Yang, Mingyuan (University of California, Berkeley), Badhrudeen, Mohamed (Florida Atlantic University), Kan, David (Florida Atlantic University), Yagantekin, Kemal (Florida Atlantic University), Lu, Xiao-Yun (Univ. of California, Berkeley)

Modeling Commercial Adaptive Cruise Control (acc) on Multi Lane Facilities by Incorporating Receiving-Lane-Change Car-Following

Scheduled for presentation during the Regular Session "Driving behavior models" (ThBT6), Thursday, September 26, 2024, 15:30−15:50, Salon 14

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

Today’s mainstream vehicles are partially automated via an advanced driver assistance system (ADAS), including Adaptive Cruise Control (ACC). ACC uses onboard remote sensor target detection and tracking to automatically adjust speed to maintain a constant time-gap. Contrary to expectations, ACC may reduce capacity at bottlenecks because its delayed response and limited acceleration during queue discharge could increase the average headway. However, this has not been investigated for cases when lane changers enter from the adjacent lane, which prevents accurate analysis for multi-lane roadways. ACC could respond differently to those scenarios, and such car-following behavior is often referred to as receiving-lane-change (RLC) car following. Using data from carefully planned field tests on lane change car following an RLC car following model was developed in this study. This model was then integrated into a comprehensive ACC car-following model for microscopic traffic simulation. Lastly, this study proposed a revised ACC car-following model to represent electric vehicles (EV) with ACC that exhibit significantly different braking and acceleration characteristics versus internal combustion (IC) engine ACC vehicles.