A taxonomy of human driver errors and unsafe adaptations in mixed traffic

Abstract Human driver errors remain a central road-safety concern, but existing error and violation taxonomies were developed mainly for conventional traffic. In mixed traffic, human drivers must also interpret and respond to autonomous vehicles (AVs), whose behavior, intent, recognizability, and social role may differ from those of human-driven vehicles. This paper presents a case-level evidence synthesis and develops a taxonomy of human driver errors and unsafe adaptations in mixed traffic, focusing on drivers of manually driven vehicles who interact with AVs as surrounding road users. The proposed taxonomy links AV-specific interaction factors, including recognizability, driving style, predictability, trust, social evaluation, infrastructure, penetration rate, and repeated exposure, to observable unsafe behavior and to the information-processing stages at which driver errors are inferred to occur. The synthesis suggests that many mixed-traffic errors are concentrated in the projection and decision stages, with two recurring pathways: safety-margin reduction caused by poor anticipation of AV behavior or excessive reliance on AV capability, and exploitation caused by intentional priority-taking or social devaluation of AVs. The taxonomy provides a structured basis for mixed-traffic safety analysis and assessment.