论文标题
使用强化学习进行自动驾驶的安全轨迹计划
Safe Trajectory Planning Using Reinforcement Learning for Self Driving
论文作者
论文摘要
自动驾驶汽车必须能够在具有高维状态的特征，无数的优化目标和复杂行为的标志性环境中聪明地行动。传统上，经典的优化和搜索技术已应用于自动驾驶问题。但是它们并不能完全解决具有高维状态和复杂行为的环境中的操作。最近，为自动驾驶的任务提出了模仿学习。但是获得足够的培训数据是劳动密集型的。已经提出了加强学习的一种直接控制汽车的方式，但这具有安全性和舒适性。我们建议使用无模型的强化学习在自动驾驶的轨迹计划阶段，并表明这种方法使我们能够以更安全，一般和舒适的方式操作汽车，这是自动驾驶任务所需的。
Self-driving vehicles must be able to act intelligently in diverse and difficult environments, marked by high-dimensional state spaces, a myriad of optimization objectives and complex behaviors. Traditionally, classical optimization and search techniques have been applied to the problem of self-driving; but they do not fully address operations in environments with high-dimensional states and complex behaviors. Recently, imitation learning has been proposed for the task of self-driving; but it is labor-intensive to obtain enough training data. Reinforcement learning has been proposed as a way to directly control the car, but this has safety and comfort concerns. We propose using model-free reinforcement learning for the trajectory planning stage of self-driving and show that this approach allows us to operate the car in a more safe, general and comfortable manner, required for the task of self driving.
