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Extracting V2V Encountering Scenarios from Naturalistic Driving Database
It is necessary to thoroughly evaluate the effectiveness and safety of C...
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Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies
Testing and evaluation is a critical step in the development and deploym...
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An Interaction-aware Evaluation Method for Highly Automated Vehicles
It is important to build a rigorous verification and validation (V V) ...
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Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification
Safety is an essential aspect in the facilitation of automated vehicle d...
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An Accelerated Approach to Safely and Efficiently Test Pre-produced Autonomous Vehicles on Public Streets
Various automobile and mobility companies, for instance, Ford, Uber, and...
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Model Predictive Instantaneous Safety Metric for Evaluation of Automated Driving Systems
Vehicles with Automated Driving Systems (ADS) operate in a high-dimensio...
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A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods
Evaluation and validation of complicated control systems are crucial to ...
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Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers
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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