Autonomous driving in an urban environment with surrounding agents remains challenging.
One of the key challenges is to accurately predict the traversability map that probabilistically …

When driving in urban environments, an autonomous vehicle must account for the
interaction with other traffic participants. It must reason about their future behavior, how its …

Predicting the future trajectories of surrounding vehicles plays a vital role in ensuring the
safety of autonomous driving. It is extremely challenging for the pure imitation method due to …

Considerable research efforts have been devoted to the development of motion planning
algorithms, which form a cornerstone of the autonomous driving system (ADS). Nonetheless …

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 …

Autonomous vehicles (AVs) must share the driving space with other drivers and often
employ conservative motion planning strategies to ensure safety. These conservative …

The accurate prediction of behaviors of surrounding traffic participants is critical for
autonomous vehicles (AV). How to fully encode both explicit (eg, map structure and road …

Forecasting the trajectories of vehicles in a highway scene is a crucial task in the area of
Intelligent Transportation Systems (ITS), as the complexities of a Web of driving maneuvers …

The field of self-driving vehicles (SDVs) is going viral among researchers from a broad
spectrum of specialties. SDVs are expected to have profound impacts on the world once fully …

Motion prediction of road users in traffic scenes is critical for autonomous driving systems
that must take safe and robust decisions in complex dynamic environments. We present a …