用少量真人驾驶数据，让自动驾驶车学会在复杂越野路面上自主导航。
Learning Autonomy: Off-Road Navigation Enhanced by Human Input
- 仅用单目摄像头和5-10分钟真人驾驶示范，直接学习人类驾驶习惯。
- 能在草地、泥地等多种地形中快速适应并稳定导航。
- 大幅减少真实数据需求，适合快速部署于野外自动驾驶场景。
在自动驾驶领域，非铺装路面导航面临独特挑战，包括草地、泥土等不可预测路面，以及灌木、水坑等意外障碍。本文提出一种基于学习的局部规划器，通过仅使用单目摄像头，直接从真实世界驾驶示范中捕捉人类驾驶细节。该规划器具备在多种复杂非铺装环境中导航的能力，并具有快速学习特性。仅需5至10分钟的人类示范数据，即可快速掌握各类非铺装路况下的驾驶行为。该方法显著降低了实现人类驾驶偏好所需的真实世界数据量，使规划器可直接应用于实际场景，无需手动调参，展现出在非铺装自动驾驶中的快速适应与高灵活性。
原文摘要 · Abstract (English)
In the area of autonomous driving, navigating off-road terrains presents a unique set of challenges, from unpredictable surfaces like grass and dirt to unexpected obstacles such as bushes and puddles. In this work, we present a novel learning-based local planner that addresses these challenges by directly capturing human driving nuances from real-world demonstrations using only a monocular camera. The key features of our planner are its ability to navigate in challenging off-road environments with various terrain types and its fast learning capabilities. By utilizing minimal human demonstration data (5-10 mins), it quickly learns to navigate in a wide array of off-road conditions. The local planner significantly reduces the real world data required to learn human driving preferences. This allows the planner to apply learned behaviors to real-world scenarios without the need for manual fine-tuning, demonstrating quick adjustment and adaptability in off-road autonomous driving technology.
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