13 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…
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…
Relative Position Matters: Trajectory Prediction and Planning with Polar Representation
Bozhou Zhang, Nan Song, Bingzhao Gao +1
Trajectory prediction and planning in autonomous driving are highly challenging due to the complexity of predicting surrounding agents' movements and planning the ego agent's actio…
Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving
Bozhou Zhang, Jingyu Li, Nan Song +1
End-to-end autonomous driving has achieved remarkable advancements in recent years. Existing methods primarily follow a perception-planning paradigm, where perception and planning…
DeMo++: Motion Decoupling for Autonomous Driving
Bozhou Zhang, Nan Song, Xiatian Zhu +1
Motion forecasting and planning are tasked with estimating the trajectories of traffic agents and the ego vehicle, respectively, to ensure the safety and efficiency of autonomous d…
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…
Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning
Bozhou Zhang, Nan Song, Xin Jin +1
End-to-end autonomous driving unifies tasks in a differentiable framework, enabling planning-oriented optimization and attracting growing attention. Current methods aggregate histo…
Motion Forecasting in Continuous Driving
Nan Song, Bozhou Zhang, Xiatian Zhu +1
Motion forecasting for agents in autonomous driving is highly challenging due to the numerous possibilities for each agent's next action and their complex interactions in space and…
DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States
Bozhou Zhang, Nan Song, Li Zhang
Accurate motion forecasting for traffic agents is crucial for ensuring the safety and efficiency of autonomous driving systems in dynamically changing environments. Mainstream meth…
DeepInteraction++: Multi-Modality Interaction for Autonomous Driving
Zeyu Yang, Nan Song, Wei Li +3
Existing top-performance autonomous driving systems typically rely on the multi-modal fusion strategy for reliable scene understanding. This design is however fundamentally restric…