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ISBN 978-3-8439-4256-0

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Malte Mauritz
Engineering of Safe Autonomous Vehicles through Seamless Integration of System Development and System Operation

370 Seiten, Dissertation Technische Universität Clausthal (2019), Softcover, A4

Zusammenfassung / Abstract

One of the significant open challenges is the lack of verification and validation approaches for assuring the safety of autonomous vehicles. The vast number of realworld traffic situations have to be considered in the verification and validation. Today’s conventional engineering methods are not adequate for providing such guarantees for autonomous vehicles in a cost-efficient way. One strategy for reducing the costs of quality assurance is transferring a significant part of the verification and validation from road tests to (system-level) simulations.

Extensive coverage of real-world situations in simulations requires the integration of development and operation. This thesis presents an engineering approach that integrates the development and operation of autonomous vehicles seamlessly using runtime monitoring. The runtime monitoring verifies if autonomous vehicles satisfy their requirements and operate within safe limits which have been verified in the simulations.

Systematic and comprehensive simulations support the improvement of autonomous vehicles and coverage of traffic situations. Results of the runtime monitoring during operation are transferred to the development for the verification of autonomous vehicles and their safe limits in simulations with additional traffic situations.

The incomplete verification of autonomous vehicles for the vast number of real-world traffic situations in simulations requires the validation of simulation results and additional monitoring in the real world. Results from simulations are transferred to the runtime monitoring during operation in the real world.

Vehicle data and real-world situations possess high complexities and, therefore, impact the complexity and efficiency of the verification in simulations. The runtime monitoring abstracts from internal data of autonomous vehicles and real-world situations in the evaluation.