We present a novel approach for risk-aware planning with human agents in multi-agent
traffic scenarios. Our approach takes into account the wide range of human driver behaviors …

Devising planning algorithms for autonomous driving is non-trivial due to the presence of
complex and uncertain interaction dynamics between road users. In this paper, we introduce …

For an autonomous vehicle to operate reliably within real-world traffic scenarios, it is
imperative to assess the repercussions of its prospective actions by anticipating the …

Generating safe and non-conservative behaviors in dense, dynamic environments remains
challenging for automated vehicles due to the stochastic nature of traffic participants' …

The actions of an autonomous vehicle on the road affect and are affected by those of other
drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual …

The goal of autonomous vehicles is to navigate public roads safely and comfortably. To
enforce safety, traditional planning approaches rely on handcrafted rules to generate …

Choosing optimal parameters for large-scale, safetycritical, real-world motion planners in
autonomous driving systems is crucial. In this paper, we present a highly efficient framework …

The ultimate goal of self-driving technologies is to offer a safe and human-like driving
experience. As one of the most important enabling functionalities, trajectory planning has …

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 …

Given the current capabilities of autonomous vehicles, one can easily imagine autonomous
vehicles being released on the road in the near future. However, it can be assumed that this …