A Modified Enhanced Driver Model for Heavy-Duty Vehicles with Safe Deceleration 2023-24-0171

To accurately evaluate the energy consumption benefits provided by connected and automated vehicles (CAV), it is necessary to establish a reasonable baseline virtual driver, against which the improvements are quantified before field testing. Virtual driver models have been developed that mimic the real-world driver, predicting a longitudinal vehicle speed profile based on the route information and the presence of a lead vehicle. The Intelligent Driver Model (IDM) is a well-known virtual driver model which is also used in the microscopic traffic simulator, SUMO. The Enhanced Driver Model (EDM) has emerged as a notable improvement of the IDM. The EDM has been shown to accurately forecast the driver response of a passenger vehicle to urban and highway driving conditions, including the special case of approaching a signalized intersection with varying signal phases and timing. However, most of the efforts in the literature to calibrate driver models have focused on passenger vehicles. This study aims to expand the calibration of the EDM to commercial vehicle drivers, specifically those driving heavy-duty trucks. Real-world data for the calibration are collected with an onboard advanced connectivity platform that not only acquires and manages information about vehicles and routes but also provides ADAS and vehicles-to-everything (V2X) communication. Therefore, the data can be processed either on-board or on a cloud platform. Furthermore, a new mode is introduced within the EDM which enables preemptive deceleration of the vehicle when approaching an intersection, making a turn, or exiting from a highway. This effort will not only provide a baseline virtual driver to benchmark the performance of CAV technology in the commercial truck industry but will also enable the assessment of the impact of driver aggressiveness on the energy consumption of electric commercial vehicles.