仅用单个鱼眼摄像头实现通用场景下的自动驾驶跟车。
End-to-end Autonomous Vehicle Following System using Monocular Fisheye Camera
- 端到端框架融合语义掩码与动态采样，提升多帧数据融合精度。
- 真实车辆实验验证在多种场景下均优于传统分阶段算法。
- 低成本方案适合推广至普通车辆的智能编队应用。
车辆保有量上升导致交通拥堵、事故增多和碳排放增加。车辆编队是改善道路容量并降低油耗的有前景解决方案。然而，现有编队系统依赖车道线和高成本高精度传感器，适用性受限。为此，我们提出一种仅使用摄像头的端到端跟车框架，将能力从受限场景拓展至通用场景。该方法引入语义掩码以解决多帧数据融合中的因果混淆问题，并设计动态采样机制精准追踪前车轨迹。在真实车辆环境中的闭环验证表明，系统可在多种场景下稳定跟车，性能优于传统多阶段算法。该方案为低成本自动驾驶编队提供了可行路径。完整实验视频见：https://youtu.be/zL1bcVb9kqQ。
原文摘要 · Abstract (English)
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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