A Naturalistic Driving Study for Lane Change Detection and Personalization

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Virginia Tech


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 thesis, a human-centric approach is adopted in order to provide an enriching driving experience. The thesis investigates the natural behavior of drivers when changing lanes in terms of preferences of vehicle kinematics parameters using a real-world driving dataset collected as part of the Second Strategic Highway Research Program (SHRP2). The SHRP2 Naturalistic Driving Study (NDS) set is mined for lane change events. This work develops a way to detect reliable lane changing instances from a huge NDS dataset with more than 5,400,000 data files. The lane changing instances are distinguished from noisy and erroneous data by using machine vision lane tracking system variables such as left lane marker probability and right lane marker probability. We have shown 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 from time series data to characterize these lane-changing instances for a given driver. We have shown how these vehicle kinematic parameters change and exhibit patterns during lane change maneuvers for a specific driver. The thesis shows the limitations of analyzing vehicle kinematic parameters separately and develops a novel metric, Lane Change Dynamic Score(LCDS) that shows the collective effect of these vehicle kinematic parameters. LCDS is used to classify each lane change and thereby different driving styles.



Lane Change, Personalization, Deep Learning