As the automotive industry transitions toward higher automation, Level 2 automated vehicles (AVs) represent a pivotal phase where human oversight remains essential. This study examines car-following (CF) behavior, a key factor in traffic efficiency and safety, within mixed traffic environments that include both Level 2 automated vehicles (AVs) and human-driven vehicles (HDVs). The objectives are to (1) analyze steady-state CF behavior between AVs and various HDV types, (2) to calibrate CF parameters to understand the behavior of level 2 AVs with different types of HDVs, (3) to compare the CF behavior of level 2 AVs with different aggressiveness types and different vehicle types of HDVs, and (4) to compare the CF behavior of level 2 AVs with different aggressiveness types across diverse facility types.
A mixed-methods approach using real-world trajectory data and simulations was employed to model and calibrate CF behavior using the Intelligent Driver Model (IDM) and the Gipps model. Key findings show that aggressive AV settings can increase traffic capacity but have higher safety risks, while mild settings promote safer interactions through increased spacing. HDVs adjust their behavior based on AV characteristics, maintaining a greater distance behind more aggressive AVs. Roadway type also affects CF dynamics—urban and signalized roads lead to more cautious driving behavior than freeways. These insights refine CF models, enhance AV simulation accuracy, and support safer integration of AVs into real-world traffic systems.