Conformal Trajectory Prediction with Multi-View Data Integration in Cooperative Driving

• URL: http://arxiv.org/abs/2408.00374v2
• Date: Fri, 2 Aug 2024 13:00:46 GMT
• Title: Conformal Trajectory Prediction with Multi-View Data Integration in Cooperative Driving
• Authors: Xi Chen, Rahul Bhadani, Larry Head,
• Abstract summary: Current research on trajectory prediction primarily relies on data collected by onboard sensors of an ego vehicle. We introduce V2INet, a novel trajectory prediction framework designed to model multi-view data by extending existing single-view models. Our results demonstrate superior performance in terms of Final Displacement Error (FDE) and Miss Rate (MR) using a single GPU.
• Score: 4.628774934971078
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current research on trajectory prediction primarily relies on data collected by onboard sensors of an ego vehicle. With the rapid advancement in connected technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, valuable information from alternate views becomes accessible via wireless networks. The integration of information from alternative views has the potential to overcome the inherent limitations associated with a single viewpoint, such as occlusions and limited field of view. In this work, we introduce V2INet, a novel trajectory prediction framework designed to model multi-view data by extending existing single-view models. Unlike previous approaches where the multi-view data is manually fused or formulated as a separate training stage, our model supports end-to-end training, enhancing both flexibility and performance. Moreover, the predicted multimodal trajectories are calibrated by a post-hoc conformal prediction module to get valid and efficient confidence regions. We evaluated the entire framework using the real-world V2I dataset V2X-Seq. Our results demonstrate superior performance in terms of Final Displacement Error (FDE) and Miss Rate (MR) using a single GPU. The code is publicly available at: \url{https://github.com/xichennn/V2I_trajectory_prediction}.

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