论文标题
Raspberry Pi和Arduino的实时车道检测和运动计划，用于自动驾驶汽车原型
Real-time Lane detection and Motion Planning in Raspberry Pi and Arduino for an Autonomous Vehicle Prototype
论文作者
论文摘要
本文讨论了一辆车辆原型，该原型识别街道的车道并在没有任何人类投入的情况下相应地计划其运动。 PI摄像头1.3捕获实时视频，然后由Raspberry-Pi 3.0模型B处理。图像处理算法用Python 3.7.4编写，使用OpenCV 4.2。 Arduino Uno用于控制控制电动机控制器的PID算法，该算法又控制车轮。用于检测车道的算法是Canny Edge检测算法和霍夫转化。基本代数用于绘制检测到的车道。检测后，使用Kalman滤波器预测方法跟踪车道。然后找到两个车道的中点，这是初始转向方向。通过使用过去的累积平均方法和Kalman滤波器预测方法，可以进一步平滑这个初始转向方向。该原型在实时的受控环境中进行了测试。全面测试结果表明，该原型可以检测道路车道并成功计划其运动。
This paper discusses a vehicle prototype that recognizes streets' lanes and plans its motion accordingly without any human input. Pi Camera 1.3 captures real-time video, which is then processed by Raspberry-Pi 3.0 Model B. The image processing algorithms are written in Python 3.7.4 with OpenCV 4.2. Arduino Uno is utilized to control the PID algorithm that controls the motor controller, which in turn controls the wheels. Algorithms that are used to detect the lanes are the Canny edge detection algorithm and Hough transformation. Elementary algebra is used to draw the detected lanes. After detection, the lanes are tracked using the Kalman filter prediction method. Then the midpoint of the two lanes is found, which is the initial steering direction. This initial steering direction is further smoothed by using the Past Accumulation Average Method and Kalman Filter Prediction Method. The prototype was tested in a controlled environment in real-time. Results from comprehensive testing suggest that this prototype can detect road lanes and plan its motion successfully.
