To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …

Reinforcement learning (RL) is a powerful data-driven control method that has been largely
explored in autonomous driving tasks. However, conventional RL approaches learn control …

On-ramp merge is a complex traffic scenario in autonomous driving. Because of the
uncertainty of the driving environment, most rule-based models cannot solve such a …

Changing lanes while driving requires coordinating the lateral and longitudinal controls of a
vehicle, considering its running state and the surrounding environment. Although the …

Autonomous Vehicles (AVs) have emerged as a promising solution by replacing human
drivers with advanced computer-aided decision-making systems. However, for AVs to …

Reinforcement learning is nowadays a popular framework for solving different decision
making problems in automated driving. However, there are still some remaining crucial …

Recently, the autonomous driving technology is at a critical phase evolving from typical,
closed scenarios to largescale, open driving scenarios, which is challenged by the diversity …

Autonomous driving has the potential to revolutionize transportation, but developing safe
and reliable systems remains a significant challenge. Reinforcement learning (RL) has …

It is expected that human-driven vehicles and autonomous vehicles (AVs) will coexist in a
mixed traffic for a long time. To enable AVs to safely and efficiently maneuver in this mixed …

Decision-making is critical for lane change in autonomous driving. Reinforcement learning
(RL) algorithms aim to identify the values of behaviors in various situations and thus they …