Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …

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

Facts proved that obeying traffic laws keeps the promise to promote the safety of self-driving
vehicles. Current self-driving vehicles usually have fixed algorithms during autonomous …

Compliance with traffic laws is a fundamental requirement for human drivers on the road,
and autonomous vehicles must adhere to traffic laws as well. However, current autonomous …

Defined traffic laws must be respected by all vehicles when driving on the road, including
self-driving vehicles without human drivers. Nevertheless, the ambiguity of human-oriented …

As autonomous driving advances, autonomous vehicles will share the road with human
drivers. This requires autonomous vehicles to adhere to human traffic laws under safe …

Modern neural trajectory predictors in autonomous driving are developed using imitation
learning (IL) from driving logs. Although IL benefits from its ability to glean nuanced and …

Training reinforcement learning (RL) agents for motion planning in heavily constrained
solution spaces may require extensive exploration, leading to long training times. In …

Purpose This study aims to comprehensively address the challenge of delineating traffic
scenarios in video footage captured by an embedded camera within an autonomous …

Motion planning algorithms should be tested on a large, diverse, and realistic set of
scenarios before deploying them in real vehicles. However, existing 3D simulators usually …