Symbolic Representations for Autonomous Vehicle Perception and Control in Urban Environments

In this paper, we describe our recent efforts on symbolic representations enabling autonomous driving in dynamic environments such as on-road driving. In contrast to traditional bottom-up approaches where temporal accumulation of locally insignificant inaccuracies causes eventual failures in high-level scene interpretation, our proposed knowledge-driven top-down approach combined with vehicle´s intentions can provide valuable information to guide low-level bottom-up tasks and vice-versa. We contend that such rich symbolic representations can reduce the burden on sensory processing thereby dynamically directing it to look for particular features in expected locations and subsequently facilitating the vehicle to better react to potentially dangerous situations, such as the appearance of pedestrians in the road. We demonstrate the proposed approaches in various scenarios pertaining to vehicle perception and control using field data obtained from a military unmanned ground vehicle (UGV) traversing urban environments