Predicting the behaviors of other road users is crucial to safe and intelligent decision-making
for autonomous vehicles (AVs). However, most motion prediction models ignore the …

By observing their environment as well as other traffic participants, humans are enabled to
drive road vehicles safely. Vehicle passengers, however, perceive a notable difference …

Autonomous vehicles should be able to predict the future states of its environment and
respond appropriately. Specifically, predicting the behavior of surrounding human drivers is …

Before taking a decision, a driver anticipates the future behavior of other traffic participants.
However, if a driver is inattentive or overloaded, he may fail to consider relevant information …

“Socially cooperative driving” is an integral part of our everyday driving, hence requiring
special attention to imbue the autonomous driving with a more natural driving behavior. In …

This paper introduces a robust prediction-and cost-function based algorithm for autonomous
freeway driving. A prediction engine is built so that the autonomous vehicle is able to …

We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for
urban driving. Previous work has employed a variety of methods, including multimodal …

To reduce the number of collision fatalities at crossroads intersections, many countries have
started replacing intersections with non-signalized roundabouts, forcing the drivers to be …

Automated driving within a lane is a fascinating experience already. However, more exiting
but also technically more challenging is to dare the next step of automating tactical behavior …

Human driving depends on latent states, such as aggression and intent, that cannot be
directly observed. In this work, we propose a method for learning driver models that can …