Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data
Journal article, 2024

Lane changing is a complex, yet extremely common driving manoeuvre. Studying lane changes can provide insight into how long drivers wait after activating their turn signal before changing lanes -a time that we call time-to-lane-change-initiation (TTLCI). TTLCI can offer valuable insights into driver behaviour prior to changing lanes. However, a better understanding of TTLCI, particularly in real-world settings, is lacking. To address this knowledge gap, we investigated TTLCI using driving data collected on public roads in Gothenburg, Sweden. We used the Kaplan-Meier (K-M) method and the mixed-effect Cox Proportional Hazard (CPH) model (statistical techniques from survival analysis) to comprehensively analyze TTLCI and identify factors that significantly influence it. The results of the K-M method indicate that most lane changes were initiated within two seconds of activating the turn signal. The mixed-effect CPH model showed that the speed of the lane-changing vehicle, the type and direction of the lane change, the presence of lead and lag vehicles, and the lag gap were all significant factors. These findings provide new insights into pre-lane-change behaviour and pave the way for future studies, in part by improving current lane change models. Moreover, the findings have implications for future regulations concerning turn-signal usage by human drivers. Additionally, our results can contribute to the development of algorithms for autonomous vehicles by improving their ability to detect imminent lane changes by surrounding vehicles.

realistic driving data

Analytical models

Trajectory

Radar

autonomous vehicles

Lane change

Europe

Regulation

mixed effect Cox model

time-to-lane-change-initiation

Vehicles

Roads

Author

Sarang Jokhio

University of Ulm

Pierluigi Olleja

Jonas Bärgman

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Fei Yan

University of Ulm

Martin Baumann

University of Ulm

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. 25 5 4620-4633

Subject Categories

Applied Psychology

Vehicle Engineering

DOI

10.1109/TITS.2023.3329690

More information

Latest update

6/1/2024 4