Forecasting the long-term future motion of road actors is a core challenge to the deployment
of safe autonomous vehicles (AVs). Viable solutions must account for both the static …

For an autonomous vehicle to operate reliably within real-world traffic scenarios, it is
imperative to assess the repercussions of its prospective actions by anticipating the …

Current validation methods often rely on recorded data and basic functional checks, which
may not be sufficient to encompass the scenarios an autonomous vehicle might encounter …

Automated Vehicles (AVs) have been receiving increasing attention as a potential highly
mechanised, intelligent, self-regulating futuristic mode of transport. AVs are predicted to …

Despite the advances in the autonomous driving domain, autonomous vehicles (AVs) are
still inefficient and limited in terms of cooperating with each other or coordinating with …

This thesis is concerned with the development of trajectory planning approaches targeting
autonomous driving applications in dynamic environments shared with other traffic …

A major challenge in autonomous driving has been the intractability of planning algorithms.
Research has largely focused on simple, short-term scenarios with few interacting traffic …

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 …

The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …

Creating rational driving options and designing the decision process to select the best
solution in a traffic situation with multiple participants present is a challenging problem …