纯视觉方案实现车辆避障，兼顾精度与实时性。
Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios
- 仅用摄像头结合YOLOv11与Depth Anything V2感知障碍物
- 在校园场景中成功避开多种障碍物，验证系统有效性
- 适合关注低成本视觉导航的自动驾驶研究者
障碍物避让对自动驾驶车辆的安全至关重要。准确的感知与运动规划是车辆在复杂环境中导航并避免碰撞的关键。本文提出一种高效的避障流程，采用纯摄像头感知模块与基于Frenet-Pure Pursuit的规划策略。通过融合计算机视觉进展，系统利用YOLOv11进行目标检测，并采用当前先进的单目深度估计模型Depth Anything V2来估算物体距离。对这些模型的对比分析提供了其在真实环境下的准确性、效率与鲁棒性的宝贵见解。系统在大学校园的多样化场景中进行了评估，展示了其处理各类障碍物并提升自主导航能力的有效性。避障实验结果视频可在此观看：https://www.youtube.com/watch?v=FoXiO5S_tA8
原文摘要 · Abstract (English)
Obstacle avoidance is essential for ensuring the safety of autonomous vehicles. Accurate perception and motion planning are crucial to enabling vehicles to navigate complex environments while avoiding collisions. In this paper, we propose an efficient obstacle avoidance pipeline that leverages a camera-only perception module and a Frenet-Pure Pursuit-based planning strategy. By integrating advancements in computer vision, the system utilizes YOLOv11 for object detection and state-of-the-art monocular depth estimation models, such as Depth Anything V2, to estimate object distances. A comparative analysis of these models provides valuable insights into their accuracy, efficiency, and robustness in real-world conditions. The system is evaluated in diverse scenarios on a university campus, demonstrating its effectiveness in handling various obstacles and enhancing autonomous navigation. The video presenting the results of the obstacle avoidance experiments is available at: https://www.youtube.com/watch?v=FoXiO5S_tA8
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