Self-driving vehicles are being increasingly deployed in the wild. One of the most important
next hurdles for autonomous driving is how such vehicles will optimally interact with one …

Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …

In this paper, we study how to learn an appropriate lane changing strategy for autonomous
vehicles by using deep reinforcement learning. We show that the reward of the system …

Lane change is a common-yet-challenging driving behavior for automated vehicles. To
improve the safety and efficiency of automated vehicles, researchers have proposed various …

Lane change is receiving attention in academia. Most existing lane changing models are
rule-based lane changing models which only assume one-direction impact of surrounding …

In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …

Performing safe and efficient lane changes is a crucial feature for creating fully autonomous
vehicles. Recent advances have demonstrated successful lane following behavior using …

Lane change is a challenging task which requires delicate actions to ensure safety and
comfort. Some recent studies have attempted to solve the lane-change control problem with …

Driving is a social activity: drivers often indicate their intent to change lanes via motion cues.
We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an …

Frequent lane changes during congestion at freeway bottlenecks such as merge and
weaving areas further reduce roadway capacity. The emergence of deep reinforcement …