Towards Co-operative Congestion Mitigation
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Abstract (arXiv)
The effects of traffic congestion are widespread and are an impedance to everyday life. Piecewise constant driving policies have shown promise in helping mitigate traffic congestion in simulation environments. However, no works currently test these policies in situations involving real human users. Thus, we propose to evaluate these policies through the use of a shared control framework in a collaborative experiment with the human driver and the driving policy aiming to co-operatively mitigate congestion. We intend to use the CARLA simulator alongside the Flow framework to conduct user studies to evaluate the affect of piecewise constant driving policies. As such, we present our in-progress work in building our framework and discuss our proposed plan on evaluating this framework through a human-in-the-loop simulation user study.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 16 references that are in the map. First posted on arXiv 17 Feb 2023.
Builds on
3 of 16 references.
References · 3
- 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
- 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 67
- 2022RILI: Robustly Influencing Latent Intentcited by 8
Built on by
Citing papers per year: 2023: 2; 2024: 1.
3 papers.
- 2023Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomycited by 6
- 2023PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systemscited by 4
- 2024Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Studycited by 0
- 2024Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards Real-Time Advisory Systems from a Driving Simulator User Study29 Jun 2024
- 2023Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy27 Nov 2023
- 2023PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems1 Aug 2023
Similar, not cited
Close in wording, no citation either way
- 2021Scalable Multiagent Driving Policies For Reducing Traffic Congestion
- 2023Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments
- 2024CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control
- 2023Reinforcement Learning with Human Feedback for Realistic Traffic Simulation
- 2025A Human-Oriented Cooperative Driving Approach: Integrating Driving Intention, State, and Conflict
- 2021Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction
- 2026CommonRoad-Game: A Human-in-the-Loop Simulation Framework for Autonomous Driving
- 2025RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulation