Car-Following Speed Prediction and Anomaly Detection for Mixed Traffic Flow of Autonomous Vehicle based on Attention-LSTM-Transformer

In an Autonomous Vehicle-Mixed Traffic Flow (AV-MTF) environment, accurately predicting vehicle speeds is essential for vehicle and traffic operation and management. However, existing research has not achieved high-precision vehicle speed prediction in mixed autonomous traffic environments. To address this, we proposed a car-following speed prediction and anomaly detection method based on the Attention-LSTM-Transformer model. We first employ a multihead attention enhanced LSTM network to dynamically classify vehicle categories in the AV-MTF environment based on vehicle motion states. We propose a novel Transformer-based model that embeds classification labels and car-following state information, enabling precise speed prediction in AV-MTF environments. Additionally, the anomaly detection algorithm is proposed to identify abnormal speeds in car-following situations, covering both constant and instant offsets. The proposed models were trained and tested using the OpenACC dataset, and the effectiveness of the Attention-LSTM-Transformer-based speed prediction model and anomaly detection algorithm is validated. Results show that the classification model achieves an accuracy of 95.57%. Compared to baseline models, the speed prediction model considering vehicle category labels effectively reduces the prediction errors by more than 6.8% in all horizons. This car-following speed anomaly detector achieves over 99% accuracy for constant speed offsets and nearly 90% detection rate for small instant speed anomalies. The findings of this study provide valuable insights for vehicle and traffic operation and management in future AV-MTF environments.