A Cooperative Control Framework for CAV Lane Change in a Mixed Traffic Environment
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Abstract (arXiv)
In preparing for connected and autonomous vehicles (CAVs), a worrisome aspect is the transition era which will be characterized by mixed traffic (where CAVs and human-driven vehicles (HDVs) share the roadway). Consistent with expectations that CAVs will improve road safety, on-road CAVs may adopt rather conservative control policies, and this will likely cause HDVs to unduly exploit CAV conservativeness by driving in ways that imperil safety. A context of this situation is lane-changing by the CAV. Without cooperation from other vehicles in the traffic stream, it can be extremely unsafe for the CAV to change lanes under dense, high-speed traffic conditions. The cooperation of neighboring vehicles is indispensable. To address this issue, this paper develops a control framework where connected HDVs and CAV can cooperate to facilitate safe and efficient lane changing by the CAV. Throughout the lane-change process, the safety of not only the CAV but also of all neighboring vehicles, is ensured through a collision avoidance mechanism in the control framework. The overall traffic flow efficiency is analyzed in terms of the ambient level of CHDV-CAV cooperation. The analysis outcomes are including the CAVs lane-change feasibility, the overall duration of the lane change. Lane change is a major source of traffic disturbance at multi-lane highways that impair their traffic flow efficiency. In providing a control framework for lane change in mixed traffic, this study shows how CHDV-CAV cooperation could help enhancing system efficiency.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Oct 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 27
- 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Trafficcited by 20
- 2023Evaluation of Infrastructure-based Warning System on Driving Behaviors-A Roundabout Studycited by 1
- 2021Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecturecited by 0
- 2023Evaluation of Infrastructure-based Warning System on Driving Behaviors-A Roundabout Study6 Dec 2023
- 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications16 Jun 2022
- 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic11 Nov 2021
- 2021Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture11 Oct 2021