Paper Info
Title
Adaptive Design of Experiments for Safety Evaluation of Automated Vehicles
Abstract
Automated Vehicles (AVs) need to be thoroughly evaluated in order to ensure their driving capabilities. However, comprehensive evaluations are intractable due to both time and monetary costs. To address this problem, we propose an Adaptive Design of Experiments (ADOE) method to evaluate the safety of AVs. Using this method, a Surrogate Model (SM) is established and updated iteratively. SM in ADOE is used to approximate the results of AV testing and help to select the next concrete scenario to be tested in each iteration. Two different ADOE approaches are proposed in this study for different testing purposes. Since the choice of the surrogate model has a profound impact on the performance of the ADOE method, 6 surrogate models were compared with two logical scenarios at different scales – a car following logical scenario and a cut-in logical scenario. Results show that Extreme Gradient Boosting (XGB) is suitable for both ADOE approaches. And both proposed ADOE approaches achieved desired performance. Scenario-oriented ADOE made full use of each concrete scenario, capturing one collision case for every 1.12 test runs in the car following logical scenario, while SM-oriented ADOE successfully depicted the boundary between safety and danger. Using 0.46% test resources compared to enumeration, the SM-oriented ADOE found 93.9% dangerous scenarios with 90.9% precision. ADOE approaches have great potential in accelerating the evaluation of AV safety.
Year
DOI
Venue
2022
10.1109/TITS.2021.3130040
IEEE Transactions on Intelligent Transportation Systems
Keywords
DocType
Volume
Autonomous vehicles,design of experiments,motion planning,vehicle crash testing
Journal
23
Issue
ISSN
Citations 
9
1524-9050
1
PageRank 
References 
Authors
0.38
18
5
Name
Order
Citations
PageRank
Jian Sun16014.76
Huajun Zhou240.83
Haochen Xi310.38
He Zhang440.83
Ye Tian540.83