A Cooperative Lane Change Model for Connected and Automated Vehicles

Li, Tingting; Wu, Jianping; Chan, Ching-Yao; Li, Mingyu; Zhu, Chunli; Lu, Weixin; Hu, Kezhen

doi:10.1109/access.2020.2981169

Cited by 45 publications

(15 citation statements)

References 40 publications

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“…In this subsection, we evaluate the effectiveness of the proposed multi-objective reward function with the driving comfort in Eqn. (3). Figure 5 shows the acceleration and deceleration of the AV with or without the comfort measurement defined in Eqn.…”

Section: Verification Of Driving Comfortmentioning

confidence: 99%

“…However, there exists a huge challenge in the task of high-level decision-making in AVs due to the complex and dynamic traffic environment, especially in mixed traffic co-existing with other road users. Lane changing is one of the largest challenges in the high-level decision-making of AVs, which has significant influences on traffic safety and efficiency [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic

Zhou

Chen

Yan

et al. 2022

Auton. Intell. Syst.

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Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving and mitigating traffic congestion, among others. Despite promising progress, lane-changing remains a great challenge for autonomous vehicles (AV), especially in mixed and dynamic traffic scenarios. Recently, reinforcement learning (RL) has been widely explored for lane-changing decision makings in AVs with encouraging results demonstrated. However, the majority of those studies are focused on a single-vehicle setting, and lane-changing in the context of multiple AVs coexisting with human-driven vehicles (HDVs) have received scarce attention. In this paper, we formulate the lane-changing decision-making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs. Specifically, a multi-agent advantage actor-critic (MA2C) method is proposed with a novel local reward design and a parameter sharing scheme. In particular, a multi-objective reward function is designed to incorporate fuel efficiency, driving comfort, and the safety of autonomous driving. A comprehensive experimental study is made that our proposed MARL framework consistently outperforms several state-of-the-art benchmarks in terms of efficiency, safety, and driver comfort.

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Section: Verification Of Driving Comfortmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic

Zhou

Chen

Yan

et al. 2022

Auton. Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Studies [10], [11], [12] demonstrated wireless V2V communication protocols for cooperative maneuvering systems for autonomous vehicles to enable them to perform complex road maneuvers through message exchange. The authors of [13] and [14] proposed a Cooperative Lane Changing solutions for AVs based on V2V and V2I connections to improve motion planning and thus improve traffic operations. The study in [15] investigated cooperative vehicle parking system via V2V wireless communication to allow vehicles to find and select parking spaces.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Cooperative Health Emergency Response System in Autonomous Vehicles

Elayan¹,

Aloqaily

Guizani

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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Recent technological advances have reshaped many aspects of our lives, especially modern transportation systems. For instance, AI and B5G Networks have raised the level of automation as autonomous vehicles (AV) become decisionindependent and self-aware. However, in-vehicle health monitoring is still an open issue. Therefore, a cooperative healthcare emergency response framework has been proposed that employs in-vehicle intelligent health monitoring and local networks for AV to minimize the time to receive emergency treatment for passengers with abnormal health conditions. The extensive simulation results show that the framework minimizes the First Emergency Treatment Time by at least 75%, and eliminates hospital waiting time, the Total Time for Emergency Treatment is minimized by at least 93%. Finally, it reduces Travel Time by nearly 50%. All results compared to the autopilot approach.

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“…In the mixed traffic, there are few studies on the influence of lane-changing behavior on traffic flow, and more researches focus on the study of lane-changing behavior of autonomous vehicles. e research studies on lanechanging behavior of autonomous vehicles mainly include lane-changing intention [24,25], lane-changing decision [26,27], cooperative lane-changing between front and rear vehicles [28][29][30][31], and lane-changing position [32]. Research studies use human behavior as the research basis to construct human-like behavior models.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Relationship between the Density and Lane-Changing Behavior of Circular Multilane Urban Expressway in Mixed Traffic

Xie

Zhu

Huawei

2022

Journal of Advanced Transportation

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The behavior of changing lanes has a great impact on road traffic with heavy traffic. Traffic flow density is one of the important parameters that characterize the characteristics of traffic flow, and it will also be affected by the behavior of changing lanes, especially in the case of each lane. The penetration of autonomous vehicles can effectively reduce lane-changing behavior. Studying the relationship between traffic flow density and lane-changing behavior under different autonomous vehicle penetration rates is of great significance for describing the operation mechanism of mixed traffic flow and the control of mixed traffic. In this article, we use empirical, simulation, and data-driven methods to analyze the urban expressway of autonomous vehicles with penetration rates of 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation experiment was carried out on the road, and data related to density, the rate of changing into the lanes, and the rate of changing out lanes were collected. The analysis of the experimental results found the following: (1) The increase in penetration of autonomous vehicles leads to a certain degree of downward trend in density, the rate of changing into the lanes, and the rate of changing out lanes. (2) Different lanes have different effects on the penetration of autonomous vehicles. In a 4-lane road, the two lanes farther from the entrance and exit are closer in appearance, while the two lanes closer to the entrance and exit are similar. (3) The relationship between density and the rate of changing into the lanes and the rate of changing out lanes shows a linear relationship with the penetration of autonomous vehicles. Although the performance of each lane is slightly different, in general, it can be carried out by a multiple regression model. The given parameter value range is relatively close under different permeability. In summary, autonomous vehicles effectively reduce the traffic density and lane-changing behavior of each lane. There is a linear relationship between traffic flow density and lane-changing behavior with the penetration of autonomous vehicles. The density-lane-changing behavior model proposed in this paper can better describe the relationship between the density of the circular multilane urban expressway and the lane-changing behavior in the case of a large traffic flow in mixed traffic.

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A Cooperative Lane Change Model for Connected and Automated Vehicles

Cited by 45 publications

References 40 publications

Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic

Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic

Intelligent Cooperative Health Emergency Response System in Autonomous Vehicles

Analysis of the Relationship between the Density and Lane-Changing Behavior of Circular Multilane Urban Expressway in Mixed Traffic

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