Modeling Perception Performance in Microscopic Simulation of Traffic Flows Including Automated Vehicles.

Mixed traffic with automated and human-driven vehicles interacting with one another will soon become a common reality. Microscopic traffic simulation can preemptively help assess the impact on the traffic flow dynamics as long as the tools adequately capture the differences on how automated driving systems (ADSs) drive compared to humans. In this work a modeling approach that captures differences in perception performance is proposed. While human drivers perceive through their senses and cognitive processes, ADS perceive the driving context through on-board sensors, connectivity features and software. The perception performance is described in terms of accuracy, precision, detection range, and detection delay. The model for perception is implemented in SUMO and a simulation test in a platoon shows the acceleration response affected by up to 35 % for perception errors of $\approx 10 \%$ which by extension will affect the traffic flow dynamics. The proposed modeling approach for perception contributes to the robustness of microscopic traffic simulation and the modeling of heterogeneous mixed traffic.