Autonomous Vehicles Scenario Testing Framework and Model of Computation
ISSN: 2574-0741, e-ISSN: 2574-075X
Published December 18, 2019 by SAE International in United States
Citation: Alnaser, A., Akbas, M., Sargolzaei, A., and Razdan, R., "Autonomous Vehicles Scenario Testing Framework and Model of Computation," SAE Intl. J CAV 2(4):205-218, 2019, https://doi.org/10.4271/12-02-04-0015.
Autonomous Vehicle (AV) technology has the potential to fundamentally transform the automotive industry, reorient transportation infrastructure, and significantly impact the energy sector. Rapid progress is being made in the core artificial intelligence engines that form the basis of AV technology. However, without a quantum leap in testing and verification, the full capabilities of AV technology will not be realized. Critical issues include finding and testing complex functional scenarios, verifying that sensor and object recognition systems accurately detect the external environment independent of weather conditions, and building a regulatory regime that enables accumulative learning. The significant contribution of this article is to outline a novel methodology for solving these issues by using the Florida Poly AV Verification Framework (FLPolyVF).