Driving Process’ Analysis and HUD Design Based on Conditional Autonomous Traffic Safety

With the rapid increasing of car quantity, traffic safety problem has become more and more serious. Traffic crash is also a major world public health problem; hence, it has become a topic of interest among many scholars. This study intends to analyze the factors that affect driving safety under different driving scenarios. The results of this study are based on conditional autonomous driving. The context-aware conditional autonomous safety driving process and the relationships among three elements, namely, the environment, the driver and the car, are analyzed based on the Haddon matrix and the system attribution model. The vehicle used is a conditional autonomous car capable of context awareness; it can send useful information as feedback to the driver and interact with driver behavior. Afterward, the results are applied in parking scenario and lane changing scenario, and the factors that influence safety during parking and lane changing are analyzed. Then, the obtained information is validated based on driver perspective, and a design concept for head-up display is proposed. This design is expected to assist drivers in parking and lane changing without accident.