This paper presents a latent space reinforcement learning method for interpretable decision-
making of autonomous vehicles at highway on-ramps. This method is based on the latent …

The safe operation of automated vehicles (AVs) is now on the research agenda, with
attention to the AV's operational design domain (ODD), which defines the conditions in …

Autonomous agents (robots) face tremendous challenges while interacting with
heterogeneous human agents in close proximity. One of these challenges is that the …

Achieving the highest levels of automated driving will require effective solutions to the key
challenging maneuver of highway on-ramp merging. This paper extends our previous work …

We propose a novel autonomous driving frame-work that leverages graph-based features of
roads, such as road positions and connections. The proposed method is divided into two …

Highways, as a type of high-grade road, are an essential component of intelligent connected
vehicle testing and a crucial aspect in achieving fully autonomous driving technology. To …

Autonomous driving is expected to become more common in the future. Autonomous
vehicles operate today in limited use cases like highway driving and in major cities as …

This paper presents a novel approach to modeling human driving behavior, designed for
use in evaluating autonomous vehicle control systems in a simulation environments. Our …

Decision-making is a crucial task for autonomous driving. Taking the wrong decision might
cause a cartographical accident. Decision-making implies planning the appropriate …

Adjacent lane-changing is one of the most dangerous maneuvers which may lead to rear-
end crash, uncomfortable braking and sharp steering. If the autonomous driving system can …