Optimizing driving entity switching of semi-automated vehicles under automation degradation

Abstract

Transitioning to use automated vehicles is a gradual process. Until full automation capabilities are developed there is a need to mediate which driving entity - human or autonomous driving system (ADS) - should be in control depending on the circumstances. This research aims at investigating the switching between manual and automated driving in semi-autonomous vehicles when the ADS becomes unfit to drive. To this end, a simple environment simulation was created and an MDP model was formulated that accounts for sensor failures and leaving the operational design domain (ODD). Deep Q-Network (DQN), a deep reinforcement learning (RL) algorithm was trained and evaluated against a hand-curated decision-tree-based standard. The DQN-based policy did not reach the performance of the baseline algorithm. The conclusion is drawn that using DQN to handle this multi-objective decision problem using an intuition-based reward function cannot learn an optimal policy.