A Comprehensive Survey on Anomaly Detection in Autonomous Vehicles with a Conceptual LOA ResNet Framework

DOI

10.2991/978-94-6239-616-6_9How to use a DOI?

Keywords

Autonomous Vehicles; Anomaly Detection; CNN–ResNet50; Lion Optimization Algorithm; Deep Learning; V2X Communication; Cybersecurity

Abstract

Autonomous vehicles (AVs) rely on Vehicle-to-Everything (V2X) communication to ensure safe and efficient navigation. However, the increasing sophistication of cyber-physical threats—such as Simple, Bad-Mouth, and Zig-Zag attacks—threatens the reliability and security of these systems. This paper presents a comprehensive review of anomaly detection techniques for autonomous vehicles, emphasizing deep learning and metaheuristic optimization methods proposed between 2022 and 2025. It explores models built on CNNs, LSTMs, and Transformers, along with hybrid optimization strategies such as the Lion Optimization Algorithm (LOA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO).The study compares these methods regarding accuracy, scalability, interpretability, and computational efficiency using benchmark datasets such as VeReMi, NS2, and NGSIM. It also discusses a conceptual framework that combines LOA with the CNN-ResNet50 architecture to tackle issues related to robustness and resource efficiency. This hybrid approach aims to improve hyperparameter tuning, feature optimization, and detection accuracy in dynamic and complex traffic conditions. The paper ends by highlighting open research challenges and future directions for lightweight, explainable, and multimodal anomaly detection systems in intelligent transportation.

Copyright

© 2026 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  - M. Lakshmi Prabha
AU  - G. Nishanth
AU  - T. Saravanane
AU  - G. Sarjith
PY  - 2026
DA  - 2026/03/31
TI  - A Comprehensive Survey on Anomaly Detection in Autonomous Vehicles with a Conceptual LOA ResNet Framework
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 110
EP  - 117
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_9
DO  - 10.2991/978-94-6239-616-6_9
ID  - Prabha2026
ER  -