ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2510.08562v1
• Date: Thu, 09 Oct 2025 17:59:36 GMT
• Title: ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
• Authors: Zhiyu Zheng, Shaoyu Chen, Haoran Yin, Xinbang Zhang, Jialv Zou, Xinggang Wang, Qian Zhang, Lefei Zhang,
• Abstract summary: ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
• Score: 64.42138266293202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: End-to-end autonomous driving (E2EAD) systems, which learn to predict future trajectories directly from sensor data, are fundamentally challenged by the inherent spatio-temporal imbalance of trajectory data. This imbalance creates a significant optimization burden, causing models to learn spurious correlations instead of causal inference, while also prioritizing uncertain, distant predictions, thereby compromising immediate safety. To address these issues, we propose ResAD, a novel Normalized Residual Trajectory Modeling framework. Instead of predicting the future trajectory directly, our approach reframes the learning task to predict the residual deviation from a deterministic inertial reference. The inertial reference serves as a counterfactual, forcing the model to move beyond simple pattern recognition and instead identify the underlying causal factors (e.g., traffic rules, obstacles) that necessitate deviations from a default, inertially-guided path. To deal with the optimization imbalance caused by uncertain, long-term horizons, ResAD further incorporates Point-wise Normalization of the predicted residual. It re-weights the optimization objective, preventing large-magnitude errors associated with distant, uncertain waypoints from dominating the learning signal. Extensive experiments validate the effectiveness of our framework. On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy with only two denoising steps, demonstrating that our approach significantly simplifies the learning task and improves model performance. The code will be released to facilitate further research.

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