It is necessary to thoroughly evaluate the safety of Automated Vehicles (AVs)  before their release and deployment. Current evaluation approach mainly relies on i) testing AVs on public roads or  ii) track testing with scenarios defined in a test matrix. These two methods have completely opposite drawbacks: the former  takes too much time to execute but is realistic ; the latter can be finished in a short time but has no clear correlation to the safety benefits in the real world. To avoid the aforementioned problems, we propose the Accelerated Evaluationapproach focusing on the car-following scenario. The stochastic human-controlled vehicle (HV) motions were 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 were 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 the 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 to accelerate the AV evaluation process.
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.
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