Investigate the Effects of V2X Technologies for Automated Vehicles Using Virtual Simulation and Driving Simulator Experiments—Exploring the Effects of Visual Environment on Traffic Safety

The visual environment could have effects on the performance of automated vehicles within the V2X technology regarding traffic safety. This research aims to explore the effects of visual environment on traffic safety for the development of virtual simulation and driving simulator experiments. Both the effects on the speeding crashes and the severity of single-vehicle crashes were explored. To obtain the data of drivers’ visual environment in the real world, a framework was proposed to obtain the Google street view (GSV) images. Deep neural network and computer vision technologies were applied to obtain the clustering and depth information from the GSV images. To reflect drivers’ visual environment in the real world, the coordinate transformation was conducted, and several visual measures were proposed and calculated. Three different tree-based ensemble models (i.e., random forest, adaptive boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost)) were applied to estimate the number of speeding crashes and the comparison results showed that XGBoost could provide the best data fit. The explainable machine learning method were applied to explore the effects of drivers’ visual environment and other features on speeding crashes. The results validated the visual environment data obtained by the proposed method for the speeding crash analysis. It was suggested that the proportion of trees in the drivers’ view and the proportion of road length with trees could reduce speeding crashes. In addition, the complexity level of drivers’ visual environment was found to increase the crash occurrence.