  Automated vehicles (AVs) must be evaluated thoroughly before their release
and deployment. A widely-used evaluation approach is the Naturalistic-Field
Operational Test (N-FOT), which tests prototype vehicles directly on the public
roads. Due to the low exposure to safety-critical scenarios, N-FOTs are
time-consuming and expensive to conduct. In this paper, we propose an
accelerated evaluation approach for AVs. The results can be used to generate
motions of the primary other vehicles to accelerate the verification of AVs in
simulations and controlled experiments. Frontal collision due to unsafe cut-ins
is the target crash type of this paper. Human-controlled vehicles making unsafe
lane changes are modeled as the primary disturbance to AVs based on data
collected by the University of Michigan Safety Pilot Model Deployment Program.
The cut-in scenarios are generated based on skewed statistics of collected
human driver behaviors, which generate risky testing scenarios while preserving
the statistical information so that the safety benefits of AVs in
non-accelerated cases can be accurately estimated. The Cross Entropy method is
used to recursively search for the optimal skewing parameters. The frequencies
of occurrence of conflicts, crashes and injuries are estimated for a modeled
automated vehicle, and the achieved accelerated rate is around 2,000 to 20,000.
In other words, in the accelerated simulations, driving for 1,000 miles will
expose the AV with challenging scenarios that will take about 2 to 20 million
miles of real-world driving to encounter. This technique thus has the potential
to reduce greatly the development and validation time for AVs.
