This report demonstrates several methods used to make a self-driving vehicle using a supervised learning algorithm and a forward-facing RGBD camera. The project originally involved research in creating an adversarial attack on the vehicle's model, but due to difficulties with the initial training of the car, the plans were discarded in favor of completing the imitation learning portion of the project. Many approaches were explored, but due to challenges introduced by an unbalanced data set, the approaches had limited effectiveness.
翻译:本报告展示了使用监督的学习算法和前方的RGBD照相机自行驾驶车辆所用的几种方法,该项目最初涉及研究对车辆模型进行对抗式攻击,但由于车辆初始训练困难,计划被丢弃,赞成完成项目的模拟学习部分,许多办法得到了探讨,但由于数据集不平衡所带来的挑战,这些办法效果有限。