14 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…
Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving
Qiqi Liu, Huan Xu, Jingyu Li +5
Autonomous driving requires reasoning about how the environment evolves and planning actions accordingly. Existing world-model-based approaches typically predict future scenes firs…
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…
SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving
Jingyu Li, Junjie Wu, Dongnan Hu +6
Recent end-to-end autonomous driving approaches have leveraged Vision-Language Models (VLMs) to enhance planning capabilities in complex driving scenarios. However, VLMs are inhere…
ScenePilot-4K: A Large-Scale First-Person Dataset and Benchmark for Vision-Language Models in Autonomous Driving
Yujin Wang, Yutong Zheng, Wenxian Fan +7
In this paper, we introduce ScenePilot-4K, a large-scale first-person dataset for safety-aware vision-language learning and evaluation in autonomous driving. Built from public onli…
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…
ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving
Jingyu Li, Bozhou Zhang, Xin Jin +3
Autonomous driving requires rich contextual comprehension and precise predictive reasoning to navigate dynamic and complex environments safely. Vision-Language Models (VLMs) and Dr…
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…
UniScene: Unified Occupancy-centric Driving Scene Generation
Bohan Li, Jiazhe Guo, Hongsi Liu +14
Generating high-fidelity, controllable, and annotated training data is critical for autonomous driving. Existing methods typically generate a single data form directly from a coars…
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…