2 citations · 2 across the 4 of their papers we have counts for
5 papers
DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning
Qian Hu, Haoyang Peng, Songan Zhang +2
Traffic signal control at urban intersections inherently introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency, especially under high traffi…
ROAD: Responsibility-Oriented Reward Design for Reinforcement Learning in Autonomous Driving
Yongming Chen, Miner Chen, Liewen Liao +5
Reinforcement learning (RL) in autonomous driving employs a trial-and-error mechanism, enhancing robustness in unpredictable environments. However, crafting effective reward functi…
Prospective Role of Foundation Models in Advancing Autonomous Vehicles
Jianhua Wu, Bingzhao Gao, Jincheng Gao +8
With the development of artificial intelligence and breakthroughs in deep learning, large-scale Foundation Models (FMs), such as GPT, Sora, etc., have achieved remarkable results i…
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan Paleja, Letian Chen, Yaru Niu +10
Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…
Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions
Siddharth H. Nair, Hotae Lee, Eunhyek Joa +3
We propose a Stochastic MPC (SMPC) formulation for path planning with autonomous vehicles in scenarios involving multiple agents with multi-modal predictions. The multi-modal predi…