Intersections are considered one of the most complex scenarios in a self-driving framework
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …

To solve the maneuvering decision problem in air combat of unmanned combat aircraft
vehicles (UCAVs), in this paper, an autonomous maneuver decision method is proposed for …

This paper proposes a systematic driving framework where the decision making module of
reinforcement learning (RL) is integrated with rapidly-exploring random tree (RRT) as …

The adoption of the Fifth Generation (5G) and beyond 5G networks is driving the demand for
learning approaches that enable users to co-exist harmoniously in a multi-user distributed …

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 …

Graph networks have recently been used for decision making in automated driving tasks for
their ability to capture a variable number of traffic participants. Current high-level graph …

Abstract In recent years, Deep Reinforcement Learning (DRL) has been extensively used to
solve problems in various domains like traffic control, healthcare, and simulation-based …

Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios
that require a high degree of cooperation between traffic participants. However, for …

Neural networks in the automotive sector commonly have to process varying number of
objects per observation. Deep Set feature extractors have shown great success on problems …

Navigating unsignalized intersections in urban environments poses a complex challenge for
self-driving vehicles, where issues such as view obstructions, unpredictable pedestrian …