Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations enabled by Vehicle-to-Everything (V2X) communication. However, the distributed nature of platoon coordination creates security vulnerabilities, allowing authenticated vehicles to inject falsified kinematic data, compromise operational stability, and pose a threat to passenger safety. Traditional misbehaviour detection approaches, which rely on plausibility checks and statistical methods, suffer from high False Positive (FP) rates and cannot capture the complex temporal dependencies inherent in multi-vehicle coordination dynamics. We present Attention In Motion (AIMformer), a transformer-based framework specifically tailored for real-time misbehaviour detection in vehicular platoons with edge deployment capabilities. AIMformer leverages multi-head self-attention mechanisms to simultaneously capture intra-vehicle temporal dynamics and inter-vehicle spatial correlations. It incorporates global positional encoding with vehicle-specific temporal offsets to handle join/exit maneuvers. We propose a Precision-Focused (BCE) loss function that penalizes FPs to meet the requirements of safety-critical vehicular systems. Extensive evaluation across 4 platoon controllers, multiple attack vectors, and diverse mobility scenarios demonstrates superior performance ($\geq$ 0.93) compared to state-of-the-art baseline architectures. A comprehensive deployment analysis utilizing TensorFlow Lite (TFLite), Open Neural Network Exchange (ONNX), and TensorRT achieves sub-millisecond inference latency, making it suitable for real-time operation on resource-constrained edge platforms. Hence, validating AIMformer is viable for both in-vehicle and roadside infrastructure deployment.

翻译：车辆编队通过车联网（V2X）通信实现多车协同，有望在交通效率与安全性方面带来变革性提升。然而，编队协调的分布式特性存在安全漏洞，已认证车辆可能注入伪造的运动学数据，破坏运行稳定性并威胁乘客安全。传统的异常行为检测方法依赖合理性检验与统计手段，存在较高的误报率（FP），且难以捕捉多车协调动态中固有的复杂时序依赖关系。本文提出AIMformer（运动注意力框架），这是一种专为车辆编队实时异常行为检测设计的基于Transformer的框架，具备边缘部署能力。AIMformer利用多头自注意力机制，同步捕捉车辆内部时序动态与车辆间空间关联性。该框架融合全局位置编码与车辆特定时序偏移量，以处理车辆加入/退出编队的动态过程。我们提出一种聚焦精度的二元交叉熵损失函数，通过惩罚误报来满足安全关键型车辆系统的需求。在4种编队控制器、多重攻击向量及多样化移动场景下的广泛评估表明，相较于现有先进基线架构，本框架展现出更优性能（F1分数≥0.93）。基于TensorFlow Lite（TFLite）、开放神经网络交换格式（ONNX）及TensorRT的完整部署分析实现了亚毫秒级推理延迟，使其适用于资源受限边缘平台的实时操作。因此，AIMformer被验证可同时部署于车载系统与路侧基础设施。