10 papers
Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models
Lei Zheng, Peiqi Yu, Zengqi Peng +2
Diffusion models excel at generating diverse and multimodal trajectories for robotic planning, yet their iterative denoising process introduces latency that is incompatible with hi…
Occlusion-Aware Contingency Safety-Critical Planning for Autonomous Driving
Lei Zheng, Rui Yang, Minzhe Zheng +3
Ensuring safe driving while maintaining travel efficiency for autonomous vehicles in dynamic and occluded environments is a critical challenge. This paper proposes an occlusion-awa…
SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model
Xiao Zhou, Zengqi Peng, Jun Ma
Accurate trajectory prediction of surrounding vehicles (SVs) is crucial for autonomous driving systems to avoid misguided decisions and potential accidents. However, achieving reli…
Orchestrate, Generate, Reflect: A VLM-Based Multi-Agent Collaboration Framework for Automated Driving Policy Learning
Zengqi Peng, Yusen Xie, Yubin Wang +3
The advancement of foundation models fosters new initiatives for policy learning in achieving safe and efficient autonomous driving. However, a critical bottleneck lies in the manu…
SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
Zewei Yang, Zengqi Peng, Jun Ma
Autonomous parking is a critical component for achieving safe and efficient urban autonomous driving. However, unstructured environments and dynamic interactions pose significant c…
DECAMP: Towards Scene-Consistent Multi-Agent Motion Prediction with Disentangled Context-Aware Pre-Training
Jianxin Shi, Zengqi Peng, Xiaolong Chen +2
Trajectory prediction is a critical component of autonomous driving, essential for ensuring both safety and efficiency on the road. However, traditional approaches often struggle w…