Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

We propose an integrated prediction and planning system for autonomous driving which
uses rational inverse planning to recognise the goals of other vehicles. Goal recognition …

Autonomous vehicles face numerous challenges to ensure safe operation in unpredictable
and hazardous conditions. The autonomous driving environment is characterized by high …

Generating safe and non-conservative behaviors in dense, dynamic environments remains
challenging for automated vehicles due to the stochastic nature of traffic participants' …

As vehicle automation advances, motion planning algorithms face escalating challenges in
achieving safe and efficient navigation. Existing Advanced Driver Assistance Systems …

In this review, we provide an overview of emerging trends and challenges in the field of
intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of …

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 …

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 …

Learning-based approaches to autonomous vehicle planners have the potential to scale to
many complicated real-world driving scenarios by leveraging huge amounts of driver …

End-to-end motion planners have shown great poten-tial for enabling fully autonomous
driving. However, when facing out-of-distribution (OOD) events, these planners might not …