Deep Reinforcement Learning (DRL) is used here for decentralized decision-making and
longitudinal control for high-speed on-ramp merging. The DRL environment state includes …

Predicting lane-changing intentions has long been a very active area of research in the
autonomous driving community. However, most of the literature has focused on individual …

For an efficient integration of autonomous vehicles on roads, human-like reasoning and
decision making in complex traffic situations are needed. One of the key factors to achieve …

Driving at an unsignalized intersection is a complex traffic scenario that requires both traffic
safety and efficiency. At the unsignalized intersection, the driving policy does not simply …

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 …

This paper proposes an improved decision-making method based on deep reinforcement
learning to address on-ramp merging challenges in highway autonomous driving. A novel …

Lane change for autonomous vehicles (AVs) is an important but challenging task in complex
dynamic traffic environments. Due to difficulties in guarantee safety as well as a high …

Advanced deep reinforcement learning shows promise as an approach to addressing
continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a …

Autonomous vehicles (AVs) need to interact with other traffic participants who can be either
cooperative or aggressive, attentive or inattentive. Such different characteristics can lead to …

Predicting future trajectories of traffic agents in highly interactive environments is an
essential and challenging problem for the safe operation of autonomous driving systems. On …