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.

我们提出概率逻辑因子化马尔可夫决策过程（PL-fMDPs，probabilistic logic factored Markov decision processes）作为自动驾驶汽车的行为选择方案。概率逻辑将逻辑编程与概率论相结合，可对多变量概率分布实现清晰的基于规则的知识描述，并支持演绎推理与概率推理的灵活融合。因子化马尔可夫决策过程（fMDPs，factored Markov decision processes）已被广泛应用于为随机序贯决策问题生成奖励最优的动作策略。为开展评估实验，我们开发了一款集成行为选择、感知与控制三类可靠模块的模拟自动驾驶汽车。该汽车的行为选择模块由基于PL-fMDPs得到的4种动作策略构成双层结构。我们共开展了三类核心测试，分别针对特定驾驶场景下的合适动作选择、静态障碍车辆超车以及动态障碍车辆超车任务，上述测试共计完成了520次重复实验。在99.2%的重复实验中，该自动驾驶汽车均未发生碰撞并顺利完成预设任务。实验结果表明，所提出的整体自动驾驶策略以及PL-fMDPs均可适用于为自动驾驶汽车构建安全可靠的动作策略。