11 papers
See Tomorrow, Act Today: Foresight-Driven Autonomous Driving
Bozhou Zhang, Nan Song, Yuang Wang +3
Current end-to-end autonomous driving planners are fundamentally reactive: they condition on historical and present observations to predict future actions. We argue that autonomous…
PanguMotion: Continuous Driving Motion Forecasting with Pangu Transformers
Quanhao Ren, Yicheng Li, Nan Song
Motion forecasting is a core task in autonomous driving systems, aiming to accurately predict the future trajectories of surrounding agents to ensure driving safety. Existing metho…
UniMotion: A Unified Motion Framework for Simulation, Prediction and Planning
Nan Song, Junzhe Jiang, Jingyu Li +2
Motion simulation, prediction and planning are foundational tasks in autonomous driving, each essential for modeling and reasoning about dynamic traffic scenarios. While often addr…
RealEngine: Simulating Autonomous Driving in Realistic Context
Junzhe Jiang, Nan Song, Jingyu Li +2
Driving simulation plays a crucial role in developing reliable driving agents by providing controlled, evaluative environments. To enable meaningful assessments, a high-quality dri…
Future-Aware End-to-End Driving: Bidirectional Modeling of Trajectory Planning and Scene Evolution
Bozhou Zhang, Nan Song, Jingyu Li +3
End-to-end autonomous driving methods aim to directly map raw sensor inputs to future driving actions such as planned trajectories, bypassing traditional modular pipelines. While t…
LMAD: Integrated End-to-End Vision-Language Model for Explainable Autonomous Driving
Nan Song, Bozhou Zhang, Xiatian Zhu +2
Large vision-language models (VLMs) have shown promising capabilities in scene understanding, enhancing the explainability of driving behaviors and interactivity with users. Existi…