A Hybrid Driving Decision-Making System Integrating Markov Logic Networks and Connectionist AI

Abstract

Connectionist artificial intelligence (AI) can power many critical tasks for connected and autonomous vehicles (CAVs). However, connectionist AI lacks interpretability and usually needs large amount of data for learning. A Markov logic network (MLN), which combines first-order logic (FOL) with statistical learning, learns weighted FOL formulas for inference. MLNs can incorporate domain expert knowledge in the form of FOL formulas to achieve data-efficient learning and transparent decision process. In this paper, we propose a hybrid driving decision-making system, which integrates a MLN module and a deep Q-network (DQN) for enhanced driving safety. The MLN module evaluates the safety of ranked actions from DQN to reduce potential collisions. A collective MLN (Co-MLN) learning algorithm is proposed and it enables CAVs collectively learn a global MLN model for safe state transitions, given distributed small amount of noisy data. A hybrid DQN-MLN learning algorithm is also developed for CAVs to collectively learn to drive in new driving environments. Simulations performed using a highway driving simulator show that the proposed Co-MLN algorithm is highly data-efficient and the learned hybrid driving system can effectively reduce collisions. In addition, the learned MLN module provides transparency for safety-critical driving decisions.