Driver's Perceived Risk Prediction Method Based on Semisupervised Learning Strategy

Safety has become a critical issue in the development of automated driving systems (ADSs). Drivers' perception of risk determines their acceptance, trust, and use of ADS. However, driver's perceived risk is subjective and difficult to evaluate using traditional risk assessment methods. To address this issue, a driver's subjective perceived risk (DSPR) model is proposed, which regards driver's perceived risk as a dynamically triggered mechanism that exhibits anisotropy and attenuation. Subsequently, 20 participants are recruited for a driver-in-the-loop experiment to report their real-time subjective risk ratings (SRRs) when experiencing various real-world automated driving scenarios. A convolutional neural network and bidirectional long short-term memory with temporal pattern attention (CNN-Bi-LSTM-TPA) network is embedded into a semisupervised learning strategy to predict driver's SRRs, aiming to reduce data noise caused by the subjective randomness of drivers. Results illustrate that our proposed DSPR model combined with the semisupervised learning strategy achieves the highest prediction accuracy of 87.91% in predicting driver's SRRs, outperforming other three state-of-the-art risk models. Compared to the model trained on the original data, the semisupervised method improves accuracy by 20.12%. The proposed CNN-Bi-LSTM-TPA network presents the highest among four different network structures. Finally, a genetic algorithm is applied to optimize parameters, which improves the accuracy to 89.95%. This study offers an effective method for assessing driver's perceived risk, providing support for the safety enhancement of ADS and driver's trust improvement.