Print Email Facebook Twitter Adversarial Attacks against the Perception System of Autonomous Vehicles Title Adversarial Attacks against the Perception System of Autonomous Vehicles Author Gao, Yuxing (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control) Contributor Laurenti, L. (mentor) Zgonnikov, A. (mentor) Koyal, Koyal (mentor) Boer, Koen (mentor) Caesar, H.C. (graduation committee) Degree granting institution Delft University of TechnologyMechanical, Maritime and Materials Engineering Programme Mechanical Engineering | Systems and Control Date 2023-12-21 Abstract The rapid advancement in autonomous driving technology underscores the importance of studying the fragility of perception systems in autonomous vehicles, particularly due to their profound impact on public transportation safety. These systems are of paramount importance due to their direct impact on the lives of passengers and pedestrians. Additionally, their reliability can be easily compromised given the complexity and unpredictability of driving environments. However, current research and existing regulations often fail to adequately address the adversarial robustness of autonomous vehicle perception systems. This thesis delves into the adversarial robustness of camera-based perception systems of autonomous vehicles. Our research concentrates on developing and implementing evasion attacks that use black-box gradient estimation, as well as physical attacks in traffic sign detection and classification systems. Our findings indicate that even minor perturbations can impact the accuracy of these systems, leading to detection and classification errors. This finding highlights a critical vulnerability in the perception system's robustness against adversarial attacks. Moreover, the study extends to assess the transferability of adversarial examples across diverse perception models. Our results also expose significant gaps in the current regulatory frameworks of autonomous vehicles, necessitating the establishment of more rigorous and comprehensive safety standards. Subject Adversarial attacksAutonomous drivingPerceptionRobustness Evaluations To reference this document use: http://resolver.tudelft.nl/uuid:0a654db1-b867-4b25-b901-aea1fa63e17c Bibliographical note https://github.com/yuxing-gao/adversarial-attack-traffic-sign-detection Part of collection Student theses Document type master thesis Rights © 2023 Yuxing Gao Files PDF Adversarial_attacks_again ... hicles.pdf 15.16 MB Close viewer /islandora/object/uuid:0a654db1-b867-4b25-b901-aea1fa63e17c/datastream/OBJ/view