In urban driving scenarios, autonomous vehicles are expected to conform to traffic rules
covering traffic lights, traversable and non-traversable traffic lines, etc. In this article, we …

In this article, we propose an optimization-based integrated behavior planning and motion
control scheme, which is an interpretable and adaptable urban autonomous driving solution …

Trajectory planning and control have historically been separated into two modules in
automated driving stacks. Trajectory planning focuses on higher-level tasks like avoiding …

Autonomous driving has been gaining more and more attention in the last decades, thanks
to its positive social-economic impacts including the enhancement of traffic efficiency and …

Trajectory planning in urban automated driving is challenging because of the high
uncertainty resulting from the unknown future motion of other traffic participants. Robust …

Autonomous driving is achieved through environment recognition, path planning, and path
tracking control. To achieve safe driving, it is necessary to increase the performance of these …

Safety and efficiency are two defining factors for autonomous vehicles. While there is
already extensive literature on how to safely cope with highway situations, approaches for …

In this work, we propose a novel learning-based online model predictive control (MPC)
framework for motion synthesis of self-driving vehicles. In this framework, the decision …

We propose a data-driven control framework for autonomous driving which combines
learning-based risk assessment with personalized, safety-focused, predictive control …

Autonomous driving in urban environments requires safe control policies that account for the
non-determinism of moving obstacles, for instance, the intention of other vehicles while …