The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …

Currently, autonomous vehicles (AVs) have gained considerable research interest in motion
planning (MP) to control driving. Deep learning (DL) is a subset of machine learning …

The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world
scenarios with human-like understanding and responsiveness. In this paper, we introduce …

With the rapid development and the growing deployment of autonomous ground vehicles
(AGVs) worldwide, there is an increasing need to design reliable, efficient, robust, and …

Autonomous driving is an emerging technology that has advanced rapidly over the last
decade. Modern transportation is expected to benefit greatly from a wise decision-making …

Autonomous driving involves complex decision-making in highly interactive environments,
requiring thoughtful negotiation with other traffic participants. While reinforcement learning …

Reinforcement learning has emerged as an important approach for autonomous driving. A
reward function is used in reinforcement learning to establish the learned skill objectives …

Conditional imitation learning (CIL) has proven superior to other autonomous driving (AD)
algorithms. However, its performance evaluation through physical implementations is still …

Real-world driving involves intricate interactions among vehicles navigating through dense
traffic scenarios. Recent research focuses on enhancing the interaction awareness of …