Abstract

As an ever-growing number of autonomous vehicles are deployed on public roads, developing robust decision-making algorithms becomes increasingly important. To enable better decision making, recent research efforts have focused on detecting, modelling, and predicting the behavior of traffic participants. The first aim of this workshop is to discuss recent advances, open research challenges, and future directions for robust decision making for autonomous driving. This includes the requirements and advances for situation-awareness pipelines to enable robust decision making, the scientific challenges involved in integration of these pipelines, and the decision-making aspects involved towards enabling robust interactive autonomy. The second aim of this workshop, is to discuss the interplay between prediction and planning, given the multi-agent nature of driving. ‘Socially-aware’ motion planning w.r.t. forecasting models of other agents is often often necessary, but also vice-versa; forecasting w.r.t. plans, to anticipate how a robot’s plan will likely affect surrounding drivers. Given the increasing amount of interest in this area in robotics, computer vision, and machine learning communities, we hope this workshop can be a suitable venue to promote further discussion and developments in this area.Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellusac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam.