11 citations · 15 across the 6 of their papers we have counts for
4 papers · 1 filter
Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning
Liu Weiwei, Hu Wenxuan, Jing Wei +3
Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained pol…
Long-term Microscopic Traffic Simulation with History-Masked Multi-agent Imitation Learning
Ke Guo, Wei Jing, Lingping Gao +3
A realistic long-term microscopic traffic simulator is necessary for understanding how microscopic changes affect traffic patterns at a larger scale. Traditional simulators that mo…
CCIL: Context-conditioned imitation learning for urban driving
Ke Guo, Wei Jing, Junbo Chen +1
Imitation learning holds great promise for addressing the complex task of autonomous urban driving, as experienced human drivers can navigate highly challenging scenarios with ease…
Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow
Dongkun Zhang, Jintao Xue, Yuxiang Cui +6
Acquiring driving policies that can transfer to unseen environments is challenging when driving in dense traffic flows. The design of traffic flow is essential and previous studies…