Situation Awareness for Tactical Driving

A primary challenge to creating an intelligent vehicle that can competently drive in traffic is the task of tactical reasoning: deciding which maneuvers to perform in a particular driving situation, in real-time, given incomplete information about the rapidly changing traffic configuration. Human expertise in tactical driving is attributed to situation awareness, a task-specific understanding of the dynamic entities in the environment, and their projected impact on the agent’s actions. In this thesis, I demonstrate how situation awareness may be used as a basis for tactical-level reasoning in intelligent vehicles. SAPIENT (Situation Awareness Planner Implementing Effective Navigation in Traffic) combines a knowledge of high-level driving goals with low-level reactive behavior to control vehicles in a custom tactical-level simulator, SHIVA. The simulated vehicles are based on the Carnegie Mellon Navlabs, sharing a common perception and control interface, allowing researchers to port systems from simulation to real life with minimal modification. The first implementation, MonoSAPIENT, uses explicitly encoded rules for competent driving, along with specialized algorithms for gap selection and lane changing to drive safely in the simulated world. The second implementation, PolySAPIENT, is a distributed intelligence, built around the notion of reasoning objects, independent experts, each specializing in a single aspect of the driving domain. Each reasoning object is associated with an observed traffic entity, such as a nearby vehicle or an upcoming exit, and examines the projected interactions of that entity on the agent’s proposed actions. Thus, a reasoning object associated with a vehicle is responsible for preventing collisions, while one associated with a desired exit recommends those actions that will help maneuver the vehicle to the exit. The results are expressed as votes and vetoes over a tactical action space of available maneuvers, and are used by a domainindependent arbiter to select the agent’s next action. This loose coupling avoids the complex interactions common in traditional architectures, and also allows new reasoning objects to be easily added to an existing PolySAPIENT system. I also introduce a new learning strategy, based on the PBIL evolutionary algorithm, that simultaneously optimizes internal parameters for multiple reasoning objects given a user-specified evaluation metric. This automated parameter exploration also enables rapid prototyping of new PolySAPIENT configurations.