2025/11/24 by Zhang, Jiale, Qian, Yeqiang, Qin, Tong +3
Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Robotics (cs.RO) #Traffic control and management #Vehicle Dynamics and Control Systems
paper · doi:10.48550/arxiv.2511.19011
openalex publication_date 2025/11/24 · openalex created_date 2025/11/27 · openalex updated_date 2026/07/28
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.