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

As one of the alternatives to conventional fuel vehicles, hybrid electric vehicles (HEV) offer
lower fuel consumption and fewer exhaust emissions. To improve the performance of the …

Model predictive control (MPC) has been widely researched for automotive control.
However, the real-world application of MPC for autonomous vehicles (AV) is still limited due …

Autonomous driving techniques are promising for improving the energy efficiency of
electrified vehicles (EVs) by adjusting driving decisions and optimizing energy requirements …

Kinodynamic motion planning is critical for autonomous vehicles with high maneuverability
in dynamic environments. However, obtaining near-optimal motion planning solutions with …

One of the major obstacles along the way of electric vehicles'(EVs') wider global adoption is
their limited driving range. Extreme cold or hot environments can further impact the EV's …

Nowadays, with the advancement of technology, world is trending toward high mobility and
dynamics. In this context, intersection management (IM) as one of the most crucial elements …

Safety is one of the critical challenges in the autonomous driving task. Recent works address
the safety by implementing a safe reinforcement learning (safe RL) mechanism. However …

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with
human-driven vehicles (HDVs), which render uncertain driving behavior due to varying …

Cooperative driving of connected and automated vehicles (CAVs) has attracted extensive
attention and researchers have proposed various approaches. However, existing …