A Data-Driven Test Scenario Generation Framework for AD/ADAS-Enabled Vehicles

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A Data-Driven Test Scenario Generation Framework for AD/ADAS-Enabled Vehicles

Bhatta, Niraja Narayan / Nair, Binoy B.

Advanced Driver Assistance System (ADASAdvanced Driver Assistance System (ADAS))-enabled vehicles capable of operating at SAE J 3016—level 3 or higher extensively employ deep learningDeep Learning-based systems for realizing ADASAdvanced Driver Assistance System (ADAS) features. Such systems need to be validated with all possible scenarios that could unfold during a trip. In this paper, a novel data-driven methodology to automatically generate relevant driving scenarios based on the operational design domain (ODD) in which the vehicle being driven is proposed; variables that are necessary to evaluate the functionality of an AD/ADASAdvanced Driver Assistance System (ADAS) vehicle are identified and a process to quantify the relevance of each of these variables is proposed using a combination of statistical framework such as CatBoostCatboost, Weight of Evidence (WoE), and Information Value (IV)Weight of Evidence & Information Value. An analytical framework is then developed on the basis of analytical hierarchy process (AHP) using real-world accident scenarios data to automatically generate test scenarios. The framework proposed in this study can be adapted to generate relevant test scenarios given the variables in that ODD.

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Dokumentinformationen

Titel :

A Data-Driven Test Scenario Generation Framework for AD/ADAS-Enabled Vehicles

Weitere Titelangaben:

Lect. Notes Electrical Eng.

Beteiligte:

Sharma, Sanjay (Herausgeber:in) / Subudhi, Bidyadhar (Herausgeber:in) / Sahu, Umesh Kumar (Herausgeber:in) / Bhatta, Niraja Narayan (Autor:in) / Nair, Binoy B. (Autor:in)

Kongress:

International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022

Erschienen in:

Intelligent Control, Robotics, and Industrial Automation ; Kapitel : 6 ; 69-81

Lecture Notes in Electrical Engineering ; 1066

Erscheinungsdatum :

2023-11-18

Format / Umfang :

13 pages

ISBN :

978-981-99-4634-1 , 978-981-99-4633-4

ISSN :