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.

中文翻译：

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使用 AI 技术检测自动驾驶电动汽车的异常情况：一项综合调查

智能交通的下一波浪潮旨在设计可再生能源，推动汽车行业转向自动电动汽车 (AEV)。AEV 是传感器驱动和无人驾驶，在车联网 (IoV) 生态系统中使用基于人工智能 (AI) 的交互。AEV 可以减少碳足迹并与同行 AEV、智能电网 (SG) 和路边单元 (RSU) 进行能源交易。它支持绿色交通愿景。然而，传感器信息、能量单元和用户数据是通过开放渠道交换的，因此容易受到各种安全和隐私攻击。因此，AEV 可以被恶意实体远程操作和指挥，这些恶意实体可以将错误的更新传播到 IoV 环境中的对等节点。这可能导致组件故障、拥塞、以及 IoV 网络的整个中断。全球研究人员和安全分析师已经解决了与特定安全要求相关的解决方案，但恶意 AEV 的检测和分类仍然是一个广泛研究的主题。恶意 AEV 表现出与正常 AEV 不同的异常行为，因此需要检测异常 AEV 并对异常类型进行分类。受上述事实的启发，该调查提出了人工智能技术在 AEV 异常检测中的系统展望。基于现有调查中的研究空白，提出了一种解决方案分类法，并讨论了基于人工智能的异常检测的评估指标。讨论了 AI 部署中的开放挑战和问题，并通过加权集成技术对异常分类进行了案例研究。因此，拟议的调查旨在指导制造业、人工智能从业者和全球研究人员制定和设计准确和精确的机制来检测异常。