Enterprise AI Analysis

AIMFORMER is a transformer-based framework specifically designed for real-time misbehavior detection in vehicular platoons, with a focus on edge deployment. Its core innovation lies in leveraging multi-head self-attention mechanisms to simultaneously capture both intra-vehicle temporal dynamics and inter-vehicle spatial correlations, crucial for distinguishing legitimate coordination from malicious manipulation. It integrates global positional encoding with vehicle-specific temporal offsets to maintain temporal awareness across multi-vehicle sequences, even during asynchronous events like join/exit maneuvers. A novel Precision-Focused Binary Cross-Entropy (PFBCE) loss function is introduced to explicitly penalize False Positive predictions, addressing the precision-recall trade-off vital for safety-critical vehicular systems, while also using balanced positive class weighting to preserve attack detection capability. This comprehensive design enables robust, real-time analysis across diverse attack vectors, platoon topologies, and operational scenarios.

Extensive evaluation across four different platoon controllers, various attack types (position, speed, acceleration falsification with constant and gradual offsets, and combined physics-consistent attacks), and diverse mobility scenarios (join, exit, steady-state) consistently demonstrates AIMFORMER's superior performance. It achieves F1-scores ranging from 0.93 to 0.98 and Area Under the Curve (AUC) values of 96-99%, significantly outperforming state-of-the-art baseline architectures including LSTM, BiLSTM, CNN, GRU, and MLP. Crucially, deployment analysis using TensorFlow Lite (TFLite), ONNX, and TensorRT on edge hardware reveals sub-millisecond inference latencies (as low as 0.13ms for individual models with int8 quantization), making it highly suitable for real-time operation on resource-constrained embedded platforms. This performance is achieved without significant accuracy degradation from quantization.

AIMFORMER's design ensures practical viability for real-world deployment. The model's architecture, leveraging vehicle-independent processing, allows for flexible deployment as either global models (trained on all platoon vehicles for roadside units) or individual models (per-vehicle for local processing). Quantization techniques (TFLite, ONNX, TensorRT with float16 and int8) drastically reduce model sizes and inference times. TFLite int8 quantization, for instance, reduces global model size by 86% (to 1.7 MB) and individual model size by 80% (to 1.2 MB), while achieving the fastest inference at 0.13ms (individual) and 0.26ms (global). This minimal footprint and ultra-low latency are critical for deploying safety-critical AI applications on resource-constrained in-vehicle On-Board Units (OBUs) and roadside infrastructure units (RSUs), facilitating distributed security without prohibitive communication overhead or reliance on centralized cloud systems. The global model's generalization capabilities also extend to larger platoon sizes without requiring architectural modifications.