Although autonomous driving techniques have achieved great improvements, challenges
still exist in decision making for variety of different scenarios under uncertain and interactive …

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 …

Safe motion planning in complex and interactive environments is one of the major
challenges for developing autonomous vehicles. In this paper, we propose an interaction …

Autonomous driving requires decision making in dynamic and uncertain environments. The
uncertainty from the prediction originates from the noisy sensor data and from the fact that …

Precisely assessing driving threat of road segments could significantly enhance the driving
efficiency of intelligent connected vehicles (ICV) within mixed traffic scenarios. Existing …

Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore,
it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most …

For highly automated driving above SAE level 3, behavior generation algorithms must
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …

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

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 …

Trajectory planning is one of the most important modules of the Autonomous Driving
Systems (ADSs), which aims to achieve a safe and comfortable interaction between the …