用行为克隆让小车模仿人类驾驶，实现精准路径跟踪
Behavior Cloning for Mini Autonomous Car Path Following
- 通过摄像头数据训练神经网络，直接映射人类驾驶动作到控制输出
- 在13米路线测试中实现平滑精准的路径跟随，无需复杂算法
- 适合想快速验证自动驾驶控制逻辑的研究者或教学场景
本文提出并评估了一种基于行为克隆的自主小车路径跟随方法。行为克隆是一种机器学习技术，通过神经网络学习人类驾驶员的驾驶行为。利用摄像头捕捉环境与车辆运动数据，神经网络可预测维持预定路径所需的控制动作。实验采用小型自动驾驶车辆作为测试平台，简化了控制系统，避免了复杂算法设计。在13米长的测试路线中进行了评估，结果表明该方法能实现平滑且精确的路径跟踪，具备从小规模实验向全尺寸车辆应用过渡的可行性。
原文摘要 · Abstract (English)
This article presents the implementation and evaluation of a behavior cloning approach for route following with autonomous cars. Behavior cloning is a machine-learning technique in which a neural network is trained to mimic the driving behavior of a human operator. Using camera data that captures the environment and the vehicle's movement, the neural network learns to predict the control actions necessary to follow a predetermined route. Mini-autonomous cars, which provide a good benchmark for use, are employed as a testing platform. This approach simplifies the control system by directly mapping the driver's movements to the control outputs, avoiding the need for complex algorithms. We performed an evaluation in a 13-meter sizer route, where our vehicle was evaluated. The results show that behavior cloning allows for a smooth and precise route, allowing it to be a full-sized vehicle and enabling an effective transition from small-scale experiments to real-world implementations.
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