论文标题
通过概率控制障碍功能的安全加强学习，以坡道合并
Safe Reinforcement Learning with Probabilistic Control Barrier Functions for Ramp Merging
论文作者
论文摘要
先前的工作已经考虑将强化学习和模仿学习方法应用于自主驾驶方案，但是算法的安全性或效率都受到损害。通过使用嵌入加固学习政策中的控制屏障功能，我们制定了安全的政策，以优化自主驾驶工具的性能。但是，控制屏障功能需要对汽车模型的良好近似。我们使用概率控制屏障的功能作为模型不确定性的估计。该算法在Carla（Dosovitskiy等，2017）模拟器中作为在线版本实现，并在从NGSIM数据库中提取的数据集中实现。提出的算法不仅是一种安全的坡道合并算法，而且是用于解决高速公路上坡道合并的安全自动驾驶算法。
Prior work has looked at applying reinforcement learning and imitation learning approaches to autonomous driving scenarios, but either the safety or the efficiency of the algorithm is compromised. With the use of control barrier functions embedded into the reinforcement learning policy, we arrive at safe policies to optimize the performance of the autonomous driving vehicle. However, control barrier functions need a good approximation of the model of the car. We use probabilistic control barrier functions as an estimate of the model uncertainty. The algorithm is implemented as an online version in the CARLA (Dosovitskiy et al., 2017) Simulator and as an offline version on a dataset extracted from the NGSIM Database. The proposed algorithm is not just a safe ramp merging algorithm but a safe autonomous driving algorithm applied to address ramp merging on highways.
