An extended two-lane car-following model accounting for inter-vehicle communication

Ou, Hui; Tang, Tie-Qiao

doi:10.1016/j.physa.2017.12.100

Cited by 142 publications

(30 citation statements)

References 52 publications

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“…In [17], Forbes studied the car-following behaviour by considering the reaction time needed for the follower to decelerate and apply the brakes. In [18], Hui et al developed a car-following model considering Inter-Vehicle Communication (IVC) to examine each of the vehicle movement and to better identify and hence, adjust the driving behavior in a two-lane traffic system when an incident occurs on a lane. In [11], the authors developed a path-following algorithm for AVs.…”

Section: Literature Reviewmentioning

confidence: 99%

A Reinforcement Learning Framework for Video Frame-Based Autonomous Car-Following

Masmoudi

Friji

Ghazzai

et al. 2021

IEEE Open J. Intell. Transp. Syst.

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Car-following theory has received considerable attention as a core component of Intelligent Transportation Systems. However, its application to the emerging autonomous vehicles (AVs) remains an unexplored research area. AVs are designed to provide convenient and safe driving by avoiding accidents caused by human errors. They require advanced levels of recognition of other drivers' driving-style. With car-following models, AVs can use their built-in technology to understand the environment surrounding them and make real-time decisions to follow other vehicles. In this paper, we design an end-to-end carfollowing framework for AVs using automated object detection and navigation decision modules. The objective is to allow an AV to follow another vehicle based on Red Green Blue Depth (RGB-D) frames. We propose to employ a joint solution involving the You Look Once version 3 (YOLOv3) object detector to identify the leader vehicle and other obstacles and a reinforcement learning (RL) algorithm to navigate the self-driving vehicle. Two RL algorithms, namely Q-learning and Deep Q-learning have been investigated. Simulation results show the convergence of the developed models and investigate their efficiency in following the leader. It is shown that, with video frames only, promising results are achieved and that AVs can adopt a reasonable car-following behavior.

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Section: Literature Reviewmentioning

confidence: 99%

A Reinforcement Learning Framework for Video Frame-Based Autonomous Car-Following

Masmoudi

Friji

Ghazzai

et al. 2021

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

“…Inter-vehicle communication [71][72][73][74] is helpful to improve the security of public traffic, increase driving efficiency and enhance the view of on-board devices. Thus, it has attracted attention from academical researchers and companies, especially in the United States, European Union and Japan.…”

Section: Ad Hoc Domainmentioning

confidence: 99%

A Survey on Secure Computation Based on Homomorphic Encryption in Vehicular Ad Hoc Networks

Sun

Zhang

et al. 2020

Sensors

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In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of data dissemination and computation. However, encrypted vehicle data cannot be analyzed easily and flexibly. Because homomorphic encryption supports computations of the ciphertext, it can completely solve this problem. In this paper, we provide a comprehensive survey of secure computation based on homomorphic encryption in VANETs. We first describe the related definitions and the current state of homomorphic encryption. Next, we present the framework, communication domains, wireless access technologies and cyber-security issues of VANETs. Then, we describe the state of the art of secure basic operations, data aggregation, data query and other data computation in VANETs. Finally, several challenges and open issues are discussed for future research.

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“…However, by the progress of intelligent transportation systems (ITS), especially for vehicle communication and automatic cruise control (ACC) [25], a vehicle can acquire more information from infrastructures and other vehicles than before, so as to adjust the driving condition. Based on that, some scholars have tried to utilize these messages to build a more adaptable car-following model [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Tang et al found that under the guidance of the forecast information produced by ITS, traffic flow stability would be improved [26].…”

Section: Introductionmentioning

confidence: 99%

A Modified Car-following Model Considering Traffic Density and Acceleration of Leading Vehicle

2020

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Although the difference between the velocity of two successive vehicles is considered in the full velocity difference model (FVDM), more status information from preceding vehicles affecting the behavior of car-following has not been effectively utilized. For improving the performance of the FVDM, an extended modified car-following model taking into account traffic density and the acceleration of a leading vehicle (DAVD, density and acceleration velocity difference model) is presented under the condition of vehicle-to-vehicle (V2V) communications. Stability in the developed model is derived through applying linear stability theory. The curves of neutral stability for the improved model indicate that when the driver pays more attention to the traffic status in front, the traffic flow stability region is larger. Numerical simulation illustrates that traffic flow disturbance could be suppressed by gaining more information on preceding vehicles.

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An extended two-lane car-following model accounting for inter-vehicle communication

Cited by 142 publications

References 52 publications

A Reinforcement Learning Framework for Video Frame-Based Autonomous Car-Following

A Reinforcement Learning Framework for Video Frame-Based Autonomous Car-Following

A Survey on Secure Computation Based on Homomorphic Encryption in Vehicular Ad Hoc Networks

A Modified Car-following Model Considering Traffic Density and Acceleration of Leading Vehicle

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