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.

翻译：车辆保有量的增加导致了交通拥堵加剧、事故频发以及碳排放量上升。车辆编队行驶通过提升道路通行能力和降低燃油消耗，为解决这些问题提供了一种前景广阔的方案。然而，现有编队系统面临依赖车道标记和昂贵的高精度传感器等挑战，这限制了其普遍适用性。为解决这些问题，我们提出了一种仅使用摄像头的车辆跟随框架，将其能力从受限场景扩展到通用场景应用。这是通过我们新提出的端到端方法实现的，该方法提升了整体驾驶性能。该方法引入了语义掩码以解决多帧数据融合中的因果混淆问题。此外，我们提出了一种动态采样机制，以精确跟踪前方车辆的轨迹。在真实车辆实验中进行的广泛闭环验证表明，该系统能够在多种场景下跟随车辆，其性能优于传统的多阶段算法，使其成为经济高效的自主车辆编队行驶的有力解决方案。完整的真实车辆实验视频可见：https://youtu.be/zL1bcVb9kqQ。