用半监督学习预测司机实时风险感知，准确率达87.91%。
Predicting Driver's Perceived Risk: a Model Based on Semi-Supervised Learning Strategy
- 基于卷积与双向LSTM注意力网络，结合半监督策略建模风险感知动态特性。
- 模型在20名参与者上实现87.91%预测准确率，较现有方法提升20.12%。
- 适用于自动驾驶系统安全评估与信任机制设计，适合人机交互研究者。
驾驶员对风险的感知决定了其对自动驾驶系统（ADS）的接受度、信任度与使用意愿。然而，主观风险感知难以通过传统方法有效评估。为此，本文提出一种驾驶员主观感知风险（DSPR）模型，将风险感知视为具有各向异性和衰减特性的动态触发机制。招募20名参与者参与驾驶模拟实验，实时报告不同自动驾驶场景下的主观风险评分（SRRs）。采用卷积神经网络与双向长短期记忆网络结合时间模式注意力（CNN-Bi-LSTM-TPA）结构，并嵌入半监督学习策略，以降低参与者主观随机性带来的数据噪声。结果表明，DSPR模型在预测SRRs时达到最高87.91%的准确率，优于三种前沿风险模型；半监督策略使准确率提升20.12%。此外，CNN-Bi-LSTM-TPA在四种LSTM结构中表现最优。本研究为评估驾驶员风险感知提供了有效方法，有助于提升自动驾驶系统的安全性与用户信任度。
原文摘要 · Abstract (English)
Drivers' perception of risk determines their acceptance, trust, and use of the Automated Driving Systems (ADSs). However, perceived risk is subjective and difficult to evaluate using existing methods. To address this issue, a driver's subjective perceived risk (DSPR) model is proposed, regarding perceived risk as a dynamically triggered mechanism with anisotropy and attenuation. 20 participants are recruited for a driver-in-the-loop experiment to report their real-time subjective risk ratings (SRRs) when experiencing various automatic driving scenarios. A convolutional neural network and bidirectional long short-term memory network with temporal pattern attention (CNN-Bi-LSTM-TPA) is embedded into a semi-supervised learning strategy to predict SRRs, aiming to reduce data noise caused by subjective randomness of participants. The results illustrate that DSPR achieves the highest prediction accuracy of 87.91% in predicting SRRs, compared to three state-of-the-art risk models. The semi-supervised strategy improves accuracy by 20.12%. Besides, CNN-Bi-LSTM-TPA network presents the highest accuracy among four different LSTM structures. 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.
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