Proposal of a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions – application: AEB system. Issue 9 (6th September 2018)

• Record Type: Journal Article
• Authors: Chelbi, Nacer Eddine; Gingras, Denis; Sauvageau, Claude
• Is Part Of: IET intelligent transport systems. Volume 12:Issue 9(2018)
• Page Start: 1148
• Page End: 1156
• Publication Date: 2018-09-06
• Subjects: Markov processes -- sampling methods -- driver information systems -- road vehicles -- expectation‐maximisation algorithm -- Bayes methods -- Monte Carlo methods; virtual evaluation approach -- preventive safety applications -- advanced driver assistance functions -- worst‐case scenarios -- given advanced driver assistance function -- field operational tests [safety pilot model deployment -- studied AEB system -- relevant tests scenarios -- test parameter -- kernel method -- expectation‐maximisation algorithm; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2018.5269
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : This study presents a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions. The approach identifies the worst‐case scenarios for a given advanced driver assistance function, AEB system in this study, based on field operational tests (FOT) [safety pilot model deployment (SPMD), in this study]. The authors begin with a description of the studied AEB system and a synthesis of the most relevant tests scenarios. Then, they model the distribution of each test parameter retrieved from the SPMD database by applying two estimation methods (kernel method and expectation‐maximisation algorithm). A comparison was made between the two methods to choose the best one. These distributions are then sampled using the proposed sampling strategy based on Metropolis‐Hastings algorithm. Then, the idea is to take the samples of each parameter retrieved with this sampler, simulate them on a vehicular software simulator (PreScan) and to get their simulation results. For each test and in case of impact, a proportional score to the speed of impact reduction is attributed. Finally, a risk classification is done based on the scoring results which allows to recover high and very high‐risk cases to build a set of worst‐case scenarios.