This chapter presents multi-policy decision-making (MPDM): a novel approach to navigating
in dynamic multi-agent environments. Rather than planning the trajectory of the robot …

Real-world autonomous driving in city traffic must cope with dynamic environments including
other agents with uncertain intentions. This poses a challenging decision-making problem …

To operate reliably in real-world traffic, an autonomous car must evaluate the consequences
of its potential actions by anticipating the uncertain intentions of other traffic participants. This …

We propose a multi-agent based computational framework for modeling decision-making
and strategic interaction at micro level for smart vehicles in a smart world. The concepts of …

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 …

The successful integration of autonomous robots in real-world environments strongly
depends on their ability to reason from context and take socially acceptable actions. Current …

In dynamic environments crowded with people, robot motion planning becomes difficult due
to the complex and tightly-coupled interactions between agents. Trajectory planning …

Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …

This article proposes a novel decision-making framework for autonomous vehicles (AVs),
called predictor–corrector potential game (PCPG), composed of a predictor and a corrector …

We address the problem of ego-vehicle navigation in dense simulated traffic environments
populated by road agents with varying driver behaviors. Navigation in such environments is …