Uncertainty-Aware End-to-End Autonomous Driving Modeling

End-to-end autonomous driving has become a prominent research paradigm. However, these methods typically rely on deterministic static and dynamic representations, leading to compounded errors that accumulate over time across modules and become increasingly amplified, ultimately degrading planning performance. Meanwhile, the deterministic sequential arrangement of modules leads to information loss and error propagation, and makes it difficult to model the interactions between the ego and surrounding agents, thereby compromising the reliability of the planning outcomes. Moreover, these methods exhibit limited capacity in perceiving and prioritizing critical features, as they assume a deterministic and uniform importance across all feature dimensions. To overcome these limitations, we introduce an uncertainty-aware end-to-end driving model to enhance planning performance and overall safety. First, we jointly model both static and dynamic uncertainties and incorporate them into the model, enabling the model to perceive and respond to environment uncertainties. Static uncertainty guides the extraction of correct map structures and driving rules, while dynamic uncertainty enables the model to capture the stochastic interactions between traffic participants and environment and provides probabilistic scores for trajectory selection, helping to avoid collisions. Second, we propose a distributed query mechanism that leverages planning-specific queries to decode raw features, thereby removing the reliance of planning on scene understanding. In addition, we jointly model prediction and planning, enabling rich interactions between ego and surrounding agents. Third, we introduce a multi-scale dilated aggregation that adjusts the attention to informative features, highlighting critical feature contributions and enlarges the receptive field. We conduct extensive evaluations on nuScenes dataset and Bench2Drive benchmark. Experimental results demonstrate that our approach achieves the lowest planning collision rate and L2 error, and enhances inference speed and efficiency, supporting the deployment of high-performance, low-cost.