A computational model of driving for autonomous vehicles

Driving models are needed by many researchers to improve traffic safety and to advance autonomous vehicle design. To be most useful, a driving model must state specifically what information is needed and how it is processed. Such models are called computational because they tell exactly what computations the driving system must carry out; To date, detailed computational models have primarily been developed for research in robot vehicles. Other driving models are generally too vague or abstract to show the driving process in full detail. However, the existing computational models do not address the problem of selecting maneuvers in a dynamic traffic environment. In this paper we present a dynamic task analysis and use it to develop a computational model of driving in traffic. This model has been implemented in a driving program called Ulysses as part of our research program in robot vehicle development. Ulysses shows how traffic and safety rules constrain the vehicle's acceleration and lane use, and shows exactly where the driver needs to look at each moment as driving decisions are being made. Ulysses works in a simulated environment provided by our new traffic simulator called PHAROS, which is similar in spirit to previous simulators (such as NETSIM) but far more detailed. Our new driving model is a key component for developing autonomous vehicles and intelligent driver aids that operate in traffic, and provides a new tool for traffic research in general. This research was sponsored by the Avionics Lab, Wright Research and Development Center, Aeronautical Systems Division (AFSC), U.S. Air Force, Wright-Patterson AFB, OH 45433-6543 under Contract F33615-90-C-1465, Arpa Order No. 7597 and Contract DACA76-85-C-0003. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government.