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

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

Bakos, Csanád (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology)

Li, Y. (mentor)
Spaan, M.T.J. (mentor)
van Deursen, A. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-01

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.

reinforcement learning
Markov Decision Processes
semi-autonomous vehicles
control authority
intelligent transportation systems
safety

http://resolver.tudelft.nl/uuid:b2a0aa8e-fffe-473e-97de-57e2174cdf90

Student theses

bachelor thesis

© 2021 Csanád Bakos