FocalAD: Local Motion Planning for End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2506.11419v1
• Date: Fri, 13 Jun 2025 02:39:01 GMT
• Title: FocalAD: Local Motion Planning for End-to-End Autonomous Driving
• Abstract summary: In end-to-end autonomous driving, the motion prediction plays a pivotal role in ego-vehicle planning.<n>Existing methods often rely on globally aggregated motion features, ignoring the fact that planning decisions are primarily influenced by a small number of locally interacting agents.<n>We propose FocalAD, a novel framework that focuses on critical local neighbors and refines planning by enhancing local motion representations.
• Score: 11.37427528557442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In end-to-end autonomous driving,the motion prediction plays a pivotal role in ego-vehicle planning. However, existing methods often rely on globally aggregated motion features, ignoring the fact that planning decisions are primarily influenced by a small number of locally interacting agents. Failing to attend to these critical local interactions can obscure potential risks and undermine planning reliability. In this work, we propose FocalAD, a novel end-to-end autonomous driving framework that focuses on critical local neighbors and refines planning by enhancing local motion representations. Specifically, FocalAD comprises two core modules: the Ego-Local-Agents Interactor (ELAI) and the Focal-Local-Agents Loss (FLA Loss). ELAI conducts a graph-based ego-centric interaction representation that captures motion dynamics with local neighbors to enhance both ego planning and agent motion queries. FLA Loss increases the weights of decision-critical neighboring agents, guiding the model to prioritize those more relevant to planning. Extensive experiments show that FocalAD outperforms existing state-of-the-art methods on the open-loop nuScenes datasets and closed-loop Bench2Drive benchmark. Notably, on the robustness-focused Adv-nuScenes dataset, FocalAD achieves even greater improvements, reducing the average colilision rate by 41.9% compared to DiffusionDrive and by 15.6% compared to SparseDrive.

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