This paper comprehensively reviews recent developments in modeling lane-changing behavior. The major lane changing models in the literature are categorized into two groups: models that aim to capture the lane changing decision-making process, and models that aim to quantify the impact of lane changing behavior on surrounding vehicles. The methodologies and important features (including their limitations) of representative models in each category are outlined and discussed. Future research needs are determined.

Recent developments and research needs in modeling lane changing

A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling.

To address the safety and efficiency issues of vehicles at multi-lane merging zones, a cooperative decision-making framework is designed for connected automated vehicles (CAVs) using a coalitional game approach. Firstly, a motion prediction module is established based on the simplified single-track vehicle model for enhancing the accuracy and reliability of the decision-making algorithm. Then, the cost function and constraints of the decision making are designed considering multiple performance indexes, i.e. the safety, comfort and efficiency. Besides, in order to realize human-like and personalized smart mobility, different driving characteristics are considered and embedded in the modeling process. Furthermore, four typical coalition models are defined for CAVS at the scenario of a multi-lane merging zone. Then, the coalitional game approach is formulated with model predictive control (MPC) to deal with decision making of CAVs at the defined scenario. Finally, testings are carried out in two cases considering different driving characteristics to evaluate the performance of the developed approach. The testing results show that the proposed coalitional game based method is able to make reasonable decisions and adapt to different driving characteristics for CAVs at the multi-lane merging zone. It guarantees the safety and efficiency of CAVs at the complex dynamic traffic condition, and simultaneously accommodates the objectives of individual vehicles, demonstrating the feasibility and effectiveness of the proposed approach.

Cooperative Decision Making of Connected Automated Vehicles at Multi-Lane Merging Zone: A Coalitional Game Approach

This paper presents a cooperative traffic control strategy to increase the capacity of nonrecurrent bottlenecks such as work zones by making full use of the spatial resources upstream of w...

/pdf/a-platoon-regulation-algorithm-to-improve-the-traffic-19j3ckskhx.pdf

A Platoon Regulation Algorithm to Improve the Traffic Performance of Highway Work Zones

The goal of driver assistance systems is to monitor the driver's decision making and intervene where deemed necessary. In order to support the driver's subsequent driving manoeuvres, knowledge of the driver's intentions is required. Eye movements have been found to be an indicator of information gathering, and therefore could be used to derive information about the driver's next planned objective. The aim of this study was to establish whether characteristic eye movement patterns precede particular driving manoeuvres. For this purpose, drivers' gaze behaviour was analysed prior to the execution of different driving manoeuvres performed in real traffic using the DLR ViewCar. It was found that different driving manoeuvres are preceded by different gaze patterns.

Do eye movements reflect driving manoeuvres

Vehicle merging is one of the main causes of reduced traffic efficiency, increased risk of collision, and fuel consumption. Connected and automated vehicles (CAVs) can improve traffic efficiency, increase safety, and reduce the negative environmental impacts through effective communication and control. Therefore, to improve the traffic efficiency and reduce the fuel consumption in on-ramp scenarios, this paper addresses the global and optimal coordination of the CAVs in a merging zone. Herein, a cooperative multi-player game-based optimization framework and an algorithm are presented to coordinate vehicles and achieve minimum values for the global pay-off conditions. Fuel consumption, passenger comfort, and travel time within the merging control zone were used as the pay-off conditions. After analyzing the characteristics of the merging control zone and selecting the appropriate control decision duration, multi-player games were decomposed into multiple two-player games. An optimal merging strategy was, thereby, derived from a pay-off matrix, and minimum payoffs were predicted for a number of different potential strategies. The optimal trajectory corresponding to the predicted minimum payoffs was then utilized as the control law to coordinate the vehicles merging. The proposed control scheme derives an optimal merging sequence and an optimal trajectory for each vehicle. The effectiveness of the proposed model is validated through simulation. The proposed controller is compared with two alternative methods to demonstrate its potential to reduce fuel consumption and travel time and to improve passenger comfort and traffic efficiency.

Cooperative Game Approach to Optimal Merging Sequence and on-Ramp Merging Control of Connected and Automated Vehicles

Dedicated short-range communication (DSRC) technology can provide drivers with information about other vehicles that are beyond the normal range of vision and enables the development of driving support systems such as the rear-end collision warning system (ReCWS). However, technology constraints such as communication delays and GPS error affect the accuracy of a DSRC-based ReCWS. This paper proposes a ReCWS design that explicitly represents functional specifications of DSRC technology, including transmission delay specifications that describe the information transmission process and an error-component safety distance specification used to represent the effect of GPS error and the information propagation delay. We propose three collision warning strategies each with different deceleration requirements. The system is assembled with off-the-shelf DSRC and mobile technology that can be readily installed into test vehicles. To test the effectiveness of the proposed ReCWS, we ran a variety of controlled scenarios on a test track. The results show a high degree of warning accuracy. These field test results also provide calibrated system parameter values for future studies and designs of DSRC-based ReCWSs.

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DSRC-based rear-end collision warning system – An error-component safety distance model and field test

Abnormal driving behaviours, such as rapid acceleration, emergency braking, and rapid lane changing, bring great uncertainty to traffic, and can easily lead to traffic accidents. The accurate identification of abnormal driving behaviour helps to judge the driver's driving style, inform surrounding vehicles, and ensure the road traffic safety. Most of the existing studies use clustering and shallow learning, it is difficult to accurately identify the types of abnormal driving behaviours. Aimed at addressing the difficulty of identifying driving behaviour, this study proposed a recognition model based on a long short-term memory network and convolutional neural network (LSTM-CNN). The extreme acceleration and deceleration points are detected through the statistical analysis of real vehicle driving data, and the driving behaviour recognition data set is established. By using the data set to train the model, the LSTM-CNN can achieve a better result.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2019.0200

Long short-term memory and convolutional neural network for abnormal driving behaviour recognition

A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.

/pdf/driver-lane-changing-behavior-prediction-based-on-deep-2e8r0hc1jy.pdf

Driver Lane-Changing Behavior Prediction Based on Deep Learning

Weaving sections may cause massive congestion and accident problems. Connected and automated vehicles (CAVs) are acknowledged to improve traffic safety and efficiency through effective communicatio...

Fei Hui

Papers

Cooperative Game Approach to Optimal Merging Sequence and on-Ramp Merging Control of Connected and Automated Vehicles

DSRC-based rear-end collision warning system – An error-component safety distance model and field test

Long short-term memory and convolutional neural network for abnormal driving behaviour recognition

Driver Lane-Changing Behavior Prediction Based on Deep Learning

Cooperative CAVs optimal trajectory planning for collision avoidance and merging in the weaving section