Although autonomous driving techniques have achieved great improvements, challenges
still exist in decision making for variety of different scenarios under uncertain and interactive …

While motion planning approaches for automated driving often focus on safety and
mathematical optimality with respect to technical parameters, they barely consider …

Autonomous vehicles (AVs) need to interact with other traffic participants who can be either
cooperative or aggressive, attentive or inattentive. Such different characteristics can lead to …

Cooperative driving behavior is essential for driving in traffic, especially for ramp merging,
lane changing or navigating intersections. Autonomous vehicles should also man-age these …

Urban autonomous driving is both complex and dangerous. A multitude of road types, road
users, and potential traffic rule violations all make it challenging for an autonomous vehicle …

The learning from intervention (LfI) approach has been proven effective in improving the
performance of RL algorithms; nevertheless, existing methodologies in this domain tend to …

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 …

Sampling-based motion planning methods are widely adopted in autonomous driving.
Typically, sampling can be decoupled into two layers: a path sampling layer and a speed …

We explore the complex design space of behaviour planning for autonomous driving.
Design choices that successfully address one aspect of behaviour planning can critically …

Autonomous vehicles are believed to reduce the number of deaths and casualties on the
roads while improving the traffic efficiency. However, before their mass deployment on open …