Autonomous Behavior Selection For Self-driving Cars Using Probabilistic Logic Factored Markov Decision Processes

We propose probabilistic logic factored Markov decision processes (PL-fMDPs) as a behavior selection scheme for self-driving cars. Probabilistic logic combines logic programming with probability theory to achieve clear, rule-based knowledge descriptions of multivariate probability distributions, and a flexible mixture of deductive and probabilistic inferences. Factored Markov decision processes (fMDPs) are widely used to generate reward-optimal action policies for stochastic sequential decision problems. For evaluation, we developed a simulated self-driving car with reliable modules for behavior selection, perception, and control. The behavior selection module is composed of a two-level structure of four action policies obtained from PL-fMDPs. Three main tests were conducted focused on the selection of the appropriate actions in specific driving scenarios, and the overtaking of static obstacle vehicles and dynamic obstacle vehicles. We performed 520 repetitions of these tests. The self-driving car completed its task without collisions in 99.2% of the repetitions. Results show the suitability of the overall self-driving strategy and PL-fMDPs to construct safe action policies for self-driving cars.

本方案提出了一种基于概率逻辑分解的马尔可夫决策过程（PL-fMDPs）作为自动驾驶汽车的行为选择方案。概率逻辑融合了逻辑编程与概率论，旨在实现多变量概率分布的清晰、基于规则的认知描述，以及演绎推理与概率推理的灵活混合。分解的马尔可夫决策过程（fMDPs）被广泛应用于生成奖励最优的动作策略，以解决随机序列决策问题。为评估目的，我们开发了一款配备可靠的行为选择、感知和控制模块的模拟自动驾驶汽车。行为选择模块由来自PL-fMDPs的四项动作策略构成的两层结构组成。针对特定驾驶场景中的动作选择、超越静态障碍车辆和动态障碍车辆进行了三项主要测试。我们进行了520次这些测试的重复。在99.2%的重复中，自动驾驶汽车完成了任务且未发生碰撞。结果表明，整体自动驾驶策略及PL-fMDPs适用于构建自动驾驶汽车的安全动作策略。