An intelligent traffic anomaly detection system based on self-supervised learning and self-powered hybrid nano-sensor

Transportation is a foundational element of global economic development, with vehicle anomaly detection playing a pivotal role in ensuring safety and operational efficiency. Conventional anomaly detection techniques predominantly rely on battery-powered sensors and labor-intensive manual data labeling, thereby limiting both scalability and efficiency. This study introduces a time series-oriented self-supervised framework integrated with a triboelectric-electromagnetic nanosensor (TENS) to form an Anomaly Detection System (ADS). ADS represents a paradigm shift by converting vehicle vibrations into electrical energy and enabling self-supervised anomaly detection through the temporal reconstruction of vibrational characteristics. The proposed framework automatically generates pseudo-labeled datasets using a deep autoencoder, which subsequently trains LSTM-based classifiers without the need for manual labeling. Experimental results demonstrate that TENS achieves a peak RMS power density of 81.97 W/m³. As a self-powered sensor, it effectively detects vibrations without external energy inputs, maintains stable features over more than 100,000 cycles, and has the potential to power third-party sensors. In empirical evaluations involving vehicles, Autonomous Rail Rapid Transit (ART), and bicycles, ADS achieved an average anomaly detection accuracy of 97.15 %. Compared to methods employing only unsupervised reconstruction, ADS improved accuracy by 17.81 % to 60.14 % and also surpassed self-supervised approaches based on 1D-CNN. When deployed in vehicular contexts, ADS further demonstrated robust generalization and self-supervised anomaly detection capabilities. The seamless integration of hybrid nanosensor technology with an advanced self-supervised learning framework illustrates how sustainable energy solutions can synergize with cutting-edge artificial intelligence to advance intelligent transportation systems and predictive maintenance strategies.