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Model-based Falsification and Safety Evaluation of Autonomous Systems

Capito Ruiz, Linda Jenny
http://rave.ohiolink.edu/etdc/view?acc_num=osu1692352687338805

Abstract Details

2023, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Autonomous vehicles (AVs) have the potential to revolutionize transportation safety. However, there is no consensus yet on how to effectively evaluate the safety of self-driving cars. This dissertation addresses the challenge of safety evaluation for AVs by integrating concepts from vehicle and traffic modeling, control theory, optimization, and both naturalistic and simulation-based data-driven methods. An alternative to the exhaustive testing of a system under all environmental and operational configurations are adaptive adversarial approaches, which primarily aim to expose the vehicle to safety-critical situations, also known as 'Falsification'. This dissertation evaluates the effectiveness of these algorithms, and creates a unified approach for generating adversarial testing algorithms and conducting safety analysis. We contribute to the model-based falsification task by ensuring theoretical guarantees under standard assumptions. This involves considering the environment as a gray-box, where its dynamics are partially known, and approximating the unknown model of the autonomous system. Preliminary works used deterministic and expert models, but this dissertation treats them as stochastic systems by incorporating a naturalistic behavior fitting. We make thee contributions to the safety analysis task. First, a systems' safety engineering approach is proposed for hazard analysis that considers the operational requirements from various safety standards. Second, a dynamic probabilistic assessment approach is presented for risk assessment, involving a Backtracking Process Algorithm (BPA), traditionally based on a discretized cell-to-cell probabilistic state transition mapping, for the probabilistic quantification of hazardous events. We propose using a sticky Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) for estimating system transition probabilities, aiming to reduce computational burden and allow meaningful state and transition identifications for risk assessment. Lastly, a formal method approach is proposed, which provides provably unbiased autonomous vehicle safety metrics. The proposed methods have been extensively tested across various scenarios for both ground and aerial vehicles. They can be used collectively or individually for testing and evaluating autonomous systems. These methods are anticipated to contribute significantly to discussions around potential testing standards, aiding regulatory agencies and private companies. The ultimate goal is to advance the deployment of safer autonomous systems.
Keith Redmill (Advisor)
Saeedeh Ziaeefard (Committee Member)
Mrinal Kumar (Committee Member)
Ümit Özgüner (Committee Member)
189 p.
Automotive Engineering; Computer Engineering; Electrical Engineering; Robotics
Falsification; safety evaluation; safety analysis; testing; risk assessment; control systems; data-driven methods; autonomous systems; driving systems; unmanned aerial systems

Recommended Citations

Citations

  • Capito Ruiz, L. J. (2023). Model-based Falsification and Safety Evaluation of Autonomous Systems [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1692352687338805

    APA Style (7th edition)

  • Capito Ruiz, Linda. Model-based Falsification and Safety Evaluation of Autonomous Systems. 2023. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1692352687338805.

    MLA Style (8th edition)

  • Capito Ruiz, Linda. "Model-based Falsification and Safety Evaluation of Autonomous Systems." Doctoral dissertation, Ohio State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=osu1692352687338805

    Chicago Manual of Style (17th edition)

osu1692352687338805
140
© 2023, some rights reserved.Creative Commons License Model-based Falsification and Safety Evaluation of Autonomous Systems by Linda Jenny Capito Ruiz is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.
Release 3.2.12