A multitask CNN-LSTM model for joint prediction of vehicle lanechange intention and collision risk

Abstract

Accurate prediction of vehicle lane-change (LC) behavior and potential collision risk in highway scenarios is important for advanced driver-assistance systems (ADAS). To address the separation between lane-change prediction and risk assessment in existing studies, the difficulty of identifying long-tailed high-risk scenarios, and the tendency of selfattention models to overlook local high-frequency hazard signals, this paper proposes a multi-task learning model based on convolutional neural networks and long short-term memory networks (CNN-LSTM). The model simultaneously predicts vehicle lane-change intention, time to lane crossing (TTLC), and collision risk level. Based on HighD naturalistic driving data, kinematic and interaction features of the target vehicle and surrounding vehicles are extracted; 1D-CNN is used to extract local spatial features, and a unidirectional LSTM is combined to capture temporal dependencies. Experimental results show that the proposed model achieves a lane-change intention prediction accuracy of 94.49%; in the high-risk classification task, its recall reaches 65.52%, outperforming Transformer (44.83%) and SVM (24.14%). The results indicate that the multi-task CNN-LSTM can balance macroscopic behavior prediction and microscopic risk recognition, thereby improving the active warning capability of autonomous-driving systems in complex traffic scenarios.