论文标题
评估基于MPC的模仿学习对类似人类自动驾驶的评估
Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving
论文作者
论文摘要
这项工作评估并分析了模仿学习（IL）和可区分模型预测控制（MPC）的组合，以应用类似人类的自主驾驶。我们将MPC与基于层次学习的政策相结合，并在开环和闭环中衡量其与与人类驾驶特征的安全性，舒适性和相似性相关的指标。我们还展示了通过闭环训练增强开环行为克隆的价值，以进行更强大的学习，从而通过MPC使用的状态空间模型近似时间来近似策略梯度。我们对车道保持控制系统进行实验评估，从固定基础驾驶模拟器上收集的示范中学到的学历，并表明我们的模仿策略接近了人类驾驶风格的偏好。
This work evaluates and analyzes the combination of imitation learning (IL) and differentiable model predictive control (MPC) for the application of human-like autonomous driving. We combine MPC with a hierarchical learning-based policy, and measure its performance in open-loop and closed-loop with metrics related to safety, comfort and similarity to human driving characteristics. We also demonstrate the value of augmenting open-loop behavioral cloning with closed-loop training for a more robust learning, approximating the policy gradient through time with the state space model used by the MPC. We perform experimental evaluations on a lane keeping control system, learned from demonstrations collected on a fixed-base driving simulator, and show that our imitative policies approach the human driving style preferences.
