论文标题
CW-erm：通过闭环加权经验风险最小化改善自动驾驶计划
CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization
论文作者
论文摘要
通过行为克隆对自动驾驶车辆政策的模仿学习通常以开环的方式进行，忽略了对未来国家的行动的影响。纯粹是经验风险最小化（ERM）纯粹的培训政策可能对现实世界的表现有害，因为它偏向于仅匹配开环行为，在闭环中评估时结果差。在这项工作中，我们开发了一种高效且简单的原则，称为闭环加权经验风险最小化（CW-ERM），在该原理中，首先使用闭环评估程序来识别对实践驱动性能至关重要的培训数据样本，然后我们这些样本来帮助DEBIAS帮助BEALIAS策略网络。我们在具有挑战性的城市驾驶数据集中评估了CW-MER，并表明该程序可显着减少碰撞以及其他非差异性闭环指标。
The imitation learning of self-driving vehicle policies through behavioral cloning is often carried out in an open-loop fashion, ignoring the effect of actions to future states. Training such policies purely with Empirical Risk Minimization (ERM) can be detrimental to real-world performance, as it biases policy networks towards matching only open-loop behavior, showing poor results when evaluated in closed-loop. In this work, we develop an efficient and simple-to-implement principle called Closed-loop Weighted Empirical Risk Minimization (CW-ERM), in which a closed-loop evaluation procedure is first used to identify training data samples that are important for practical driving performance and then we these samples to help debias the policy network. We evaluate CW-ERM in a challenging urban driving dataset and show that this procedure yields a significant reduction in collisions as well as other non-differentiable closed-loop metrics.
