| Efficient statistical validation with edge cases to evaluate highly automated vehicles D Karunakaran, S Worrall, E Nebot 2020 IEEE 23rd International Conference on Intelligent Transportation …, 2020 | 38 | 2020 |
| Efficient falsification approach for autonomous vehicle validation using a parameter optimisation technique based on reinforcement learning D Karunakaran, S Worrall, E Nebot arXiv preprint arXiv:2011.07699, 2020 | 10 | 2020 |
| Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data D Karunakaran, JS Berrio, S Worrall, E Nebot arXiv preprint arXiv:2203.07521, 2022 | 8 | 2022 |
| Parameterisation of lane-change scenarios from real-world data D Karunakaran, JS Berrio, S Worrall, E Nebot 2022 IEEE 25th International Conference on Intelligent Transportation …, 2022 | 4 | 2022 |
| Critical concrete scenario generation using scenario-based falsification D Karunakaran, JS Berrio, S Worrall, E Nebot 2022 IEEE International Conference on Recent Advances in Systems Science and …, 2022 | 3 | 2022 |
| Challenges of testing highly automated vehicles: A literature review D Karunakaran, JS Berrio, S Worrall, E Nebot 2022 IEEE International Conference on Recent Advances in Systems Science and …, 2022 | 2 | 2022 |
| Generating Edge Cases for Testing Autonomous Vehicles Using Real-World Data D Karunakaran, JS Berrio Perez, S Worrall Sensors 24 (1), 108, 2023 | 1 | 2023 |
| Concrete scenario generation with a focus on edge cases for the safety assessment of highly automated vehicles D Karunakaran | | 2023 |
Stewart WorrallACFR, University of SydneyVerified email at acfr.usyd.edu.au
