UC Berkeley

Commercial availability of vehicle automation has become mainstream. Most of today’s new vehicles can perform longitudinal car following autonomously via Adaptive Cruise Control (ACC). Understanding ACC car following behaviors has become crucial to modeling traffic flow at the microscopic level as market penetration increases. Besides, autonomous vehicles at any Society of Automotive Engineers (SAE) level use ACC for longitudinal control. Thus, ACC vehicle behavior will significantly impact traffic over a long period. Field experiments demonstrated that today’s commercially available ACC vehicles provide similar headways and capacities as human-driven vehicles on freeways under steady-state and free-flow conditions. However, field tests also showed that combustion-based vehicles paired with ACC could lead to further capacity reduction when operating in non-steady state conditions where queues are present, and speeds frequently fluctuate. Electric vehicles (EVs), on the other hand, have been verified to allow ACC to adopt shorter headways and accelerate more swiftly to maintain shorter headways during queue discharge due to their unique powertrain characteristics such as instantaneous torque and regenerative braking, and therefore reverse the negative impact on capacity. These experiments generated MicroSIMACC, a comprehensive field data set that encompasses full-speed range car following with interruptions from lane change maneuvers. This study developed full-speed range car-following models for both internal combustion engine vehicles (ICE vehicle) and electric vehicles (EV) equipped with ACC, capturing the variable gaps under large speed oscillation and heterogeneous car-following behaviors across different speed levels, gap settings, and powertrains. New ACC trajectory data from MicroSIMACC were utilized to help identify the limitations of the well-established constant gap ACC car-following model and propose changes. More importantly, this study clarified the logistics of implementing and incorporating the new ACC models with other models in microscopic simulation and provided novel insights about the impact of increasing market penetration of ACC with different powertrains via sensitivity analysis. The simulation results indicated that the higher the market penetration rates of EVs are, the larger discharge flow and longer total travel distance can be achieved on a freeway corridor. This was consistent with filed observations and proved that EVs with ACC provided better traffic performance than ICE vehicles with ACC.