  The safety of Automated Vehicles (AVs) must be assured before their release
and deployment. The current approach to evaluation relies primarily on (i)
testing AVs on public roads or (ii) track testing with scenarios defined in a
test matrix. These two methods have completely opposing drawbacks: the former,
while offering realistic scenarios, takes too much time to execute; the latter,
though it can be completed in a short amount of time, has no clear correlation
to safety benefits in the real world. To avoid the aforementioned problems, we
propose Accelerated Evaluation, focusing on the car-following scenario. The
stochastic human-controlled vehicle (HV) motions are modeled based on 1.3
million miles of naturalistic driving data collected by the University of
Michigan Safety Pilot Model Deployment Program. The statistics of the HV
behaviors are then modified to generate more intense interactions between HVs
and AVs to accelerate the evaluation procedure. The Importance Sampling theory
was used to ensure that the safety benefits of AVs are accurately assessed
under accelerated tests. Crash, injury and conflict rates for a simulated AV
are simulated to demonstrate the proposed approach. Results show that test
duration is reduced by a factor of 300 to 100,000 compared with the
non-accelerated (naturalistic) evaluation. In other words, the proposed
techniques have great potential for accelerating the AV evaluation process.
