Due to the complexity and volatility of the traffic environment, decision-making in autonomous driving is a significantly hard problem. In this project, we use a Deep Q-Network, along with rule-based constraints to make lane-changing decision. A safe and efficient lane change behavior may be obtained by combining high-level lateral decision-making with low-level rule-based trajectory monitoring. The agent is anticipated to perform appropriate lane-change maneuvers in a real-world-like udacity simulator after training it for a total of 100 episodes. The results shows that the rule-based DQN performs better than the DQN method. The rule-based DQN achieves a safety rate of 0.8 and average speed of 47 MPH
翻译:由于交通环境的复杂性和不稳定性,自主驾驶的决策是一个非常棘手的问题。在这个项目中,我们使用深Q网络,加上基于规则的限制,作出改变车道的决定。通过将高层横向决策与低层次基于规则的轨迹监测相结合,可以取得安全和高效的改变车道行为。预计该代理人在训练100个赛事后,在一个真实的、类似于世界的大胆模拟器中进行适当的换车动作。结果显示,基于规则的DQN比DQN方法要好。基于规则的DQN达到0.8的安全率和平均速度47 MPH。