10 papers
Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation
Mingxing Peng, Xusen Guo, Long Chen +3
Reliable safety evaluation of autonomous vehicles (AVs) is essential to improving road safety, yet it depends critically on realistic simulation of rare crashes. Existing crash sce…
SCORP: Scene-Consistent Multi-agent Diffusion Planning with Stable Online Reinforcement Post-Training for Cooperative Driving
Haojie Bai, Aimin Li, Ruoyu Yao +5
Cooperative driving is a safety- and efficiency-critical task that requires the coordination of diverse, interaction-realistic multi-agent trajectories. Although existing diffusion…
Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning
Zhezhi Lei, Zhihai Bi, Wenxin Wang +1
Collaborative transportation, where multiple robots collaboratively transport a payload, has garnered significant attention in recent years. While ensuring safe and high-performanc…
DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving
Rui Yang, Lei Zheng, Ruoyu Yao +1
Diffusion models have emerged as a powerful approach for multimodal motion planning in autonomous driving. However, their practical deployment is typically hindered by the inherent…
Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future
Tianshuai Hu, Xiaolu Liu, Song Wang +17
Autonomous driving has long relied on modular "Perception-Decision-Action" pipelines, where hand-crafted interfaces and rule-based components often break down in complex or long-ta…
nuPlan-R: A Closed-Loop Planning Benchmark for Autonomous Driving via Reactive Multi-Agent Simulation
Mingxing Peng, Ruoyu Yao, Xusen Guo +1
Recent advances in closed-loop planning benchmarks have significantly improved the evaluation of autonomous vehicles. However, existing benchmarks still rely on rule-based reactive…
CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving
Ruiguo Zhong, Ruoyu Yao, Pei Liu +3
Accurate trajectory prediction and motion planning are crucial for autonomous driving systems to navigate safely in complex, interactive environments characterized by multimodal un…
MGTraj: Multi-Granularity Goal-Guided Human Trajectory Prediction with Recursive Refinement Network
Ge Sun, Jun Ma
Accurate human trajectory prediction is crucial for robotics navigation and autonomous driving. Recent research has demonstrated that incorporating goal guidance significantly enha…
LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios
Mingxing Peng, Yuting Xie, Xusen Guo +3
Ensuring the safety and robustness of autonomous driving systems necessitates a comprehensive evaluation in safety-critical scenarios. However, these safety-critical scenarios are…
Safety-Critical Traffic Simulation with Guided Latent Diffusion Model
Mingxing Peng, Ruoyu Yao, Xusen Guo +3
Safety-critical traffic simulation plays a crucial role in evaluating autonomous driving systems under rare and challenging scenarios. However, existing approaches often generate u…