Safety Performance of Autonomous Driving Systems Based on Artificial Intelligence

DOI

10.2991/978-94-6463-518-8_30How to use a DOI?

Keywords

Artificial Intelligence; Autonomous Driving; Safety Performance; Adversarial Algorithms; Dataset Balance

Abstract

Safety incidents involving autonomous driving are on the rise with the increasing integration of artificial intelligence (AI) in this sector. Consequently, research on the safety performance of AI-based autonomous driving systems is gaining significance. This study delves into relevant security incidents, scrutinizes autonomous driving AI systems from the angles of adversarial attacks and dataset balance, and proposes a security performance assessment platform for adversarial attack algorithms to enhance the safety performance of autonomous driving AI systems. The analysis of safety accident cases elucidates various factors contributing to accidents, including erroneous decision-making due to misinterpretation of visual cues by AI systems, adversarial attacks introducing undetectable noise to input data, and system misidentification of objects. Proposed methods for improving safety performance encompass strategies such as proactive detection of data anomalies, development of robust data distribution frameworks, and implementation of defense mechanisms against adversarial attacks. Additionally, the study underscores the necessity for comprehensive evaluation platforms to assess the safety performance of AI systems in autonomous driving thoroughly. By addressing these issues, advancements in AI technology can be harnessed to ensure safer autonomous driving experiences, thereby mitigating risks and enhancing overall transportation safety. As AI technology continues to evolve, addressing safety concerns will remain paramount to realizing the full potential of autonomous driving for societal benefit.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Fan Chen
PY  - 2024
DA  - 2024/09/28
TI  - Safety Performance of Autonomous Driving Systems Based on Artificial Intelligence
BT  - Proceedings of the 2024 International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2024)
PB  - Atlantis Press
SP  - 301
EP  - 311
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-518-8_30
DO  - 10.2991/978-94-6463-518-8_30
ID  - Chen2024
ER  -