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Ontology-based test generation for automated and autonomous driving functions

  • Published:2020-01 Issue: Volume:117 Page:106200
  • ISSN:0950-5849
  • Container-title:Information and Software Technology
  • language:en
  • Short-container-title:Information and Software Technology

Author:

Li Yihao,Tao Jianbo,Wotawa Franz

Funder

Ministry of Transport

Publisher

Elsevier BV

Subject

Computer Science Applications,Information Systems,Software

Reference48 articles.

1. Challenges in autonomous vehicle testing and validation;Koopman;SAE Int. J. Trans. Saf.,2016

2. Driving to safety: how many miles of driving would it take to demonstrate autonomous vehicle reliability?;Kalra;Transp. Res. Part A,2016

3. Scenarios for Development, Test and Validation of Automated Vehicles;Menzel,2018

4. Testing autonomous and highly configurable systems: Challenges and feasible solutions;Wotawa,2017

5. From ontologies to input models for combinatorial testing;Wotawa,2018

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1. Industry Practices for Challenging Autonomous Driving Systems with Critical Scenarios;ACM Transactions on Software Engineering and Methodology;2024-01-11

2. Al and Autonomous Systems: An Experiment in Industry 5.0 Transformation;BIO Web of Conferences;2024

3. Research on Specific Scenario Generation Methods for Autonomous Driving Simulation Tests;World Electric Vehicle Journal;2023-12-19

4. SceGAN: A Method for Generating Autonomous Vehicle Cut-In Scenarios on Highways Based on Deep Learning;Journal of Intelligent and Connected Vehicles;2023-12

5. From Collision to Verdict: Responsibility Attribution for Autonomous Driving Systems Testing;2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE);2023-10-09
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