Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing
methods to address these problems are time-consuming and not adaptive to changing …

In this study, a decision-making and motion planning controller with continuous action space
is constructed in the highway driving scenario based on deep reinforcement learning. In the …

This article considers the problem of diagnosing certain common errors in reward design. Its
insights are also applicable to the design of cost functions and performance metrics more …

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 survey, we systematically summarize the current literature on studies that apply
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …

Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan,
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …

Reinforcement learning has gradually demonstrated its decision-making ability in
autonomous driving. Reinforcement learning is learning how to map states to actions by …

With the development of intelligent driving technology, human-machine cooperative driving
is significant to improve driving safety in abnormal situations, such as distraction or incorrect …

Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to
human drivers' imperfect decision-making for vehicle control. Autonomous vehicles (AVs) …