Situational Awareness for Driving in Traffic A Thesis Proposal

Situational awareness is the key to effective navigation in traffic. Although existing robot systems are capable of robustly performing many elements of the navigation task (e.g. road following and car tracking), integrating these aspects into a competent driving system requires a higher-level understanding of the driving domain. I propose to build a reasoning system, SAPIENT (Situational Awareness Planner Implementing Effective Navigation in Traffic) that understands the tactical driving task. Defensive driving heuristics, occlusion reasoning, map-based reasoning and experience-based prediction of traffic flows will be captured in reasoning objects. Explicit representations of intentions (higher-level goals)will be used to create and destroy objects in response to changing situations. In a fully autonomous implementation, these intentions will be generated by higher level reasoning modules, while in the driver ’s associate application, they will be inferred from the driver ’s behavior. A loosely coupled architecture will enable SAPIENT to address higher level goals while reacting intelligently to sensor information. SAPIENT will interact with existing modules such as road followers, car trackers and low level perception systems. Separate client modules will interface SAPIENT to higher level planners (both human and robot). I will build two representative clients: an autonomous driving module and a simple driver ’s associate. A custom simulator with realistic sensor modeling will be used for development and SAPIENT will be tested on a set of driving scenarios extracted from real-life examples. Controlled experiments with the NAVLAB II will also demonstrate SAPIENT’s competence in limited scenarios on real roads.