Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey. Issue 5 (14th June 2021)

• Record Type: Journal Article
• Authors: Dixit, Palak; Bhattacharya, Pronaya; Tanwar, Sudeep; Gupta, Rajesh
• Other Names: Sharma Rohit guestEditor.; Gupta Deepak guestEditor.; Maseleno Andino guestEditor.; Peng Sheng‐Lung guestEditor.; Menon Varun G. guestEditor.; Khosravi Reza guestEditor.; Jolfaei Alireza guestEditor.; Kumar Akshi guestEditor.; P Vinod guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-14
• Subjects: anomalous classes -- anomaly detection -- artificial intelligence -- autonomous electric vehicles -- deep learning -- machine learning -- reinforcement learning; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12754
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The next wave in smart transportation is directed towards the design of renewable energy sources that can fuel automobile sector to shift towards the autonomous electric vehicles (AEVs). AEVs are sensor‐driven and driverless that uses artificial intelligence (AI)‐based interactions in Internet‐of‐vehicles (IoV) ecosystems. AEVs can reduce carbon footprints and trade energy with peer AEVs, smart grids (SG), and roadside units (RSUs). It supports green transportation vision. However, the sensor information, energy units, and user data are exchanged through open channels, and thus, are susceptible to various security and privacy attacks. Thus, AEVs can be remotely operated and directed by malicious entities that can propagate false updates to the peer nodes in IoV environment. This can cause the failure of components, congestion, as well as the entire disruption of IoV network. Globally researchers and security analysts have addressed solutions that pertain to specific security requirements, but still, the detection and classification of malicious AEVs is a widely studied topic. Malicious AEVs exhibit an anomaly behavior that differentiates them from normal AEVs, and thereby, the detection of anomalous AEVs and classification of anomaly type is required. Motivated from the aforementioned facts, the survey presents a systematic outlook of AI techniques in anomaly detection of AEVs. A solution taxonomy is proposed based on research gaps in the existing surveys, and the evaluation metrics for AI‐based anomaly detection are discussed. The open challenges and issues in AI deployments are discussed and a case study is presented on anomaly classification through a weighted ensemble technique. Thus, the proposed survey is designed to guide the manufacturing industry, AI practitioners, and researchers worldwide to formulate and design accurate and precise mechanisms to detect anomalies.