum speed of the individual
Figure BDA00027791624600000210
Wherein the maximum speed is the speed limit value of the current road environment, and when the speed limit value is greater than or equal to the speed limit value
Figure BDA00027791624600000211
When it is, anThe body advancing speed is
Figure BDA00027791624600000212
When the speed limit value is less than
Figure BDA00027791624600000213
When the individual is advancing at a speed of
Figure BDA00027791624600000214
The self-driving force of the motor vehicle
Figure BDA00027791624600000215
The correction is as follows:
Figure BDA00027791624600000216
in the formula: t iscIs the reaction time of the motor vehicle.
Further preferably, the repulsive force of other vehicles within the predetermined range includes the vehicle b in the adjacent lane1Acting force on motor vehicle a
Figure BDA00027791624600000217
And motor vehicle b in current lane2Acting force on motor vehicle a
Figure BDA00027791624600000218
The motor vehicle b in the adjacent lane1Acting force of
Figure BDA00027791624600000219
The expression of (a) is as follows:
Figure BDA0002779162460000031
in the formula: u shapecThe scale factor of the magnitude of the repulsive force of other vehicles in the adjacent lane is shown,
Figure BDA0002779162460000032
showing motor vehicles a and b1The sum of the radii of (a) and (b),
Figure BDA0002779162460000033
showing motor vehicles a and b1Distance between, RcIndicating the sensitivity of the repulsion forces of other vehicles to distance,
Figure BDA0002779162460000034
indicating the direction from the centre of the vehicle a to the vehicle b1A unit vector of the center;
the motor vehicle b on the current lane2Acting force of
Figure BDA0002779162460000035
The expression of (a) is as follows:
Figure BDA0002779162460000036
Figure BDA0002779162460000037
in the formula: a ismaxRepresenting the maximum acceleration of the vehicle a, bcIndicating motor vehicle b2Deceleration of vaRepresenting the current speed of the motor vehicle a,
Figure BDA0002779162460000038
showing motor vehicles a and b2Speed difference of(s)*Representing the distance, s, of the motor vehicle after reaction0Indicating the minimum safe distance between the vehicles,
Figure BDA0002779162460000039
showing motor vehicles a and b2The distance between the two or more of the two or more,
Figure BDA00027791624600000310
a unit vector representing the direction of motion of the motor vehicle a; if the motor vehicle changes lane, the force applied to the motor vehicle during lane change is increased
Figure BDA00027791624600000311
Figure BDA00027791624600000312
In the formula: u shapel cThe magnitude-proportional coefficient, R, representing the repulsive force of other vehicles during a lane changel cIndicating the sensitivity of the repulsion force of other vehicles to distance during a lane change, dalRepresenting the distance between the vehicle a and the target lane,
Figure BDA00027791624600000313
a unit vector representing the motor vehicle a pointing to the target lane.
Further preferably, the repulsive force of the road traffic environment within the predetermined range
Figure BDA00027791624600000314
Is related to road conditions, the worse the road conditions, the greater the likelihood of a collision; the road condition includes a road surface adhesion coefficient muiRoad curvature ρiRoad grade τiAnd road visibility deltaiDefining the road condition influence factor at the preset position of the current individual as RiSaid R isiThe expression of (a) is as follows:
Figure BDA00027791624600000315
in the formula: gamma ray1、γ2、γ3、γ4Are all undetermined constants, and γ1、γ2Less than 0, gamma3、γ4Greater than 0, mu*Is a standard road surface adhesion coefficient, delta*For standard road visibility, p*For standard road curvature, τ*Is a standard road grade;
repulsive force of the improved road traffic environment
Figure BDA00027791624600000316
The expression of (a) is as follows:
Figure BDA0002779162460000041
Figure BDA0002779162460000042
in the formula: u'cMagnitude proportionality coefficient, d, representing road traffic environment repulsive forceasRepresenting the distance, R ', between the motor vehicle a and the lane boundary'cThe sensitivity coefficient of the road traffic environment repulsive force to the distance is shown,
Figure BDA0002779162460000043
is a unit vector pointing from the closest position to the vehicle on the lane boundary to the center of the vehicle a.
Preferably, in the step (3), the non-motor vehicle driving risk assessment model
Figure BDA0002779162460000044
Including direct mutual repulsion forces generated by the front vehicle avoiding collisions with other non-motor vehicles
Figure BDA0002779162460000045
And the acting force of the non-motor vehicle for realizing the effects of avoiding and exceeding
Figure BDA0002779162460000046
The expression is as follows:
Figure BDA0002779162460000047
Figure BDA0002779162460000048
in the formula: u shapebRepresenting the action strength of the non-motor vehicle repulsion force; b ismRepresenting the acting distance of the non-motor vehicle repulsion force;
Figure BDA0002779162460000049
a unit vector representing that the non-motor vehicle m points to the non-motor vehicle n; b represents the distance between non-motor vehicle m and non-motor vehicle n, and has:
Figure BDA00027791624600000410
in the formula: dmnRepresenting the distance of a central point connecting line between the non-motor vehicle m and the non-motor vehicle n; x is the number ofn、ynRespectively showing the short axis length and the long axis length of the obstacle; thetamnRepresenting the angle of the centre line between non-motor vehicle m and non-motor vehicle n.
Further preferably, when the non-motor vehicle encounters an obstacle, the non-motor vehicle surmounts the obstacle by changing the direction, so that a force for realizing the avoidance and surmounting effects is introduced, the magnitude of the force is set to be proportional to the mutual repulsive force, the direction is perpendicular to the direction of the mutual repulsive force, and the expression is as follows:
Figure BDA00027791624600000411
in the formula: beta is amRepresenting the desired speed factor of the non-motor vehicle, the highest speed of the non-motor vehicle being greater than the desired speed when overriding, and thus betamGreater than 1;
Figure BDA00027791624600000412
representing a transcendental vector, the expression is as follows:
Figure BDA00027791624600000413
in the formula: w represents the transverse distance between the center points of the overtaking and overtaken non-motor vehicles at the target point;
Figure BDA00027791624600000414
representing a unit vector at a distance w from the vehicle m to the overrun side of vehicle n.
Further preferably, the
Figure BDA00027791624600000415
And
Figure BDA00027791624600000416
dependent on the angle of view, thus introducing a direction-dependent weight ωmnThe expression is as follows:
Figure BDA0002779162460000051
in the formula: lambda [ alpha ]bIs a constant number of times, and is,
Figure BDA0002779162460000052
representing the included angle between the relative position vector of the non-motor vehicle m and the non-motor vehicle n and the advancing direction of the non-motor vehicle m;
improved non-motor vehicle collision risk assessment model
Figure BDA0002779162460000053
The expression of (a) is as follows:
Figure BDA0002779162460000054
preferably, in step (4), the collision risk assessment model interacted between the individuals of different types mainly comprises a collision risk assessment model interacted between a motor vehicle and a non-motor vehicle.
Preferably, in the comprehensive collision risk assessment model interacted between the motor vehicle and the non-motor vehicle, if no physical isolation exists between the motor vehicle lane and the non-motor vehicle lane, the single individual model is modified to establish a motor vehicle-non-motor vehicle hybrid traffic driving risk assessment model;
the modification process of the motor vehicle-non-motor vehicle mixed traffic driving risk assessment model is as follows:
for a motor vehicle, if there is a non-motor vehicle in the side view, the motor vehicle will be subjected to a side force:
Figure BDA0002779162460000055
in the formula:
Figure BDA0002779162460000056
and
Figure BDA0002779162460000057
is a constant coefficient, r represents the distance between the vehicle and the non-vehicle, and if there is a non-vehicle in front of the lane,
Figure BDA0002779162460000058
representing a direction vector of the non-motor vehicle pointing to the motor vehicle;
the vehicle is subjected to a force of deceleration:
Figure BDA0002779162460000059
sa＝s1+vaΔt
wherein s isaThe distance, s, between the vehicle and the non-vehicle after the vehicle reaction1For minimum safety distance between motor vehicle and non-motor vehicle, vaIs the speed of the motor vehicle, at is the reaction time, d is the distance between the motor vehicle and the non-motor vehicle,
Figure BDA00027791624600000510
a unit vector of a current individual velocity direction;
for non-motor vehicles, if there is a motor vehicle in the side view, the non-motor vehicle will be subjected to a side force:
Figure BDA00027791624600000511
wherein the content of the first and second substances,
Figure BDA00027791624600000512
and
Figure BDA00027791624600000513
is a constant coefficient of the number of the optical fiber,
Figure BDA00027791624600000514
the speed of the motor vehicle is the same as the speed of the motor vehicle,
Figure BDA00027791624600000515
if the motor vehicle exists in front of the non-motor vehicle, the non-motor vehicle is subjected to the action force of deceleration:
Figure BDA00027791624600000516
sn＝s1+vnΔt
wherein s isnIs the distance between the non-motor vehicle and the motor vehicle after reaction, b'cIndicating deceleration of non-motor vehicles, vnIs the speed of the non-motor vehicle;
when the non-motor vehicle enters the motor vehicle lane, the non-motor vehicle needs to return to the non-motor vehicle lane: the driving force experienced by the non-motor vehicle is expressed as:
Figure BDA0002779162460000061
in the formula:
Figure BDA0002779162460000062
and
Figure BDA0002779162460000063
is a constant coefficient of r3Indicating the lateral distance between the non-motor vehicle and the rear motor vehicle,
Figure BDA0002779162460000064
a unit vector representing a unit vector pointing from the center of the non-motor vehicle to a nearest position of the non-motor vehicle on the lane of the non-motor vehicle;
in the motor vehicle-non-motor vehicle hybrid traffic risk assessment model, the social force of the motor vehicle a at the moment t is as follows:
Figure BDA0002779162460000065
the social forces experienced by the non-motor vehicle n at time t are:
Figure BDA0002779162460000066
advantageous effects
Different from the existing driving risk assessment model, the driving risk assessment method based on mixed traffic provided by the invention can efficiently and reliably assess the dynamic behaviors and collision risks of different individuals in a traffic scene, and has the main advantages that:
(1) model representation of different traffic modes such as motor vehicles, non-motor vehicles and the like in a complex traffic scene is unified through a social force model, and traffic flow problems are converted into force problems;
(2) the collision risk assessment model covers standard behaviors of acceleration/deceleration, lane keeping, lane change and the like.
Drawings
FIG. 1 is a block diagram of the present invention;
FIG. 2 is a diagram of a model framework provided by the present invention;
FIG. 3 is a schematic view of a motor vehicle subjected to road boundary forces provided by the present invention;
FIG. 4 is a reference diagram of the non-motor vehicle stress provided by the present invention.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The invention is further described with reference to the following drawings and specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. The experimental procedures, in which specific conditions are not noted in the following examples, are generally carried out under conventional conditions or conditions recommended by the manufacturers.
As shown in fig. 1, the driving risk assessment method based on hybrid traffic of the embodiment includes the following steps:
(1) inputting a mixed traffic scene containing three models of motor vehicles, non-motor vehicles and road traffic environments, as shown in FIG. 2; the road traffic environment comprises highways, urban arterial roads and other roads with good structurization, and the roads have the characteristics of clear road sign lines, single road background environment, obvious road geometric characteristics and the like;
(2) representing the individual behavior result of any individual in the mixed traffic scene as the combination of different acting forces, and synthesizing the combination into a unified expression, wherein the individual is a motor vehicle or a non-motor vehicle;
(3) respectively abstracting the motor vehicle and the non-motor vehicle into a motor vehicle driving risk assessment model and a non-motor vehicle driving risk assessment model according to the unified expression;
(4) calculating the interactive collision risk among different types of individuals;
(5) and performing the risk assessment of the hybrid traffic scene based on the motor vehicle driving risk assessment model, the non-motor vehicle driving test risk assessment model and the collision risk assessment model interacted among other interference items.
In the step (2), the unified expression is:
Figure BDA0002779162460000071
in the formula:
Figure BDA0002779162460000072
representing the self-driving force of the individual i,
Figure BDA0002779162460000073
representing the current speed, M, of the individual iiRepresenting the equivalent mass of the individual i,
Figure BDA0002779162460000074
representing the desired rate of the individual i at time t,
Figure BDA0002779162460000075
speed direction, τ, representing the desired velocity of individual i at time tiWhich represents the relaxation time of the individual i,
Figure BDA0002779162460000076
indicating that individual i is subjected to a repulsive force from the other individual j. Wherein the repulsive force between individuals includes a psychological repulsive force
Figure BDA0002779162460000077
And physical repulsive force
Figure BDA0002779162460000078
Repulsion force between individuals in the same race and road traffic environment
Figure BDA0002779162460000079
Also includes psychological repulsion
Figure BDA00027791624600000710
And physical repulsive force
Figure BDA00027791624600000711
Xi is a random fluctuation term of resultant force, derived from the action of an individual accidentally or intentionally deviating from the normal rule of motionRandom and non-systematic, representing uncertain individual behavior;
the current individual being subjected to the repulsive force of a neighboring homogeneous individual
Figure BDA00027791624600000712
The different behavior characteristics of different classes are divided into two different forms of repulsion between vehicles and repulsion between non-vehicles.
In the step (3), the driving risk assessment model of the motor vehicle comprises the self-driving force of the motor vehicle, the repulsive force of other motor vehicles in a specific range and the self-driving force of the motor vehicle
Figure BDA00027791624600000713
The desired speed is corrected by a plurality of factors including the allowable speed of the environment within a specific range, the field of view, the vehicle condition, and the like
Figure BDA00027791624600000714
To correct the later desired speed
Figure BDA00027791624600000715
The corrected post desired velocity
Figure BDA00027791624600000716
The expression of (a) is as follows:
Figure BDA00027791624600000717
wherein
Figure BDA00027791624600000718
In order to correct the later desired speed,
Figure BDA00027791624600000719
to the desired speed, HsafetyTo a safety factor, LsafetySafe reflection of distance, dabFor motor vehicles a and motor vehiclesDistance between cars b, raIs the radius of the motor vehicle a.
As shown in FIG. 3, the repulsive force from the road boundary or sign line is specifically the repulsive force F that the vehicle c receives from the left and right road boundaries on the driving roadrAnd Fl(repulsive force F when the vehicle is on the road center linerAnd FlWill cancel each other); as the motor vehicle gets closer to a boundary, it experiences a larger repulsive force from the boundary.
The repulsive force of other vehicles in the specific range includes the vehicles b in the adjacent lanes1Acting force on motor vehicle a
Figure BDA0002779162460000081
And motor vehicle b in current lane2Acting force on motor vehicle a
Figure BDA0002779162460000082
The motor vehicle b in the adjacent lane1Acting force of
Figure BDA0002779162460000083
The expression of (a) is as follows:
Figure BDA0002779162460000084
wherein, UcIs the magnitude proportionality coefficient of the repulsive force, rabIs a motor vehicle a and a motor vehicle b1The sum of the radii of (a) and (b),
Figure BDA0002779162460000085
is a motor vehicle a and a motor vehicle b1Distance between, RcIs the sensitivity coefficient of this repulsive force to distance,
Figure BDA0002779162460000086
is directed from the center of the motor vehicle a to the motor vehicle b1A unit vector of the center;
the current laneMotor vehicle b2Acting force of (2) and motor vehicles (c) and (b)2With respect to the distance between vehicles and the speed difference, by means of the brake deceleration semantic in IDM, to approximate the distance between the vehicle c and the vehicle b2Repulsive force
Figure BDA0002779162460000087
The expression is as follows:
Figure BDA0002779162460000088
Figure BDA0002779162460000089
wherein, amaxRepresenting the maximum acceleration of the vehicle a, bcIndicating motor vehicle b2Deceleration of vaRepresenting the current speed of the motor vehicle a,
Figure BDA00027791624600000810
showing motor vehicles a and b2Speed difference of(s)*Representing the distance, s, of the motor vehicle after reaction0Indicating the minimum safe distance between the vehicles,
Figure BDA00027791624600000811
showing motor vehicles a and b2The distance between the two or more of the two or more,
Figure BDA00027791624600000812
a unit vector representing the direction of motion of the vehicle a.
If lane change is considered, the force applied to a lane change is increased
Figure BDA00027791624600000813
The force applied during lane change
Figure BDA00027791624600000814
The expression is as follows:
Figure BDA00027791624600000815
wherein, Ul cIs the magnitude proportionality coefficient of the repulsive force, Rl cIs the sensitivity coefficient of the repulsive force to the distance, dalRepresenting the distance between the vehicle a and the target lane,
Figure BDA00027791624600000816
a unit vector representing the motor vehicle a pointing to the target lane.
The repulsive force of the road traffic environment within the specific range
Figure BDA0002779162460000091
Is related to road conditions, the worse the road conditions, the greater the likelihood of a collision; the road conditions comprise road surface adhesion coefficient, road curvature, road gradient, road visibility and the like; defining the influence factor of the road condition at the specific position of the current individual as Ri；
The road condition influence factor R of the specific position where the current individual is locatediThe expression of (a) is as follows:
Figure BDA0002779162460000092
wherein, deltaiFor road visibility, muiIs the road adhesion coefficient, piFor road curvature, τiIs the road gradient, gamma1，γ2，γ3，γ4Are all undetermined constants, and γ1，γ2＜0，γ3，γ4＞0，μ*Is a standard road surface adhesion coefficient, delta*For standard road visibility, p*For standard road curvature, τ*Is a standard road grade.
The repulsive force of the road traffic environment improved within the specific range
Figure BDA0002779162460000093
The expression of (a) is as follows:
Figure BDA0002779162460000094
the repulsive force of the road traffic environment
Figure BDA0002779162460000095
Can be expressed as:
Figure BDA0002779162460000096
wherein, U'cIs the magnitude proportionality coefficient of the repulsive force, dasIs the distance, R ', between the motor vehicle a and the lane boundary'cIs the sensitivity coefficient of this repulsive force to distance,
Figure BDA0002779162460000097
is a unit vector pointing to the center of the motor vehicle a from the position nearest to the motor vehicle on the lane boundary;
as shown in FIG. 4, in step (3), the non-motor vehicle collision risk assessment model
Figure BDA0002779162460000098
Direct mutual repulsion forces, mainly due to avoidance of collisions with other non-motor vehicles
Figure BDA0002779162460000099
And the acting force of the non-motor vehicle for realizing the effects of avoiding and exceeding
Figure BDA00027791624600000910
The expression is as follows:
Figure BDA00027791624600000911
the direct mutual repulsive force of the non-motor vehicle for avoiding collision with other non-motor vehicles is generated for meeting the requirement of a safety space, namely a perception domain, and the conscious reaction of the non-motor vehicle for avoiding collision with other individuals can be expressed as follows:
Figure BDA0002779162460000101
wherein, UbStrength of non-motor vehicle repulsion force; b ismRepresenting the acting distance of the non-motor vehicle repulsion force;
Figure BDA0002779162460000102
a unit vector representing that the non-motor vehicle m points to the non-motor vehicle n; b represents the distance between non-motor vehicle m and non-motor vehicle n, and has:
Figure BDA0002779162460000103
wherein d ismnRepresenting the distance of a central point connecting line between the non-motor vehicle m and the non-motor vehicle n; x is the number ofn、ynRespectively showing the short axis length and the long axis length of the obstacle; thetamnRepresenting the angle of the centre line between non-motor vehicle m and non-motor vehicle n.
In order to embody the characteristic, force for realizing avoiding and exceeding effects is introduced, the magnitude of the set force is proportional to mutual repulsive force, the direction is vertical to the direction of the repulsive force, and the expression is as follows:
Figure BDA0002779162460000104
wherein, betamIndicating a desired speed factor of the non-motor vehicle, the maximum speed of the non-motor vehicle being greater than the desired speed when overriding, becauseBeta ismGreater than 1;
Figure BDA0002779162460000105
representing a transcendental vector, the expression is as follows:
Figure BDA0002779162460000106
w is the transverse distance between the center points of the overtaking and overtaken vehicles at the target point;
Figure BDA0002779162460000107
the unit vector of the distance w from the non-motor vehicle m to the side (the side with larger clearance and smaller density) with better exceeding condition of the non-motor vehicle n points to the left side if the left side is larger, and otherwise points to the right side.
Avoiding direct mutual repulsion force generated by collision with other non-motor vehicles
Figure BDA0002779162460000108
And the acting force of the non-motor vehicle for realizing the effects of avoiding and exceeding
Figure BDA0002779162460000109
Depending on the view angle, the influence of an individual located laterally behind a non-motor vehicle is much smaller than that of an individual located forward, introducing a direction-dependent weight ωmnThe expression is as follows:
Figure BDA00027791624600001010
wherein λ isbIs a constant number of times that the number of the first,
Figure BDA00027791624600001011
representing the angle between the relative position vector of the non-motor vehicle m and the non-motor vehicle n and the advancing direction of the non-motor vehicle m.
The improved non-motor vehicle collision risk assessment model
Figure BDA00027791624600001012
The expression of (a) is as follows:
Figure BDA0002779162460000111
the risk assessment model is mainly applied to data testing of unmanned vehicles, the scene is mainly suitable for roads with good structuralization, such as expressways, urban arterial roads and the like, and the risk assessment model has the characteristics of clear road sign lines, single road background environment, obvious road geometric characteristics and the like, so that the interaction between motor vehicles and non-motor vehicles is mainly considered.
In the step (4), the interactive collision risk assessment model between the different individuals mainly comprises an interactive driving risk assessment model between a motor vehicle and a non-motor vehicle.
In the interactive comprehensive collision risk assessment model between the motor vehicle and the non-motor vehicle, if no physical isolation exists between the motor vehicle lane and the non-motor vehicle lane, a motor vehicle-non-motor vehicle hybrid traffic driving risk assessment model is established by modifying the single individual model;
the modification process of the motor vehicle-non-motor vehicle mixed traffic driving risk assessment model is as follows:
for a motor vehicle, if there is a non-motor vehicle in the side view, the motor vehicle will be subjected to a side force:
Figure BDA0002779162460000112
in the formula:
Figure BDA0002779162460000113
and
Figure BDA0002779162460000114
is a constant coefficient, r represents the distance between the vehicle and the non-vehicle if there is a non-vehicle in front of the lane，
Figure BDA0002779162460000115
Representing a direction vector of the non-motor vehicle pointing to the motor vehicle;
the vehicle is subjected to a force of deceleration:
Figure BDA0002779162460000116
sa＝s1+vaΔt
wherein s isaThe distance, s, between the vehicle and the non-vehicle after the vehicle reaction1For minimum safety distance between motor vehicle and non-motor vehicle, vaIs the speed of the motor vehicle, at is the reaction time, d is the distance between the motor vehicle and the non-motor vehicle,
Figure BDA0002779162460000117
a unit vector of a current individual velocity direction;
for non-motor vehicles, if there is a motor vehicle in the side view, the non-motor vehicle will be subjected to a side force:
Figure BDA0002779162460000118
wherein the content of the first and second substances,
Figure BDA0002779162460000119
and
Figure BDA00027791624600001110
is a constant coefficient of the number of the optical fiber,
Figure BDA00027791624600001111
the speed of the motor vehicle is the same as the speed of the motor vehicle,
Figure BDA00027791624600001112
is a direction vector of the motor vehicle pointing to the non-motor vehicle,if there is a vehicle in front of the non-motor vehicle, the non-motor vehicle is subjected to a force of deceleration:
Figure BDA00027791624600001113
sn＝s1+vnΔt
wherein s isnIs the distance between the non-motor vehicle and the motor vehicle after reaction, b'cIndicating deceleration of non-motor vehicles, vnIs the speed of the non-motor vehicle;
because physical isolation does not exist, the acting force of the non-motor vehicle on the boundary adjacent to the motor vehicle lane is far smaller than that of the boundary on the other side, in real life, when the non-motor vehicle entering the motor vehicle lane causes serious interference to the movement of the motor vehicle, the motor vehicle can remind the non-motor vehicle in front through whistle operation, and therefore, the force for driving the non-motor vehicle to return to the non-motor vehicle lane is added in a model to simulate the phenomenon:
Figure BDA0002779162460000121
wherein the content of the first and second substances,
Figure BDA0002779162460000122
and
Figure BDA0002779162460000123
is a constant coefficient of r3Indicating the lateral distance between the non-motor vehicle and the rear motor vehicle,
Figure BDA0002779162460000124
representing a unit vector pointing from the center of the non-motor vehicle to the nearest location on the lane of the non-motor vehicle.
In the motor vehicle-non-motor vehicle hybrid traffic risk assessment model, the social force of the motor vehicle a at the moment t is as follows:
Figure BDA0002779162460000125
the social forces experienced by the non-motor vehicle n at time t are:
Figure BDA0002779162460000126
furthermore, it should be understood that various changes and modifications can be made by one skilled in the art after reading the above description of the present invention, and equivalents also fall within the scope of the invention as defined by the appended claims.

Claims (10)

1. A driving risk assessment method based on hybrid traffic is characterized by comprising the following steps:
(1) inputting a mixed traffic scene comprising three models of motor vehicles, non-motor vehicles and road traffic environment;
(2) the individual behavior results of any individual in the mixed traffic scene are characterized as combinations of different acting forces, the individual comprises a motor vehicle and a non-motor vehicle, and the integrated unified expression is as follows:
Figure FDA0002779162450000011
in the formula:
Figure FDA0002779162450000012
representing the self-driving force of the individual i,
Figure FDA0002779162450000013
representing the current speed, M, of the individual iiRepresenting the equivalent mass of the individual i,
Figure FDA0002779162450000014
representing the desired rate of the individual i at time t,
Figure FDA0002779162450000015
speed direction, τ, representing the desired velocity of individual i at time tiWhich represents the relaxation time of the individual i,
Figure FDA0002779162450000016
indicating that individual i is subjected to a repulsive force from other individuals j;
Figure FDA0002779162450000017
showing the repulsive force between the individual and the road traffic environment, and xi showing the random fluctuation item of the resultant force;
(3) respectively abstracting the motor vehicle and the non-motor vehicle into a motor vehicle driving risk assessment model and a non-motor vehicle driving risk assessment model according to the unified expression;
(4) calculating the interactive collision risk among different types of individuals;
(5) and evaluating the driving risk of the mixed traffic scene based on the motor vehicle driving risk evaluation model, the non-motor vehicle driving risk evaluation model and the collision risk evaluation model interacted among other interference items.
2. The driving risk assessment method based on hybrid transportation according to claim 1, wherein in the step (3), the vehicle driving risk assessment model comprises a vehicle self-driving force, a repulsive force of other vehicles within a predetermined range, and a repulsive force of a road traffic environment within a predetermined range; due to self-driving force of motor vehicle
Figure FDA0002779162450000018
Influenced by disturbance factors during driving, for which purpose the desired speed of the vehicle a is determined
Figure FDA0002779162450000019
Is modified into
Figure FDA00027791624500000110
Figure FDA00027791624500000111
In the formula:
Figure FDA00027791624500000112
for the corrected desired speed, HsafetyTo a safety factor, LsafetyTo reflect the distance safely, dabIs the distance between motor vehicle a and motor vehicle b, raIs the radius of the motor vehicle a.
3. The hybrid transportation-based driving risk assessment method according to claim 2, wherein the disturbance factors include speed limit, visual field and vehicle condition of road environment, for which the actual speed is introduced
Figure FDA00027791624500000113
And corrected desired speed
Figure FDA00027791624500000114
The relational expression of (1):
Figure FDA00027791624500000115
Figure FDA0002779162450000021
in the formula: the actual speed being limited by the maximum speed of the individual
Figure FDA0002779162450000022
Wherein the maximum speed is the speed limit value of the current road environment, and when the speed limit value is greater than or equal to the speed limit value
Figure FDA0002779162450000023
When the individual advancing speed is
Figure FDA0002779162450000024
When the speed limit value is less than
Figure FDA0002779162450000025
When the individual is advancing at a speed of
Figure FDA0002779162450000026
The self-driving force of the motor vehicle
Figure FDA0002779162450000027
The correction is as follows:
Figure FDA0002779162450000028
in the formula: t iscIs the reaction time of the motor vehicle.
4. The hybrid transportation-based driving risk assessment method according to claim 2, wherein the repulsive force of other vehicles within the predetermined range comprises the vehicle b in the adjacent lane1Acting force on motor vehicle a
Figure FDA0002779162450000029
And motor vehicle b in current lane2Acting force on motor vehicle a
Figure FDA00027791624500000210
The motor vehicle b in the adjacent lane1Acting force of
Figure FDA00027791624500000211
The expression of (a) is as follows:
Figure FDA00027791624500000212
in the formula: u shapecThe scale factor of the magnitude of the repulsive force of other vehicles in the adjacent lane is shown,
Figure FDA00027791624500000213
showing motor vehicles a and b1The sum of the radii of (a) and (b),
Figure FDA00027791624500000214
showing motor vehicles a and b1Distance between, RcIndicating the sensitivity of the repulsion forces of other vehicles to distance,
Figure FDA00027791624500000215
indicating the direction from the centre of the vehicle a to the vehicle b1A unit vector of the center;
the motor vehicle b on the current lane2Acting force of
Figure FDA00027791624500000216
The expression of (a) is as follows:
Figure FDA00027791624500000217
Figure FDA00027791624500000218
in the formula: a ismaxRepresenting the maximum acceleration of the vehicle a, bcIndicating motor vehicle b2Deceleration of vaRepresenting the current speed of the motor vehicle a,
Figure FDA00027791624500000219
showing motor vehicles a and b2Speed difference of(s)*Representing the distance, s, of the motor vehicle after reaction0Indicating the minimum safe distance between the vehicles,
Figure FDA00027791624500000220
showing motor vehicles a and b2The distance between the two or more of the two or more,
Figure FDA00027791624500000221
a unit vector representing the direction of motion of the motor vehicle a;
if the motor vehicle changes lane, the force applied to the motor vehicle during lane change is increased
Figure FDA00027791624500000222
Figure FDA0002779162450000031
In the formula: u shapel cThe magnitude-proportional coefficient, R, representing the repulsive force of other vehicles during a lane changel cIndicating the sensitivity of the repulsion force of other vehicles to distance during a lane change, dalRepresenting the distance between the vehicle a and the target lane,
Figure FDA0002779162450000032
a unit vector representing the motor vehicle a pointing to the target lane.
5. The hybrid transportation-based driving risk assessment method according to claim 2, wherein the repulsive force of the road traffic environment within the predetermined range
Figure FDA0002779162450000033
Is related to road conditions, the worse the road conditions, the greater the likelihood of a collision; the roadThe road conditions include road surface adhesion coefficient muiRoad curvature ρiRoad grade τiAnd road visibility deltaiDefining the road condition influence factor of the preset position of the individual i as RiSaid R isiThe expression of (a) is as follows:
Figure FDA0002779162450000034
in the formula: gamma ray1、γ2、γ3、γ4Are all undetermined constants, and γ1、γ2Less than 0, gamma3、γ4Greater than 0, mu*Is a standard road surface adhesion coefficient, delta*For standard road visibility, p*For standard road curvature, τ*Is a standard road grade;
repulsive force of the improved road traffic environment
Figure FDA0002779162450000035
The expression of (a) is as follows:
Figure FDA0002779162450000036
Figure FDA0002779162450000037
in the formula: u'cMagnitude proportionality coefficient, d, representing road traffic environment repulsive forceasRepresenting the distance, R ', between the motor vehicle a and the lane boundary'cThe sensitivity coefficient of the road traffic environment repulsive force to the distance is shown,
Figure FDA0002779162450000038
is a unit vector pointing from the closest position to the vehicle on the lane boundary to the center of the vehicle a.
6. The driving risk assessment method based on hybrid transportation of claim 1, wherein in the step (3), the non-motor driving risk assessment model
Figure FDA0002779162450000039
Including direct mutual repulsion forces generated by the front vehicle avoiding collisions with other non-motor vehicles
Figure FDA00027791624500000310
And the acting force of the non-motor vehicle for realizing the effects of avoiding and exceeding
Figure FDA00027791624500000311
The expression is as follows:
Figure FDA00027791624500000312
Figure FDA00027791624500000313
in the formula: u shapebRepresenting the action strength of the non-motor vehicle repulsion force; b ismRepresenting the acting distance of the non-motor vehicle repulsion force;
Figure FDA00027791624500000314
a unit vector representing that the non-motor vehicle m points to the non-motor vehicle n; b represents the distance between non-motor vehicle m and non-motor vehicle n, and has:
Figure FDA00027791624500000315
in the formula: dmnRepresenting the distance of a central point connecting line between the non-motor vehicle m and the non-motor vehicle n; x is the number ofn、ynIndividual watchThe length of the short axis and the length of the long axis of the obstacle are shown; thetamnRepresenting the angle of the centre line between non-motor vehicle m and non-motor vehicle n.
7. A driving risk assessment method based on hybrid traffic as claimed in claim 6, wherein when said non-motor vehicle encounters an obstacle, the non-motor vehicle overtakes the obstacle by changing its direction, thus introducing a force to achieve the avoidance and overtaking effect, the magnitude of the force is set in proportion to the mutual repulsion force, the direction is perpendicular to the direction of the mutual repulsion force, and the expression is as follows:
Figure FDA0002779162450000041
in the formula: beta is amRepresenting the desired speed factor of the non-motor vehicle, the highest speed of the non-motor vehicle being greater than the desired speed when overriding, and thus betamGreater than 1;
Figure FDA0002779162450000042
representing a transcendental vector, the expression is as follows:
Figure FDA0002779162450000043
in the formula: w represents the transverse distance between the center points of the overtaking and overtaken non-motor vehicles at the target point;
Figure FDA0002779162450000044
representing a unit vector at a distance w from the vehicle m to the overrun side of vehicle n.
8. The hybrid transportation-based driving risk assessment method according to claim 6, wherein the method is characterized in that
Figure FDA0002779162450000045
And
Figure FDA0002779162450000046
dependent on the angle of view, thus introducing a direction-dependent weight ωmnThe expression is as follows:
Figure FDA0002779162450000047
in the formula: lambda [ alpha ]bIs a constant number of times, and is,
Figure FDA0002779162450000048
representing the included angle between the relative position vector of the non-motor vehicle m and the non-motor vehicle n and the advancing direction of the non-motor vehicle m;
improved non-motor vehicle collision risk assessment model
Figure FDA0002779162450000049
The expression of (a) is as follows:
Figure FDA00027791624500000410
9. the driving risk assessment method based on hybrid transportation according to claim 1, wherein in step (4), the collision risk assessment model interacted between different types of individuals mainly comprises a collision risk assessment model interacted between a motor vehicle and a non-motor vehicle.
10. The driving risk assessment method based on hybrid transportation of claim 9, wherein in the comprehensive collision risk assessment model of interaction between the motor vehicle and the non-motor vehicle, if there is no physical isolation between the motor vehicle lane and the non-motor vehicle lane, the driving risk assessment model of motor vehicle-non-motor vehicle hybrid transportation is established by modifying the single individual model; the modification process of the motor vehicle-non-motor vehicle mixed traffic driving risk assessment model is as follows:
for a motor vehicle, if there is a non-motor vehicle in the side view, the motor vehicle will be subjected to a side force:
Figure FDA0002779162450000051
in the formula:
Figure FDA0002779162450000052
and
Figure FDA0002779162450000053
is a constant coefficient, r represents the distance between the vehicle and the non-vehicle, and if there is a non-vehicle in front of the lane,
Figure FDA0002779162450000054
representing a direction vector of the non-motor vehicle pointing to the motor vehicle;
the vehicle is subjected to a force of deceleration:
Figure FDA0002779162450000055
sa＝s1+vaΔt
wherein s isaThe distance, s, between the vehicle and the non-vehicle after the vehicle reaction1For minimum safety distance between motor vehicle and non-motor vehicle, vaIs the speed of the motor vehicle, at is the reaction time, d is the distance between the motor vehicle and the non-motor vehicle,
Figure FDA0002779162450000056
a unit vector of a current individual velocity direction;
for non-motor vehicles, if there is a motor vehicle in the side view, the non-motor vehicle will be subjected to a side force:
Figure FDA0002779162450000057
wherein the content of the first and second substances,
Figure FDA0002779162450000058
and
Figure FDA0002779162450000059
is a constant coefficient of the number of the optical fiber,
Figure FDA00027791624500000510
the speed of the motor vehicle is the same as the speed of the motor vehicle,
Figure FDA00027791624500000511
if the motor vehicle exists in front of the non-motor vehicle, the non-motor vehicle is subjected to the action force of deceleration:
Figure FDA00027791624500000512
sn＝s1+vnΔt
wherein s isnIs the distance between the non-motor vehicle and the motor vehicle after reaction, b'cIndicating deceleration of non-motor vehicles, vnIs the speed of the non-motor vehicle;
when the non-motor vehicle enters the motor vehicle lane, the non-motor vehicle needs to return to the non-motor vehicle lane: the driving force experienced by the non-motor vehicle is expressed as:
Figure FDA00027791624500000513
in the formula:
Figure FDA00027791624500000514
and
Figure FDA00027791624500000515
is a constant coefficient of r3Indicating the lateral distance between the non-motor vehicle and the rear motor vehicle,
Figure FDA00027791624500000516
a unit vector representing a unit vector pointing from the center of the non-motor vehicle to a nearest position of the non-motor vehicle on the lane of the non-motor vehicle;
in the motor vehicle-non-motor vehicle hybrid traffic risk assessment model, the social force of the motor vehicle a at the moment t is as follows:
Figure FDA00027791624500000517
the social forces experienced by the non-motor vehicle n at time t are:
Figure FDA00027791624500000518
CN202011276318.4A 2020-11-16 2020-11-16 Driving risk assessment method based on hybrid traffic Active CN112581756B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011276318.4A CN112581756B (en) 2020-11-16 2020-11-16 Driving risk assessment method based on hybrid traffic

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011276318.4A CN112581756B (en) 2020-11-16 2020-11-16 Driving risk assessment method based on hybrid traffic

Publications (2)

Publication Number Publication Date
CN112581756A true CN112581756A (en) 2021-03-30
CN112581756B CN112581756B (en) 2021-12-24

Family

ID=75122622

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011276318.4A Active CN112581756B (en) 2020-11-16 2020-11-16 Driving risk assessment method based on hybrid traffic

Country Status (1)

Country Link
CN (1) CN112581756B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113920699A (en) * 2021-11-26 2022-01-11 交通运输部公路科学研究所 Vehicle risk early warning method, road side control unit and risk early warning control system
CN114035569A (en) * 2021-11-09 2022-02-11 中国民航大学 Path expansion passing method for manned robot in airport terminal

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090306946A1 (en) * 2008-04-08 2009-12-10 Norman I Badler Methods and systems for simulation and representation of agents in a high-density autonomous crowd
CN108389430A (en) * 2018-01-12 2018-08-10 南京理工大学 A kind of intersection pedestrian based on video detection and collision of motor-driven vehicles prediction technique
CN109992886A (en) * 2019-04-01 2019-07-09 浙江大学 A kind of mixed traffic emulation mode based on social force
CN110414831A (en) * 2019-07-24 2019-11-05 清华大学 People's bus or train route coupling methods of risk assessment and device based on driver's Cognitive Perspective
CN111033510A (en) * 2017-09-26 2020-04-17 奥迪股份公司 Method and device for operating a driver assistance system, driver assistance system and motor vehicle
CN111767648A (en) * 2020-06-19 2020-10-13 浙江大学 Hybrid traffic simulation method based on simplified social force calculation model

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090306946A1 (en) * 2008-04-08 2009-12-10 Norman I Badler Methods and systems for simulation and representation of agents in a high-density autonomous crowd
CN111033510A (en) * 2017-09-26 2020-04-17 奥迪股份公司 Method and device for operating a driver assistance system, driver assistance system and motor vehicle
CN108389430A (en) * 2018-01-12 2018-08-10 南京理工大学 A kind of intersection pedestrian based on video detection and collision of motor-driven vehicles prediction technique
CN109992886A (en) * 2019-04-01 2019-07-09 浙江大学 A kind of mixed traffic emulation mode based on social force
CN110414831A (en) * 2019-07-24 2019-11-05 清华大学 People's bus or train route coupling methods of risk assessment and device based on driver's Cognitive Perspective
CN111767648A (en) * 2020-06-19 2020-10-13 浙江大学 Hybrid traffic simulation method based on simplified social force calculation model

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
HELBING D等: "Social force model for pedestrian dynamics", 《PHYSICAL REVIEW E》 *
何赞: "基于社会力模型的信号交叉口人车混合冲突行为仿真研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》 *
叶倩倩: "基于社会力的人车混合交通模拟", 《中国优秀硕士学位论文全文数据库（电子期刊）》 *
庞明宝等: "道路交织路段交通流优化控制仿真研究", 《计算机仿真》 *
李成利等: "混合交通流中公交车对其他车辆的影响", 《内蒙古公路与运输》 *
李珊珊: "平交路口机动车自行车行人及其相互干扰微观行为模型研究", 《中国博士学位论文全文数据库（电子期刊）》 *
王占中等: "信号交叉口自行车和行人混合交通流社会力模型", 《吉林大学学报》 *
陈大飞: "基于社会力的城市道路混合交通流建模与分析", 《中国优秀硕士学位论文全文数据库（电子期刊）》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114035569A (en) * 2021-11-09 2022-02-11 中国民航大学 Path expansion passing method for manned robot in airport terminal
CN113920699A (en) * 2021-11-26 2022-01-11 交通运输部公路科学研究所 Vehicle risk early warning method, road side control unit and risk early warning control system

Also Published As

Publication number Publication date
CN112581756B (en) 2021-12-24

Similar Documents

Publication Publication Date Title
CN109709956B (en) Multi-objective optimized following algorithm for controlling speed of automatic driving vehicle
CN108313054B (en) The autonomous lane-change decision-making technique of automatic Pilot and device and automatic driving vehicle
EP3533681A2 (en) Method for detecting safety of driving behavior, apparatus and storage medium
CN112581756B (en) Driving risk assessment method based on hybrid traffic
US20040176936A1 (en) Road traffic simulation apparatus
CN112896170A (en) Automatic driving transverse control method under vehicle-road cooperative environment
CN106491144B (en) A kind of test and evaluation method of the latent risk perceptions ability of driver
CN114626285A (en) Trajectory evaluation method, system and computer readable medium
Gürbüz et al. Improvement of safe stopping distance and accident risk coefficient based on active driver sight field on real road conditions
Sarvi et al. Freeway ramp merging phenomena in congested traffic using simulation combined with a driving simulator
Das et al. Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning
Matthews A field study of the effects of drivers' adaptation to automobile velocity
Cheng et al. A vehicle passing model in non-signalized intersections based on non-cooperative game theory
Toledo et al. Alternative definitions of passing critical gaps
CN113592221A (en) Road section risk dynamic evaluation method based on safety substitution evaluation index
Park et al. Development of complexity index and predictions of accident risks for mixed autonomous driving levels
Xue et al. Analysis of reaction time during car-following process based on driving simulation test
Xu et al. The influence of freeway curve characteristics on drivers’ speed perception accuracy
Olstam Simulation of vehicles in a driving simulator using microscopic traffic simulation
Fan et al. Role of vehicle trajectory and lateral acceleration in designing horizontal curve radius of off-ramp: a driving simulator based study.
Félez et al. A full driving simulator of urban traffic including traffic accidents
Gaetano et al. Drivers’ workload measures to verify functionality of ferry boats boarding area
CN117746639B (en) Background traffic flow model construction method and system based on automatic driving
Gruszczyński et al. Research into the effects of changes in drivers’ driving style on vehicle movement parameters
CN116090108A (en) Safety evaluation method and system for automatic driving of expressway vehicle

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant