用真实道路数据验证行为克隆模型在自动驾驶中的控制效果
Behavioral Cloning Models Reality Check for Autonomous Driving
- 基于图像输入的行为克隆方法实时预测转向角度
- 真实场景测试显示转向预测误差小，响应速度快
- 适合关注自动驾驶感知控制落地的工程师
当应用于真实世界自动驾驶控制时，近期在自动驾驶感知系统方面的进展有多有效？尽管众多基于视觉的自动驾驶系统已在模拟环境中训练和评估，但这些系统在真实环境中的验证却严重不足。本文通过使用缩比研究车辆采集的数据，在多种赛道设置上对采用行为克隆（BC）进行横向控制的先进感知系统进行了真实世界验证。该系统处理原始图像数据，直接预测转向指令。实验结果表明，这些方法在实时条件下能以极低的误差预测转向角度，显示出在真实应用中具有广阔前景。
原文摘要 · Abstract (English)
How effective are recent advancements in autonomous vehicle perception systems when applied to real-world autonomous vehicle control? While numerous vision-based autonomous vehicle systems have been trained and evaluated in simulated environments, there is a notable lack of real-world validation for these systems. This paper addresses this gap by presenting the real-world validation of state-of-the-art perception systems that utilize Behavior Cloning (BC) for lateral control, processing raw image data to predict steering commands. The dataset was collected using a scaled research vehicle and tested on various track setups. Experimental results demonstrate that these methods predict steering angles with low error margins in real-time, indicating promising potential for real-world applications.
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