Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. Many existing approaches to this problem strive to distill all possible vehicle behaviors into a simplified set of high-level actions. However, these action categories do not suffice to describe the full range of maneuvers possible in the complex road networks we encounter in the real world. To combat this deficiency, we propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road actors. We show that our approach is able to accurately predict future occupancy that remains consistent with the mapped lane geometry and naturally captures multi-modality based on the local scene context while also not suffering from the mode collapse problem observed in prior work.

Trust in automation is a crucial ingredient for successful human robot interaction. Both human related and robot related factors influence the user's trust on the robot and it is challenging to characterize each of these factors and study how they affect human trust. In this study we try to understand how blame attribution after an error impacts user trust. Three different robot personalities were implemented, each assigning blame to either of the user, the robot itself, or the human-robot team. Our study results confirm that blame attribution impacts human trust in robots.

It is challenging to quantitatively measure a user's trust in a robot system using traditional survey methods due to their invasiveness and tendency to disrupt the flow of operation. Therefore, we analyzed data from an existing experiment to identify measures which (1) have face validity for measuring trust and (2) align with the collected post-run trust measures. Two measures are promising as real-time indications of a drop in trust. The first is the time between the most recent warning and when the participant reduces the robot's autonomy level. The second is the number of warnings prior to the reduction of the autonomy level.