Traj-Explainer: An Explainable and Robust Multi-modal Trajectory Prediction Approach

• URL: http://arxiv.org/abs/2410.16795v1
• Date: Tue, 22 Oct 2024 08:17:33 GMT
• Title: Traj-Explainer: An Explainable and Robust Multi-modal Trajectory Prediction Approach
• Authors: Pei Liu, Haipeng Liu, Yiqun Li, Tianyu Shi, Meixin Zhu, Ziyuan Pu,
• Abstract summary: Navigating complex traffic environments has been significantly enhanced by advancements in intelligent technologies, enabling accurate environment perception and trajectory prediction for automated vehicles. Existing research often neglects the consideration of the joint reasoning of scenario agents and lacks interpretability in trajectory prediction models. An explainability-oriented trajectory prediction model is designed in this work, named Explainable Diffusion Conditional based Multimodal Trajectory Prediction Traj-Explainer.
• Score: 12.60529039445456
• License:
• Abstract: Navigating complex traffic environments has been significantly enhanced by advancements in intelligent technologies, enabling accurate environment perception and trajectory prediction for automated vehicles. However, existing research often neglects the consideration of the joint reasoning of scenario agents and lacks interpretability in trajectory prediction models, thereby limiting their practical application in real-world scenarios. To this purpose, an explainability-oriented trajectory prediction model is designed in this work, named Explainable Conditional Diffusion based Multimodal Trajectory Prediction Traj-Explainer, to retrieve the influencing factors of prediction and help understand the intrinsic mechanism of prediction. In Traj-Explainer, a modified conditional diffusion is well designed to capture the scenario multimodal trajectory pattern, and meanwhile, a modified Shapley Value model is assembled to rationally learn the importance of the global and scenario features. Numerical experiments are carried out by several trajectory prediction datasets, including Waymo, NGSIM, HighD, and MoCAD datasets. Furthermore, we evaluate the identified input factors which indicates that they are in agreement with the human driving experience, indicating the capability of the proposed model in appropriately learning the prediction. Code available in our open-source repository: \url{https://anonymous.4open.science/r/Interpretable-Prediction}.

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