Worst-case scenarios identification approach for the evaluation of advanced driver assistance systems in intelligent/autonomous vehicles under multiple conditions. Issue 3 (4th May 2022)

• Record Type: Journal Article
• Authors: Chelbi, Nacer Eddine; Gingras, Denis; Sauvageau, Claude
• Is Part Of: Journal of intelligent transportation systems. Volume 26:Issue 3(2022)
• Page Start: 284
• Page End: 310
• Publication Date: 2022-05-04
• Subjects: Advanced driver assistance systems (ADAS) -- ensemble and machine learning -- evaluation and validation -- intelligent and autonomous vehicles -- worst-case scenarios; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2020.1853538
• Languages: English
• ISSNs: 1547-2450

Abstract

Abstract: To demonstrate the expected performance of an advanced driver assistance system (ADAS) in an intelligent or a highly automated vehicle test approaches should include a combination of simulations, track tests, and road tests. The main objective of our work was to propose a new evaluation approach conducted by an external entity where the vehicle is treated as a black box. This approach allowed for the identification of a set of worst-case scenarios for a given ADAS application and combined the three test approaches mentioned earlier. Our proposed evaluation approach is broken down into three parts: (1) Scenarios synthesis, sampling strategy, and simulations, (2) Risk assessment and classification, and (3) Validation. In this article, we present our proposed approach for the validation portion. The validation portion can be further broken down into three parts. The first step included a description of the studied autonomous emergency braking (AEB), physical track tests, and of the different machines and ensemble learning techniques employed to create the predictive model. The second part utilized the Field Operational Tests database (SPMD), to implement the new sampling strategy based on the original, modified, and multivariate Metropolis-Hastings algorithm. The third part focused on collecting the prediction results, then assess the risk of each test, to classify them using a non-supervised technique (k-Means clustering). This allowed us to build a set of worst-case scenarios to make a final selection. Finally, two web applications were developed and deployed.