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arXiv · 2511.19011

End-to-end Autonomous Vehicle Following System using Monocular Fisheye Camera

Jiale Zhang·Yeqiang Qian·Tong Qin·Mingyang Jiang·Siyuan Chen·Ming Yang
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Abstract

The increase in vehicle ownership has led to increased traffic congestion, more accidents, and higher carbon emissions. Vehicle platooning is a promising solution to address these issues by improving road capacity and reducing fuel consumption. However, existing platooning systems face challenges such as reliance on lane markings and expensive high-precision sensors, which limits their general applicability. To address these issues, we propose a vehicle following framework that expands its capability from restricted scenarios to general scenario applications using only a camera. This is achieved through our newly proposed end-to-end method, which improves overall driving performance. The method incorporates a semantic mask to address causal confusion in multi-frame data fusion. Additionally, we introduce a dynamic sampling mechanism to precisely track the trajectories of preceding vehicles. Extensive closed-loop validation in real-world vehicle experiments demonstrates the system's ability to follow vehicles in various scenarios, outperforming traditional multi-stage algorithms. This makes it a promising solution for cost-effective autonomous vehicle platooning. A complete real-world vehicle experiment is available at https://youtu.be/zL1bcVb9kqQ.

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Jiale Zhang, Yeqiang Qian, Tong Qin, Mingyang Jiang, Siyuan Chen, Ming Yang. 2025-11-24. End-to-end Autonomous Vehicle Following System using Monocular Fisheye Camera. https://arxiv.org/abs/2511.19011

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