论文标题
在被封闭的交叉路口与增强学习的交叉路口自动驾驶的风险意识高水平决策
Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning
论文作者
论文摘要
如今，强化学习是解决自动驾驶中不同决策问题的流行框架。但是，仍然需要解决一些至关重要的挑战，以提供更可靠的政策。在本文中，我们提出了一种通用的风险感知DQN方法，以学习通过未信号的封闭交叉点驱动的高级动作。提出的状态表示提供了基于车道的信息，该信息允许用于多车道场景。此外，我们提出了一种基于风险的奖励功能，该功能会惩罚风险情况，而不仅仅是碰撞失败。这种有益的方法有助于将风险预测纳入我们的深度Q网络，并学习更多可靠的政策，这些政策在具有挑战性的情况下更安全。将所提出方法的效率与传统基于碰撞的奖励计划以及基于规则的交叉路口导航政策所学的DQN进行了比较。评估结果表明，所提出的方法的表现优于这两种方法。它提供的动作比Collision-Inaware DQN方法不如基于规则的策略过高。
Reinforcement learning is nowadays a popular framework for solving different decision making problems in automated driving. However, there are still some remaining crucial challenges that need to be addressed for providing more reliable policies. In this paper, we propose a generic risk-aware DQN approach in order to learn high level actions for driving through unsignalized occluded intersections. The proposed state representation provides lane based information which allows to be used for multi-lane scenarios. Moreover, we propose a risk based reward function which punishes risky situations instead of only collision failures. Such rewarding approach helps to incorporate risk prediction into our deep Q network and learn more reliable policies which are safer in challenging situations. The efficiency of the proposed approach is compared with a DQN learned with conventional collision based rewarding scheme and also with a rule-based intersection navigation policy. Evaluation results show that the proposed approach outperforms both of these methods. It provides safer actions than collision-aware DQN approach and is less overcautious than the rule-based policy.
