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 …

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

Reinforcement learning (RL) has been widely used in the decision-making of autonomous
vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal …

The advancement in sensor technologies, mobile network technologies, and artificial
intelligence has pushed the boundaries of different verticals, eg, eHealth and autonomous …

Motion prediction is the fundamental input for decision-making in autonomous vehicles. The
current motion prediction solutions are designed with a strong reliance on black box …

Autonomous vehicles are considered to be the next big thing. Several companies are racing
to put self-driving vehicles on the road by 2020. Regulations and standards are not ready for …

With recent efforts to make vehicles intelligent, solutions based on machine learning have
been accepted to the ecosystem. These systems in the automotive domain are growing fast …

Ensuring the safety of fully autonomous vehicles requires a multi-disciplinary approach
across all the levels of functional hierarchy, from hardware fault tolerance, to resilient …

Autonomous vehicle (AV) is expected to reshape the future transportation system, and its
decision-making is one of the most critical modules. Many current decision-making modules …

The complexity of advanced driver-assistance systems (ADASs) is steadily increasing. While
the first applications were based on mere warnings, current systems actively intervene in the …