World Models for Autonomous Driving

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Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Embodiment Adaptation
Date pendingZhidong Wang, Jingsong Liang, Zirui Li +3World Model Learning End-to-End Autonomous Driving
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore · Collaborative Initiative, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore

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Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Embodiment Adaptation

Zhidong Wang, Jingsong Liang, Zirui Li, Zhan Chen+2

School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore · Collaborative Initiative, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore

World Model Learning End-to-End Autonomous Driving Vehicle Dynamics World Models for Autonomous Driving Cross-Embodiment Transfer Model-Based RL

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End-to-end autonomous driving requires generalization ability across platforms with dissimilar physical characteristics. The chassis defines the physical embodiment of each platform, and real-world fleets span sub-tonne microcars to bus-class vehicles. Consequently, the driving stack must either be retrained per platform or adapt to the underlying chassis dynamics online. World model (WM)-based reinforcement learning offers a sample-efficient path toward end-to-end autonomous driving on egocentric bird's-eye-view (BEV) representations, but its effectiveness hinges on how faithfully the WM captures the ego vehicle's dynamics. This work identifies a structural bottleneck in BEV-based WMs: observation transitions entangle ego-motion with scene dynamics, consuming modeling capacity at the cost of imagination accuracy. This burden is embodiment-dependent: dissimilar chassis produce different observation warps under the same control input. The proposed DynaDreamer addresses this bottleneck by conditioning the WM's latent distributions on a physics-informed ego-dynamics context derived from a lateral dynamics model with a neural tire force formulation. This context is extracted online via a neural-ODE encoder-decoder that simultaneously identifies the underlying chassis parameters. Information-theoretic analysis confirms that this conditioning removes the ego-motion terms from both the WM's transition entropy and its prior-posterior KL divergence. The identified physical parameterization enables zero-shot cross-embodiment adaptation across a dynamically diverse fleet without per-platform retraining. Simulation results show 28% and 43% improvements in driving task success rates over the strongest baseline in urban and highway scenarios, and the advantage over the base Transformer WM reaches up to 73% when extrapolating to unseen chassis.

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Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Embodiment Adaptation