Evolving an intelligent vehicle for tactical reasoning in traffic

Abstract

Recent research in automated highway systems has ranged from low-level vision-based controllers to high-level route-guidance software. However there is currently no system for tactical-level reasoning. Such a system should address tasks such as passing cars, making exits on time, and merging into a traffic stream. Our approach to this intermediate-level planning combines a distributed reasoning system (PolySAPIENT) with a novel evolutionary optimization strategy (PBIL). PBIL automatically tunes PolySAPIENT module parameters in simulation by evaluating candidate modules on various traffic scenarios. Since the control interface to the simulated vehicles is identical to that on the Carnegie Mellon Navlab vehicles, modules developed using this process can be directly ported to existing hardware. This method is currently being applied to the automated highway system domain; it also generalizes to many complex robotics tasks where multiple interacting modules must simultaneously be configured without individual module feedback.