We consider the problem of predicting the motion of vehicles in the surrounding of an
autonomous car, for improved motion planning in lane-based driving scenarios without inter …

To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles
need to have the ability to predict the future motion of surrounding vehicles. Multiple …

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)
benefits the on-road motion planning for intelligent and autonomous vehicles. Complex …

Dynamic Bayesian networks (DBNs) are a popular method for driver intention estimation
and trajectory prediction. To account for hybrid state spaces and non-linear system …

We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple
agents, an important task in autonomous driving. Our representation is a spatio-temporal …

This paper presents a novel online-capable interaction-aware intention and maneuver
prediction framework for dynamic environments. The main contribution is the combination of …

Human drivers are endowed with an inborn ability to put themselves in the position of other
drivers and reason about their behavior and intended actions. State-of-the-art driving …

Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …

Autonomous vehicle technologies have evolved quickly over the last few years, with safety
being one of the key requirements for their full deployment. However, ensuring their safety …

There is a dilemma regarding the accuracy and reality of vehicle trajectory prediction.
Balancing and predicting the effective trajectory is a topic of debate in autonomous driving …