This paper reports on an integrated inference and decision-making approach for
autonomous driving that models vehicle behavior for both our vehicle and nearby vehicles …

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 …

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 …

We study an algorithm that allows a vehicle to autonomously change lanes in a safe but
personalized fashion without the driver's explicit initiation (eg activating the turn signals) …

In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …

Interpretation of common-yet-challenging inter-action scenarios can benefit well-founded
decisions for autonomous vehicles. Previous research achieved this using their prior …

This paper studies the decision making problem of autonomous vehicles in traffic. We model
the interaction between an autonomous vehicle and the environment as a stochastic Markov …

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 …

Autonomous vehicles need to perform social accepted behaviors in complex urban
scenarios including human‐driven vehicles with uncertain intentions. This leads to many …

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 …