3 accepted papers
Behavioral decision making is an important aspect of autonomous vehicles (AV). In this work, we propose a behavior planning structure based on hierarchical reinforcement learning (HRL) which is capable of performing autonomous vehicle planning tasks in simulated environments with multiple sub-goals.…
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. Understanding human drivers’ behavior can be beneficial for autonomous vehicle decision making and planning, especially when…
Performing safe and efficient lane changes is a crucial feature for creating fully autonomous vehicles. Recent advances have demonstrated successful lane following behavior using deep reinforcement learning, yet the interactions with other vehicles on-road for lane changes are rarely considered. In…