The Longitudinal Driving Behavior of a Vehicle Assisted with Lv2 Driving Automation: An Empirical Study

Abstract

As the number of automated vehicles in our transportation system increases, it becomes increasingly important to understand how automation affects their driving behavior. This study defines and tests a methodology based on optimization methods to incorporate the longitudinal driving behavior of automated vehicles in the Wiedemann 99 car-following model. A pilot study was recently conducted in Portugal using a Mercedes-Benz of 2017 assisted with level 2 driving automation to gather empirical data. In total, 61 car-following events were used to support the calibration and validation tasks. The calibration error sustains the methodology’s descriptive capability to simulate the driving behavior of AVs, and the validation error sustains that the calibrated model parameters can reproduce the dynamic driving behavior of AVs with reasonable consistency and robustness. A total of seven model parameters were estimated and are in line with the trends often described in the literature on automated vehicles but also highlight differences that can be explained by different development and deployment strategies. Nevertheless, since empirical data from automated vehicles are hard to get, the presented work findings are also valuable for improving and validating future modeling efforts.

A comprehensive high‐level automated driving assistance system with integrated multi‐functionality

“…These systems encompass both longitudinal control functions, such as Adaptive Cruise Control (ACC), Automated Emergency Braking (AEB), and Forward Collision Warning (FCW), as well as lateral control functions, including Adaptive Lane Change (ALC) and Lane Keeping Assistant (LKA) systems. The application of ADAS enables vehicles to achieve a level of autonomous driving capability within certain road constraints, with human drivers providing supervision [2]. However, despite the benefits of ADAS, the individual systems often operate independently, limiting the vehicle's ability to make intelligent and advanced decisions, particularly in complex environments such as multi-lane roadways.…”

“…The primary contributions of this research are as follows: (1) The institution of integrated HADAS, sufficiently utilising multi-road capacity and improving safety and traffic efficiency. (2) The proposal of Extension Multimode Switching Strategy (EMSS), which intelligently selects the optimal driving mode to enhance both safety and traffic efficiency. (3) The establishment of the Event-triggered MPC (ETMPC) algorithm, aiming to strike a balance between control performance and computational burden.…”