Felix Batsch

Felix Batsch

Doctoral Researcher in Machine Learning and Autonomous Driving

Posts by Collection

portfolio

publications

Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

Published in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019

Safety is an essential aspect in the facilitation of automated vehicle deployment. We propose an approach to identify the performance boundary, where corner case scenarios are located, using Gaussian Process Classification.

Recommended citation: Batsch, F., Daneshkhah, A., Cheah, M., Kanarachos, S., & Baxendale, A. (2019). Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 419–424. https://doi.org/10.1109/ITSC.2019.8917119 https://arxiv.org/pdf/1907.05364.pdf

A taxonomy of validation strategies to ensure the safe operation of highly automated vehicles

Published in Journal of Intelligent Transportation Systems, 2020

In this study, we systematically review literature that proposes new methods for these areas. The available methods were categorized into a novel taxonomy, dividing them into the strategies of combinatorial testing, robustness testing and search-based testing.

Recommended citation: Batsch, F., Kanarachos, S., Cheah, M., Ponticelli, R., & Blundell, M. (2022). A taxonomy of validation strategies to ensure the safe operation of highly automated vehicles. Journal of Intelligent Transportation Systems, 26(1), 14–33. https://doi.org/10.1080/15472450.2020.1738231 http://felixbat.github.io/files/20200320_JITS_TaxonomyValidationStrategies.pdf

Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes

Published in Applied Sciences, 2021

We make use of the scenario-based testing approach and propose a method to model simulated scenarios using Gaussian Process based models to predict untested scenario outcomes. This enables us to efficiently determine the performance boundary, where the safe and unsafe scenarios can be evidently distinguished from each other. We present an iterative method that optimises the parameter space of a logical scenario towards the most critical scenarios on this performance boundary.

Recommended citation: Batsch, F., Daneshkhah, A., Palade, V., & Cheah, M. (2021). Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes. Applied Sciences, 11(2), 775. https://doi.org/10.3390/app11020775 http://felixbat.github.io/files/2020115_AppSc_ScenarioOptimisation.pdf

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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