Title
A Validation Methodology for the Minimization of Unknown Unknowns in Autonomous Vehicle Systems
Abstract
Deployment of SAE Level 3+ automated vehicles faces validation and certification challenges due to uncertainty and state space size of the operating domain. We propose a validation and testing methodology that aims to minimize unknown unknowns through minimization of scenarios that have not been accounted for, and scenarios that have not been identified due to modeling deficiencies. The methodology utilizes simulators with different levels of fidelity for residual risk handling, functional hierarchies for simplification of complex navigation tasks, and the Backtracking Process Algorithm to identify scenarios of risk significance. The methodology is demonstrated on a scenario with an intersection preceded by a traffic light. Through use of the testing flowchart, we were able to identify and remedy scenarios leading to undesirable events.
Year
DOI
Venue
2020
10.1109/IV47402.2020.9304616
2020 IEEE Intelligent Vehicles Symposium (IV)
Keywords
DocType
ISSN
automated vehicles,certification challenges,state space size,operating domain,testing methodology,unknown unknowns,minimization,residual risk handling,validation methodology,autonomous vehicle systems,SAE level 3+ automated vehicles,backtracking process algorithm,testing flowchart
Conference
1931-0587
ISBN
Citations 
PageRank 
978-1-7281-6674-2
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Mohammad Hejase100.34
Mathieu Barbier211.07
Ümit Özgüner31014166.59
Javier Ibañez-Guzmán411312.84
Tankut Acarman56023.65