A Naturalistic Driving Study for Lane Change Detection and Personalization 2024-01-2568

Driver Assistance and Autonomous Driving features are becoming nearly ubiquitous in new vehicles. The intent of the Driver Assistant features is to assist the driver in making safer decisions. The intent of Autonomous Driving features is to execute vehicle maneuvers, without human intervention, in a safe manner. The overall goal of Driver Assistance and Autonomous Driving features is to reduce accidents, injuries, and deaths with a comforting driving experience. However, different drivers can react differently to advanced automated driving technology. It is therefore important to consider and improve the adaptability of these advances based on driver behavior. In this paper, a human-centric approach is adopted to provide an enriching driving experience. We perform data analysis of the naturalistic behavior of drivers when performing lane change maneuvers by extracting features from extensive Second Strategic Highway Research Program (SHRP2) data of over 5,400,000 data files. First, the difficult problem of filtering the data to automatically detect lane change events is developed. We will present our robust automated lane change event detection algorithm that employs machine vision lane tracking system variables such as lane marker probabilities. We then show that detected lane changing instances can be validated using only vehicle kinematics data. Kinematic vehicle parameters such as vehicle speed, lateral displacement, lateral acceleration, steering wheel angle, and lane change duration are then extracted and examined using temporal characteristics. We show how these vehicle kinematic parameters exhibit patterns during lane change maneuvers for a specific driver. The work shows limitations of analyzing vehicle kinematics parameters separately and develops a novel metric, Lane Change Dynamic Score (LCDS) that shows collective effect of vehicle kinematic parameters during lane change maneuvers and driver behaviors. LCDS is then used to classify each lane change and identification of driving styles. The results presented here will assist in the development of Driver Assistance and Autonomous Driving Lane Change maneuvers that result in more naturalistic assisted and automated vehicle controls.