Abnormal Vehicle Event Detection Based on Deep Learning

The detection of abnormal vehicle events is critical for enabling intelligent transportation systems, yet traditional methods often suffer from missed or false detections. This paper proposes a deep learning-based framework for highway vehicle anomaly detection using fused LiDAR and visual camera data. By leveraging LiDAR to capture target and lane-line information, we construct spatiotemporal input features that encode dynamic interactions between vehicles and road structures. A fully connected neural network is trained on a curated dataset of real-world highway sensor data to extract multidimensional patterns from these spatiotemporal sequences. After training, the model fuses temporal anomaly detection results with LiDAR and camera data to validate events against corresponding video frames. Experimental results demonstrate the effectiveness of our method, achieving a detection rate of 97.11% and an accuracy of 97.10%, outperforming traditional algorithms in both metrics. Additionally, our approach reduces computational time by 30% compared to conventional event detection methods, enabling faster identification of critical traffic anomalies such as sudden lane deviations or collisions. This work provides a robust, data-driven solution for enhancing real-time vehicle surveillance systems.