58 citations · 82 across the 6 of their papers we have counts for
6 papers
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
Vindula Jayawardana, Baptiste Freydt, Ao Qu +3
Despite the popularity of multi-agent reinforcement learning (RL) in simulated and two-player applications, its success in messy real-world applications has been limited. A key cha…
A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets
Cameron Hickert, Zhongxia Yan, Cathy Wu
Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work conside…
Multi-agent Path Finding for Mixed Autonomy Traffic Coordination
Han Zheng, Zhongxia Yan, Cathy Wu
In the evolving landscape of urban mobility, the prospective integration of Connected and Automated Vehicles (CAVs) with Human-Driven Vehicles (HDVs) presents a complex array of ch…
Multi-agent Path Finding for Cooperative Autonomous Driving
Zhongxia Yan, Han Zheng, Cathy Wu
Anticipating possible future deployment of connected and automated vehicles (CAVs), cooperative autonomous driving at intersections has been studied by many works in control theory…
Unified Automatic Control of Vehicular Systems with Reinforcement Learning
Zhongxia Yan, Abdul Rahman Kreidieh, Eugene Vinitsky +2
Emerging vehicular systems with increasing proportions of automated components present opportunities for optimal control to mitigate congestion and increase efficiency. There has b…
Reinforcement Learning for Mixed Autonomy Intersections
Zhongxia Yan, Cathy Wu
We propose a model-free reinforcement learning method for controlling mixed autonomy traffic in simulated traffic networks with through-traffic-only two-way and four-way intersecti…