The rapid growth of urbanization and traffic complexity in smart cities has led to heightened safety risks, particularly for vulnerable road users (VRUs) like pedestrians and cyclists. This study aims to enhance VRU safety by investigating their perceived risk and decision-making processes in various traffic scenarios. Traditional methods, such as surveys and observational studies, have limitations in capturing VRUs' cognitive processes and real-time behavior. This research proposes a multimodal approach combining electroencephalography (EEG) and eye-tracking technology to analyze VRUs' cognitive and physiological responses in simulated urban environments. By integrating data on attention, vigilance, and emotional states, this approach provides a comprehensive assessment of VRUs' risk perception mechanisms. Utilizing the CARLA simulator, a platform for pedestrian-vehicle interaction is developed to simulate realistic urban scenarios, while physiological and behavioral data are collected to establish a model for predicting VRU behavior. The study contributes to theoretical insights on risk perception and practical implications for designing safer urban infrastructure in smart cities, ultimately aiming to reduce VRU accidents and enhance traffic safety.
Zhang et al. (Thu,) studied this question.