How autonomous vehicles and human drivers share public transportation systems is an
important problem, as fully automatic transportation environments are still a long way off …

The thriving artificial intelligence (AI) technologies have been used to address various
challenges in the physical world. Currently, AI methods are widely used in perception …

Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …

Reinforcement learning (RL) provides a compelling framework for enabling autonomous
vehicles to continue to learn and improve diverse driving behaviors on their own. However …

Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …

The main topic of this thesis is tactical decision-making for autonomous driving. An
autonomous vehicle must be able to handle a diverse set of environments and traffic …

Designing reliable decision strategies for autonomous urban driving is challenging.
Reinforcement learning (RL) has been used to automatically derive suitable behavior in …

Reinforcement learning (RL) has shown to reach super human-level performance across a
wide range of tasks. However, unlike supervised machine learning, learning strategies that …

Highly-automated driving relies on informed decision making and safe control in a dynamic
environment which is regulated by traffic rules. This complex environment can be formulated …

Reinforcement learning promises to provide a state-of-the-art solution to the decision
making problem of autonomous driving. Nonetheless, numerous real-world decision making …