6 papers
OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models
Yiwei Zhang, Xuesong Chen, Jin Gao +5
Vision-Language Models(VLMs) excel at autoregressive text generation, yet end-to-end autonomous driving requires multi-task learning with structured outputs and heterogeneous decod…
Advancing Multi-agent Traffic Simulation via R1-Style Reinforcement Fine-Tuning
Muleilan Pei, Shaoshuai Shi, Shaojie Shen
Scalable and realistic simulation of multi-agent traffic behavior is critical for advancing autonomous driving technologies. Although existing data-driven simulators have made sign…
ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving
Qihang Peng, Xuesong Chen, Chenye Yang +2
Autonomous driving requires generating safe and reliable trajectories from complex multimodal inputs. Traditional modular pipelines separate perception, prediction, and planning, w…
Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics
Muleilan Pei, Shaoshuai Shi, Xuesong Chen +2
Motion forecasting for on-road traffic agents presents both a significant challenge and a critical necessity for ensuring safety in autonomous driving systems. In contrast to most…
TrajFlow: Multi-modal Motion Prediction via Flow Matching
Qi Yan, Brian Zhang, Yutong Zhang +8
Efficient and accurate motion prediction is crucial for ensuring safety and informed decision-making in autonomous driving, particularly under dynamic real-world conditions that ne…
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
Muleilan Pei, Shaoshuai Shi, Lu Zhang +2
Trajectory prediction for surrounding agents is a challenging task in autonomous driving due to its inherent uncertainty and underlying multimodality. Unlike prevailing data-driven…