Yao Sizhe, Yu Bo, Chen Yuren, Gao Kun, Bao Shan, Shangguan Qiangqiang
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, Shanghai, 201804, China.
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden.
Accid Anal Prev. 2025 Mar;211:107877. doi: 10.1016/j.aap.2024.107877. Epub 2024 Dec 9.
Aesthetics has always been an advanced requirement in road environment design, because it can provide a pleasant driving experience and guide better driving behavior for human drivers. However, it remains unknown whether aesthetics-based road environment design also has an impact on autonomous vehicles (AVs), resulting in that current evaluation models on road readiness for AVs (RRAV) do not consider road environment aesthetics. Therefore, this study aims to explore the relationship between road environment aesthetics and risky driving behavior of AVs (RDBAV) and propose an RRAV evaluation model from the new perspective of road environment aesthetics. Using real autonomous driving data, 1,491 longitudinal RDBAV events and 225 lateral RDBAV events are acquired together with corresponding road environment images. A novel quantitative model of road environment aesthetics is developed and 38 relevant feature variables are extracted from four aspects, including Naturalness, Vividness, Variety, and Unity. Then, an explainable machine learning that combines XGBoost (eXtreme Gradient Boosting) with SHAP (SHapley Additive exPlanation) is employed to establish an evaluation model of RRAV, by treating the occurrence of RDBAV as the dependent variable and feature variables of road environment aesthetics as independent variables. The results show that this XGBoost-based RRAV evaluation model performs better than other commonly-used methods, with accuracies of 96.9% and 91.8% for longitudinal and lateral RDBAV prediction, respectively. Due to the advantages of SHAP, the influence degrees of aesthetic features of road environments on RDBAV are calculated and explained based on global and individual feature contributions. In addition, a random parameters multinomial logit model with heterogeneity in means and variances reveals that the indicator of left visual curve length in the "middle scene" and the indicator of dominant color have significant heterogeneity for the analyses of longitudinal RDBAV. The findings of this study might contribute to the accurate evaluation of RRAV from the new viewpoint of aesthetics, the development of human-like visual perception systems of AVs, and the optimization of aesthetics-based road environment design.
美学一直是道路环境设计中的一项高级要求，因为它可以为人类驾驶员提供愉悦的驾驶体验，并引导更好的驾驶行为。然而，基于美学的道路环境设计是否也会对自动驾驶车辆（AV）产生影响，目前尚不清楚，这导致当前的自动驾驶车辆道路就绪性（RRAV）评估模型并未考虑道路环境美学。因此，本研究旨在探索道路环境美学与自动驾驶车辆危险驾驶行为（RDBAV）之间的关系，并从道路环境美学的新视角提出一种RRAV评估模型。利用真实的自动驾驶数据，获取了1491起纵向RDBAV事件和225起横向RDBAV事件以及相应的道路环境图像。开发了一种新颖的道路环境美学定量模型，并从自然度、生动度、多样性和统一性四个方面提取了38个相关特征变量。然后，采用一种将XGBoost（极端梯度提升）与SHAP（SHapley加法解释）相结合的可解释机器学习方法，将RDBAV的发生作为因变量，将道路环境美学的特征变量作为自变量，建立RRAV评估模型。结果表明，这种基于XGBoost的RRAV评估模型比其他常用方法表现更好，纵向和横向RDBAV预测的准确率分别为96.9%和91.8%。由于SHAP的优势，基于全局和个体特征贡献计算并解释了道路环境美学特征对RDBAV的影响程度。此外，一个具有均值和方差异质性的随机参数多项logit模型表明，“中间场景”中的左视觉曲线长度指标和主色调指标在纵向RDBAV分析中具有显著的异质性。本研究的结果可能有助于从美学的新视角对RRAV进行准确评估，推动自动驾驶车辆类人视觉感知系统的发展，并优化基于美学的道路环境设计。
