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Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving

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arxiv 2409.06702 v1 pith:XEF47O6N submitted 2024-09-10 cs.CV cs.AI

Kairui Ding , Boyuan Chen , Yuchen Su , Huan-ang Gao , Bu Jin , Chonghao Sima , Wuqiang Zhang , Xiaohui Li
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Paul Barsch Hongyang Li Hao Zhao
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classification cs.CVcs.AI
keywords drivinglanguageinterpretabilityoutputsalignedexplanationhint-adintermediate
verification ladder T0 review T1 audit T2 compute T3 formal
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End-to-end architectures in autonomous driving (AD) face a significant challenge in interpretability, impeding human-AI trust. Human-friendly natural language has been explored for tasks such as driving explanation and 3D captioning. However, previous works primarily focused on the paradigm of declarative interpretability, where the natural language interpretations are not grounded in the intermediate outputs of AD systems, making the interpretations only declarative. In contrast, aligned interpretability establishes a connection between language and the intermediate outputs of AD systems. Here we introduce Hint-AD, an integrated AD-language system that generates language aligned with the holistic perception-prediction-planning outputs of the AD model. By incorporating the intermediate outputs and a holistic token mixer sub-network for effective feature adaptation, Hint-AD achieves desirable accuracy, achieving state-of-the-art results in driving language tasks including driving explanation, 3D dense captioning, and command prediction. To facilitate further study on driving explanation task on nuScenes, we also introduce a human-labeled dataset, Nu-X. Codes, dataset, and models will be publicly available.

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