CN111968372B - Multi-vehicle type mixed traffic following behavior simulation method considering subjective factors - Google Patents
Multi-vehicle type mixed traffic following behavior simulation method considering subjective factors Download PDFInfo
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- CN111968372B CN111968372B CN202010864385.1A CN202010864385A CN111968372B CN 111968372 B CN111968372 B CN 111968372B CN 202010864385 A CN202010864385 A CN 202010864385A CN 111968372 B CN111968372 B CN 111968372B
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- 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
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- 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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention discloses a multi-vehicle type mixed traffic following behavior simulation method considering subjective factors, which is characterized in that the theoretical safe driving following distance between a leading vehicle and a following vehicle is corrected based on vehicle type differences to obtain the safe driving following distance considering vehicle type factors; based on the style of a following vehicle driver, correcting the safe driving following distance considering vehicle type factors to obtain a psychological safe driving following distance; acquiring the speed of the following vehicle in the next step based on the relative distance between the following vehicle and the leading vehicle, the psychological safety following distance and the relationship between the speed of the following vehicle and the expected speed of the driver; the styles are the sensitivity coefficient and the risk coefficient of the driver. The invention utilizes the relative distance between the front guide vehicle and the following vehicle and the psychological safety following distance to judge and calculate the longitudinal speed of the following vehicle at the next moment, thereby realizing the accurate depiction of the following behavior under the condition of mixed traffic of multiple vehicle types. The model can describe the following behaviors of different vehicle types and different driver styles, and provides reference for the management of multi-vehicle type mixed traffic.
Description
Technical Field
The invention relates to the technical field of intelligent traffic information, in particular to a multi-vehicle type mixed traffic following behavior simulation method considering subjective factors.
Background
Due to the characteristics of large volume, poor performance and the like, when the freight vehicle is mixed with other rapid vehicles, the traffic efficiency is very easy to be obviously reduced. With the rapid development of domestic economy in recent years, the number of freight vehicles is continuously increased, and the phenomenon that low-speed trucks influence the express way traffic efficiency is increasingly serious. The influence of vehicle type difference on the driving behaviors of drivers with different styles is considered, the following behavior of multi-vehicle type mixed traffic is reproduced, traffic management and control can be reasonably performed by a traffic management department, and the traffic efficiency of the multi-vehicle type mixed traffic is improved.
However, the influence of vehicle type difference on drivers of different styles is not considered in the existing following model, so that the existing following model has poor description on the following behavior under the multi-vehicle type mixed traffic environment. Actual data show that in mixed traffic composed of a minibus and a large truck, the type of a leading vehicle can influence a driver, and when the leading vehicle is the large truck, the driver can consider safety factors more and enlarge the following distance; meanwhile, the influence varies from person to person, and the subjective level influence increases difficulty in describing the following behavior under the mixed traffic environment of multiple vehicle types. The existing following model mainly considers the relation between the acceleration and the speed of the front vehicle and the rear vehicle, and the description of the following behavior is not in accordance with the reality under the mixed traffic environment.
Patent CN 106407563 a provides a following model generation method based on driving type and front vehicle acceleration information, and a clustering data mining method is used to divide the driving style of the driver according to actual data, introduce personal expected effect on the basis of a full speed difference model, and further consider the influence of the front vehicle acceleration information on the following behavior to obtain a vehicle following model. However, the method does not consider the influence of the front vehicle type on the driver of the rear vehicle, and has poor description effect on the following behavior under the condition of multi-vehicle type mixed traffic. In other literature researches, the influence of the front vehicle type on the following behavior of the rear vehicle driver is not considered, and the following behavior change caused by the driver style is only considered, so that the following behavior description effect under the condition of multi-vehicle type mixed traffic is poor.
Disclosure of Invention
In view of this, the present invention provides a multi-vehicle hybrid traffic following behavior simulation method considering subjective factors, which can describe following behaviors of different vehicles and different driver styles and provide a reference for managing multi-vehicle hybrid traffic.
The purpose of the invention is realized by the following technical scheme:
a multi-vehicle type mixed traffic following behavior simulation method considering subjective factors,
based on the vehicle type difference, correcting the theoretical safe driving following distance between the leading vehicle and the following vehicle to obtain the safe driving following distance considering the vehicle type factor;
based on the style of a following vehicle driver, correcting the safe driving following distance considering vehicle type factors to obtain a psychological safe driving following distance;
acquiring the speed of the following vehicle in the next step based on the relative distance between the following vehicle and the leading vehicle, the psychological safety following distance and the relationship between the speed of the following vehicle and the expected speed of the driver;
the styles are the sensitivity coefficient and the risk coefficient of the driver.
Further, the obtaining mode of the theoretical safe driving following distance specifically comprises the following steps:
wherein: v. of0The speed of the following vehicle at the current moment is obtained;
t0the reaction time of the following vehicle driver;
amaxthe maximum deceleration of the following vehicle;
t1the running time is the deceleration change process of the following vehicle;
t2a travel time that is a constant deceleration process of the following vehicle;
v1the speed of the leading vehicle at the current moment;
l1the length of the front guide vehicle;
d3the minimum safe distance between two workshops;
amis the maximum deceleration of the leading vehicle.
Further, the safe driving following distance considering the vehicle type factor is specifically as follows:
wherein: and f is a vehicle model influence parameter.
Further, the method for acquiring the vehicle type influence parameters comprises the following steps:
wherein: t is a unit oftypeIs a front vehicle type influence factor;
α1、α2、α3the correction coefficients are obtained under different following vehicle types.
Further, the psychological safety following distance specifically comprises:
wherein: g is a first correction coefficient;
gamma is the coefficient of sensitivity;
m is a second correction coefficient.
Further, the method for acquiring the sensitivity coefficient comprises the following steps:
γ＝(t1+t2)/(mt1)
wherein: t is t1The running time for the deceleration variation process of the following vehicle,
t2the running time is a constant deceleration process of the following vehicle.
Further, the first correction coefficient is obtained by:
G＝a0+a1cos(A·W)+b1sin(A·W)
wherein A is the risk coefficient;
W、a0、a1、b1are all undetermined coefficients.
Further, the next step of obtaining the speed of the vehicle is as follows:
wherein: h is a total ofrealIs the relative distance between the following vehicle and the leading vehicle, veFor the driver to expect the vehicle speed, aAFor acceleration of following vehicle, aDTo decelerate the car following.
The invention has the beneficial effects that:
according to the invention, based on the safe distance following model, the influence of vehicle type factors and driver styles is considered, the psychological safe following distance is calculated through the speeds of the leading vehicle and the following vehicle, and the calculation result is closer to the reality. Meanwhile, the relative distance between the front guide vehicle and the following vehicle and the psychological safety following distance are used for judging, and the longitudinal speed of the following vehicle at the next moment is calculated, so that the following behavior under the condition of multi-vehicle type mixed traffic can be accurately depicted. The model can describe the following behaviors of different vehicle types and different driver styles, and provides reference for the management of multi-vehicle type mixed traffic.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic illustration of a description scenario of the present invention;
fig. 3 is a schematic diagram of deceleration as a function of time in calculation of the theoretical safe distance of the present invention.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are only for illustrating the present invention, and are not intended to limit the scope of the present invention.
The embodiment provides a multi-vehicle type hybrid traffic following behavior simulation method considering subjective factors, wherein scene descriptions are shown in fig. 1 and 2, and specifically include:
and based on the vehicle type difference, correcting the theoretical safe driving following distance between the leading vehicle and the following vehicle to obtain the safe driving following distance considering the vehicle type factor.
The theoretical safe driving following distance of the front guide vehicle and the following vehicle is calculated based on a Kometani safe distance following model, and specifically comprises the following steps: suppose that the preceding vehicle suddenly decelerates a at a maximummBraking, the driver of the rear vehicle realizes the change of the front vehicle after t0Reaction time of (3), rapid braking, deceleration past t1Increase to a maximum value amaxTime t elapsed3And the vehicle stops and keeps the minimum safe distance with the front vehicle.
First, according to FIG. 2, the following vehicle reaction time t is calculated0Inner driving distance d1The formula is as follows:
d1＝v0t0
wherein:
v0the speed of the following vehicle at the current moment is obtained;
t0the reaction time of the following vehicle driver is shown.
Secondly, the running distance d of the following vehicle during braking is calculated2The formula is as follows:
wherein:
t1the running time of the deceleration change process of the following vehicle;
t2the running time is the deceleration constant process of the following vehicle;
amaxis the maximum deceleration of the following vehicle.
Thirdly, the distance d traveled during the braking of the lead vehicle is calculated4The formula is as follows
Wherein:
v1the speed of the leading vehicle at the current moment;
amis the maximum deceleration of the leading vehicle.
Step 14: the theoretical safe following distance h is calculated by the following formula
Wherein:
l1is the length of the front guide vehicle;
d3is the minimum safe distance between two vehicles.
Based on the style of a following vehicle driver, correcting the safe driving following distance considering the vehicle type factor to obtain the psychological safe driving following distance, wherein the style is the sensitivity coefficient and risk factor of the driver, namely the psychological factors determined according to the self psychological quality of the driver. The method specifically comprises the following steps:
firstly, determining vehicle type influence parameters, specifically calculating according to the following formula:
wherein: t is a unit oftypeIs a front vehicle type influence factor;
α1、α2、α3the correction coefficients are obtained under different following vehicle types.
Secondly, based on the vehicle type influence parameters, obtaining the safe driving following distance considering the vehicle type factors, which is specifically as follows:
wherein: and f is a vehicle model influence parameter.
Based on the relative distance between the following vehicle and the leading vehicle, the psychological safety following distance, the vehicle speed of the following vehicle and the expected vehicle speed of the driver, the speed of the next vehicle is obtained, which is specifically as follows:
firstly, a driver risk coefficient A is introduced, and a first correction coefficient G is calculated, wherein the formula is as follows:
G＝a0+a1cos(A·W)+b1sin(A·W)
wherein A is an adventure coefficient;
W、a0、a1、b1are all undetermined coefficients.
Secondly, a driver sensitivity coefficient gamma is introduced, and the formula is as follows
γ＝(t1+t2)/(mt1)
Wherein:
t2the running time is a constant deceleration process of the following vehicle.
m is a second correction coefficient.
Thirdly, according to fig. 3, the travel time t during which the deceleration of the following vehicle changes is calculated1。
v0＝amaxt2+0.5amaxt1
Fourthly, acquiring the safe driving distance in mind according to the steps, which specifically comprises the following steps:
based on the relative distance between the following vehicle and the leading vehicle, the psychological safety following distance, the vehicle speed of the following vehicle and the expected vehicle speed of the driver, the speed of the next vehicle is obtained, which is specifically as follows:
wherein: h is a total ofrealIs the relative distance between the following vehicle and the leading vehicle, veFor the driver to expect the vehicle speed, aATo follow the acceleration of the vehicle, aDTo decelerate the car following.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (6)
1. A multi-vehicle type mixed traffic following behavior simulation method considering subjective factors is characterized in that:
based on the vehicle type difference, the theoretical safe driving following distance between the leading vehicle and the following vehicle is corrected to obtain the safe driving following distance considering the vehicle type factors,
the safe driving following distance considering the vehicle type factors is specifically as follows:
wherein: v. of0In order to follow the speed of the vehicle at the current moment,
t0in order to follow the reaction time of the driver of the vehicle,
amaxfor the maximum deceleration of the following vehicle,
t1the running time for the deceleration variation process of the following vehicle,
t2for the running time of the constant deceleration process of the following vehicle,
v1is the speed of the leading vehicle at the current moment,
l1the length of the vehicle is the length of the front vehicle,
d3is the minimum safe distance between two workshops,
amis the maximum deceleration of the leading vehicle,
f is a vehicle model influence parameter;
based on the sensitivity coefficient and the risk factor of a following vehicle driver, the safe driving following distance considering the vehicle type factor is corrected to obtain the psychological safe driving following distance,
the psychological safety driving following distance specifically comprises the following steps:
wherein: g is a first correction coefficient, and G is a second correction coefficient,
gamma is the coefficient of sensitivity of the light source,
m is a second correction coefficient;
and acquiring the speed of the following vehicle in the next step based on the relative distance between the following vehicle and the guide vehicle, the psychological safety driving following distance and the relationship between the speed of the following vehicle and the expected speed of the driver.
2. The multi-vehicle type hybrid traffic following behavior simulation method considering subjective factors as claimed in claim 1, wherein: the method for acquiring the theoretical safe driving following distance specifically comprises the following steps:
3. the multi-vehicle type hybrid traffic following behavior simulation method considering subjective factors as claimed in claim 1, wherein: the method for acquiring the vehicle type influence parameters comprises the following steps:
wherein: t istypeIs a front vehicle type influence factor;
α1、α2、α3the correction coefficients are obtained under different following vehicle types.
4. The multi-vehicle type hybrid traffic following behavior simulation method considering subjective factors as claimed in claim 1, wherein: the method for acquiring the sensitivity coefficient comprises the following steps:
γ＝(t1+t2)/(mt1)
wherein: t is t1For changing deceleration of following vehicleThe travel time of the journey is determined,
5. the multi-vehicle type hybrid traffic following behavior simulation method considering subjective factors as claimed in claim 1, wherein: the first correction coefficient is obtained in the following manner:
G＝a0+a1cos(A·W)+b1sin(A·W)
wherein A is the risk coefficient;
W、a0、a1、b1are all undetermined coefficients.
6. The multi-vehicle type hybrid traffic following behavior simulation method considering subjective factors as claimed in claim 1, wherein: the next driving speed obtaining mode specifically comprises the following steps:
wherein: h isrealIs the relative distance between the following vehicle and the leading vehicle, veFor the driver to expect the vehicle speed, aATo follow the acceleration of the vehicle, aDTo decelerate the car following.
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Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112668172B (en) * | 2020-12-24 | 2023-02-28 | 西南交通大学 | Following behavior modeling method considering heterogeneity of vehicle type and driving style and model thereof |
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Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102010006063A1 (en) * | 2010-01-28 | 2010-09-16 | Daimler Ag | Method for avoiding too small distance between following vehicle and preceding vehicle i.e. car, involves providing optical warning signal to following vehicle from preceding vehicle in form of alphanumeric indications |
| CN102622516A (en) * | 2012-02-22 | 2012-08-01 | 天津港(集团)有限公司 | Microcosmic traffic flow simulation method for road safety evaluation |
| CN102662320A (en) * | 2012-03-05 | 2012-09-12 | 吴建平 | Car-following simulation method based on fuzzy mathematics |
| CN102991498A (en) * | 2011-12-19 | 2013-03-27 | 王晓原 | Driver following behavior model based on multi-source information fusion |
| WO2014098653A1 (en) * | 2012-12-19 | 2014-06-26 | Volvo Truck Corporation | Method and arrangement for determining the speed behaviour of a leading vehicle |
| CN106407563A (en) * | 2016-09-20 | 2017-02-15 | 北京工业大学 | A car following model generating method based on driving types and preceding vehicle acceleration speed information |
| CN106803226A (en) * | 2017-01-23 | 2017-06-06 | 长安大学 | Consider the vehicle follow gallop modeling method of optimal velocity memory and backsight effect |
| CN106991806A (en) * | 2017-05-05 | 2017-07-28 | 同济大学 | Low visibility highway leader of a group of people's passing method |
| CN107016193A (en) * | 2017-04-06 | 2017-08-04 | 中国科学院自动化研究所 | Driver is with the expectation following distance computational methods in car behavioural analysis |
| CN107103749A (en) * | 2017-05-19 | 2017-08-29 | 长安大学 | With traffic stream characteristics modeling method of speeding under car networking environment |
| CN107452201A (en) * | 2017-07-24 | 2017-12-08 | 重庆大学 | Rear car determines method and with speeding on as modeling method with acceleration of speeding when a kind of consideration front truck lane-change is sailed out of |
| CN107507408A (en) * | 2017-07-24 | 2017-12-22 | 重庆大学 | It is a kind of consider front truck lane-change import process with the acceleration and with speeding on as modeling method of speeding |
| CN107554524A (en) * | 2017-09-12 | 2018-01-09 | 北京航空航天大学 | A kind of following-speed model stability control method based on subjective dangerous criminal |
| WO2018026733A1 (en) * | 2016-07-31 | 2018-02-08 | Netradyne Inc. | Determining causation of traffic events and encouraging good driving behavior |
| CN108573600A (en) * | 2017-03-10 | 2018-09-25 | 重庆邮电大学 | A kind of induction of driving behavior and the flow-optimized method of local traffic |
| CN108595823A (en) * | 2018-04-20 | 2018-09-28 | 大连理工大学 | A kind of computational methods of Autonomous Vehicles lane-change strategy that combining driving style and theory of games |
| CN108848462A (en) * | 2018-06-19 | 2018-11-20 | 连云港杰瑞电子有限公司 | Real-time vehicle trajectory predictions method suitable for signalized crossing |
| CN108860148A (en) * | 2018-06-13 | 2018-11-23 | 吉林大学 | Self-adapting cruise control method based on driver's follow the bus characteristic Safety distance model |
| CN109242251A (en) * | 2018-08-03 | 2019-01-18 | 百度在线网络技术（北京）有限公司 | Vehicular behavior safety detecting method, device, equipment and storage medium |
| CN110033617A (en) * | 2019-04-19 | 2019-07-19 | 中国汽车工程研究院股份有限公司 | A kind of train tracing model assessment system and method towards natural driving data |
| CN110299004A (en) * | 2019-07-31 | 2019-10-01 | 山东理工大学 | The following-speed model of intersection turning vehicle is established and its method for analyzing stability |
| CN110517486A (en) * | 2019-08-16 | 2019-11-29 | 东南大学 | A kind of forward direction anti-collision warning method based on driving behavior state |
| CN110516746A (en) * | 2019-08-29 | 2019-11-29 | 吉林大学 | A kind of driver's follow the bus behavior genre classification method based on no label data |
| WO2020000191A1 (en) * | 2018-06-26 | 2020-01-02 | Psa Automobiles Sa | Method for driver identification based on car following modeling |
| CN110750877A (en) * | 2019-09-27 | 2020-02-04 | 西安理工大学 | Method for predicting car following behavior under Apollo platform |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102007045960B3 (en) * | 2007-09-26 | 2009-04-16 | Daimler Ag | Method and device for warning subsequent vehicles in frontal escalating longitudinal traffic |
| US8280560B2 (en) * | 2008-07-24 | 2012-10-02 | GM Global Technology Operations LLC | Adaptive vehicle control system with driving style recognition based on headway distance |
| CN101935969B (en) * | 2010-09-10 | 2012-01-11 | 天津市市政工程设计研究院 | Harbor road longitudinal gradient design method based on cellular automaton |
| US10347127B2 (en) * | 2013-02-21 | 2019-07-09 | Waymo Llc | Driving mode adjustment |
| US9412276B2 (en) * | 2014-07-18 | 2016-08-09 | Tsun-Huang Lin | Following distance reminding device and method thereof |
| CN106157608B (en) * | 2015-03-23 | 2019-09-13 | 高德软件有限公司 | Information processing method and device |
| US10429842B2 (en) * | 2017-07-10 | 2019-10-01 | Toyota Research Institute, Inc. | Providing user assistance in a vehicle based on traffic behavior models |
| US10766489B2 (en) * | 2017-09-05 | 2020-09-08 | Arizona Board Of Regents On Behalf Of Arizona State University | Model predictive adaptive cruise control for reducing rear-end collision risk with follower vehicles |
| CN107993453B (en) * | 2017-12-28 | 2020-04-21 | 武汉理工大学 | Method for calculating safe speed of curve based on vehicle-road cooperation |
| CN111338385A (en) * | 2020-01-22 | 2020-06-26 | 北京工业大学 | Vehicle following method based on fusion of GRU network model and Gipps model |
-
2020
- 2020-08-25 CN CN202010864385.1A patent/CN111968372B/en active Active
Patent Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102010006063A1 (en) * | 2010-01-28 | 2010-09-16 | Daimler Ag | Method for avoiding too small distance between following vehicle and preceding vehicle i.e. car, involves providing optical warning signal to following vehicle from preceding vehicle in form of alphanumeric indications |
| CN102991498A (en) * | 2011-12-19 | 2013-03-27 | 王晓原 | Driver following behavior model based on multi-source information fusion |
| CN102622516A (en) * | 2012-02-22 | 2012-08-01 | 天津港(集团)有限公司 | Microcosmic traffic flow simulation method for road safety evaluation |
| CN102662320A (en) * | 2012-03-05 | 2012-09-12 | 吴建平 | Car-following simulation method based on fuzzy mathematics |
| WO2014098653A1 (en) * | 2012-12-19 | 2014-06-26 | Volvo Truck Corporation | Method and arrangement for determining the speed behaviour of a leading vehicle |
| WO2018026733A1 (en) * | 2016-07-31 | 2018-02-08 | Netradyne Inc. | Determining causation of traffic events and encouraging good driving behavior |
| CN106407563A (en) * | 2016-09-20 | 2017-02-15 | 北京工业大学 | A car following model generating method based on driving types and preceding vehicle acceleration speed information |
| CN106803226A (en) * | 2017-01-23 | 2017-06-06 | 长安大学 | Consider the vehicle follow gallop modeling method of optimal velocity memory and backsight effect |
| CN108573600A (en) * | 2017-03-10 | 2018-09-25 | 重庆邮电大学 | A kind of induction of driving behavior and the flow-optimized method of local traffic |
| CN107016193A (en) * | 2017-04-06 | 2017-08-04 | 中国科学院自动化研究所 | Driver is with the expectation following distance computational methods in car behavioural analysis |
| CN106991806A (en) * | 2017-05-05 | 2017-07-28 | 同济大学 | Low visibility highway leader of a group of people's passing method |
| CN107103749A (en) * | 2017-05-19 | 2017-08-29 | 长安大学 | With traffic stream characteristics modeling method of speeding under car networking environment |
| CN107452201A (en) * | 2017-07-24 | 2017-12-08 | 重庆大学 | Rear car determines method and with speeding on as modeling method with acceleration of speeding when a kind of consideration front truck lane-change is sailed out of |
| CN107507408A (en) * | 2017-07-24 | 2017-12-22 | 重庆大学 | It is a kind of consider front truck lane-change import process with the acceleration and with speeding on as modeling method of speeding |
| CN107554524A (en) * | 2017-09-12 | 2018-01-09 | 北京航空航天大学 | A kind of following-speed model stability control method based on subjective dangerous criminal |
| CN108595823A (en) * | 2018-04-20 | 2018-09-28 | 大连理工大学 | A kind of computational methods of Autonomous Vehicles lane-change strategy that combining driving style and theory of games |
| CN108860148A (en) * | 2018-06-13 | 2018-11-23 | 吉林大学 | Self-adapting cruise control method based on driver's follow the bus characteristic Safety distance model |
| CN108848462A (en) * | 2018-06-19 | 2018-11-20 | 连云港杰瑞电子有限公司 | Real-time vehicle trajectory predictions method suitable for signalized crossing |
| WO2020000191A1 (en) * | 2018-06-26 | 2020-01-02 | Psa Automobiles Sa | Method for driver identification based on car following modeling |
| CN109242251A (en) * | 2018-08-03 | 2019-01-18 | 百度在线网络技术（北京）有限公司 | Vehicular behavior safety detecting method, device, equipment and storage medium |
| CN110033617A (en) * | 2019-04-19 | 2019-07-19 | 中国汽车工程研究院股份有限公司 | A kind of train tracing model assessment system and method towards natural driving data |
| CN110299004A (en) * | 2019-07-31 | 2019-10-01 | 山东理工大学 | The following-speed model of intersection turning vehicle is established and its method for analyzing stability |
| CN110517486A (en) * | 2019-08-16 | 2019-11-29 | 东南大学 | A kind of forward direction anti-collision warning method based on driving behavior state |
| CN110516746A (en) * | 2019-08-29 | 2019-11-29 | 吉林大学 | A kind of driver's follow the bus behavior genre classification method based on no label data |
| CN110750877A (en) * | 2019-09-27 | 2020-02-04 | 西安理工大学 | Method for predicting car following behavior under Apollo platform |
Non-Patent Citations (11)
| Title |
|---|
| Car-Following Behavior of Coach Bus Based on Naturalistic Driving Experiments in Urban Roads;Zuo Wang;《2019 IEEE International Symposium on Circuits and Systems (ISCAS)》;20190529;1-4 * |
| CPS环境下基于驾驶行为的交通拥堵特征及抑制方法研究;周桐;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20141215;C034-92 * |
| Driving Style Classification for Vehicle-Following with Unlabeled Naturalistic Driving Data;Xinjie Zhang;《2019 IEEE Vehicle Power and Propulsion Conference (VPPC)》;20200109;1-5 * |
| 基于元胞自动机的快速路仿真建模与交通流优化分析;狄宣;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20080815;C034-255 * |
| 基于多前车位置及速度差信息的车辆跟驰模型;孙棣华;《系统工程理论与实践》;20101115;1326-1332 * |
| 基于期望车速的跟驰模型研究;吕贞;《交通运输工程与信息学报》;20100915;68-73 * |
| 基于模糊逻辑的高速公路微观换道行为;聂琳真;《北京工业大学学报》;20171220;424-432 * |
| 基于车辆互联的道路基本路段交通流特性研究;蔡明;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20180315;C034-914 * |
| 混有CACC车辆和ACC车辆的异质交通流基本图模型;秦严严;《中国公路学报》;20171015;127-136 * |
| 考虑前后车辆综合效应的跟驰模型及其稳定性分析;孙棣华;《哈尔滨工业大学学报》;20140228;115-120 * |
| 车辆换道行为对交通流影响分析;王茉莉;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20160915;C034-128 * |
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