Analysis of vehicle lane-changing behaviour at signalised intersection

2020 ◽  
Vol 15 (4) ◽  
pp. 379
Author(s):  
Yan Xing ◽  
Zhe Zhang ◽  
Weidong Liu ◽  
Qi Zhan ◽  
Chaojun Chu
Author(s):  
Chaojun Chu ◽  
Qi Zhan ◽  
Weidong Liu ◽  
Zhe Zhang ◽  
Yan Xing

2016 ◽  
Vol 13 (2) ◽  
pp. 39
Author(s):  
Jezan Md Diah ◽  
Li Sian Tey ◽  
Fathiyah Roslee

In recent years, lane changing has become a crucial issue in traffic engineering and safety aspect due to distribution of vehicles across lanes thus contributing to traffic movements. In order to keep the right route, drivers have to change their lanes. However, lane changing has a high potential of accidents, especially at signalised intersection. This may cause the traffic flow to become heavier and traffic congestion. The aims of this study are to study on lane changing issue at signalised intersection, to determine the factors contributing to lane changing at signalised intersection and to develop a model for improvement of traffic flow in lane changing behaviour at signalised intersection. Lane changing model is important because it will reduce traffic congestion and smoothen the traffic. This study will contribute in studying the changing lane issue at signalised intersection which is to control the flow of traffic in order to ensure the traffic flows smoothly and to reduce traffic congestion especially on the merging issue at signalised intersection. Keywords: lane changing, signalised intersection, model, traffic flow


PAMM ◽  
2007 ◽  
Vol 7 (1) ◽  
pp. 2150029-2150030 ◽  
Author(s):  
Amiruddin Ismail ◽  
Shahrum Abdullah ◽  
Azami Zaharim ◽  
Ibrahim Ahmad

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Shiqiang Cheng ◽  
Liyang Wei ◽  
Xuelan Ma ◽  
Jianfeng Shen ◽  
Jian Wang
Keyword(s):  

2015 ◽  
Vol 8 (3) ◽  
pp. 184-194 ◽  
Author(s):  
Ronghui Zhang ◽  
Fuliang Li ◽  
Xuecai Yu ◽  
Zhonghua Zhang ◽  
Feng You ◽  
...  

Author(s):  
Li Zhao ◽  
Laurence Rilett ◽  
Mm Shakiul Haque

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.


Actuators ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 173
Author(s):  
Hongbo Wang ◽  
Shihan Xu ◽  
Longze Deng

Traffic accidents are often caused by improper lane changes. Although the safety of lane-changing has attracted extensive attention in the vehicle and traffic fields, there are few studies considering the lateral comfort of vehicle users in lane-changing decision-making. Lane-changing decision-making by single-step dynamic game with incomplete information and path planning based on Bézier curve are proposed in this paper to coordinate vehicle lane-changing performance from safety payoff, velocity payoff, and comfort payoff. First, the lane-changing safety distance which is improved by collecting lane-changing data through simulated driving, and lane-changing time obtained by Bézier curve path planning are introduced into the game payoff, so that the selection of the lane-changing start time considers the vehicle safety, power performance and passenger comfort of the lane-changing process. Second, the lane-changing path without collision to the forward vehicle is obtained through the constrained Bézier curve, and the Bézier curve is further constrained to obtain a smoother lane-changing path. The path tracking sliding mode controller of front wheel angle compensation by radical basis function neural network is designed. Finally, the model in the loop simulation and the hardware in the loop experiment are carried out to verify the advantages of the proposed method. The results of three lane-changing conditions designed in the hardware in the loop experiment show that the vehicle safety, power performance, and passenger comfort of the vehicle controlled by the proposed method are better than that of human drivers in discretionary lane change and mandatory lane change scenarios.


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