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 …

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 …

Safe and efficient interactions with surrounding vehicles in multilane driving are essential for
autonomous vehicles. However, achieving smooth and flexible responses to surrounding …

Forecasting the scalable future states of surrounding traffic participants in complex traffic
scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible …

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 …

Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …

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 …

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 …

The motion planners used in self-driving vehicles need to generate trajectories that are safe,
comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior …

Planning for autonomous driving in complex, urban scenarios requires accurate prediction
of the trajectories of surrounding traffic participants. Their future behavior depends on their …