Pedestrian crossing intention prediction is crucial for autonomous vehicles due to their inherent inertia, yet challenging. The prevailing practice is to leverage multi-modal data that correlate with pedestrian crossing intention as input to infer it, with ego-vehicle speed being a commonly used modality. However, from a causal perspective, we identify two critical issues overlooked by existing methods: 1) The causal relationship between ego-vehicle speed and pedestrian crossing intention is inconsistent across the training and testing phases, leading to a distribution shift in the data; 2) There exists a bidirectional causality between ego-vehicle speed and pedestrian crossing intention, comprising forward causality and anti-causality. The imbalanced distribution of these two causal directions in natural datasets results in causal confusion, further exacerbating the distribution shift. These issues lead to a counter-intuitive hypothesis: removing ego-vehicle speed as input can actually benefit prediction performance. Therefore, we propose LIM (Less Is More), a uni-modal model that utilizes only skeleton sequences as input. LIM features specially designed modules for efficient skeleton sequence processing, eliminating the reliance on unstable ego-vehicle speed. Moreover, LIM employs adversarial training to identify and remove any latent ego-vehicle speed information embedded within the skeleton sequences. LIM achieves competitive prediction performance compared to state-of-the-art multi-modal models while offering superior real-time capabilities and lower computational costs. By revealing critical issues overlooked by most existing work and providing a more robust crossing intention prediction model, our work contributes to the development of safer autonomous vehicles.


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    Title :

    Causal Confusion in Pedestrian Crossing Intention Prediction for Autonomous Vehicles: The Role of Ego-Vehicle Speed


    Contributors:
    Chen, Haoqiang (author) / Sun, Jianxiang (author) / Liu, Yadong (author) / Peng, Zhiyong (author) / Hu, Dewen (author)


    Publication date :

    2025-08-01


    Size :

    2285242 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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