The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/19170302

Driessen, Tom; Bindu Prasad, Lokin Lakshmindra; Bazilinskyy, Pavlo; de Winter, Joost (2022): Supplementary data for the paper 'Identifying lane changes automatically using the GPS sensors of portable devices'. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/19170302.v2

Dataset

x choose version: version 1 - 2022-03-21

• Cognitive Sciences
• Automotive Engineering

Driving Maneuver Recognition, Driving Style Recognition, Floating Car Data, Highway Engineering, Road Design, Lane Change Detection

CC0

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by Tom Driessen , Lokin Lakshmindra Bindu Prasad , Pavlo Bazilinskyy , Joost de Winter

Mobile applications that provide GPS-based route navigation advice or driver diagnostics (e.g., driving speed, driving style) are gaining popularity. However, these applications currently do not have knowledge of whether the driver is performing a lane change. Having such information may prove valuable to individual drivers (e.g., to provide more specific navigation instructions) or road authorities (e.g., knowledge of lane change hotspots may inform road design). The present study aimed to assess the accuracy of lane change recognition algorithms that rely solely on mobile GPS sensor input. Three trips on Dutch highways, totaling 158 km of driving, were performed while carrying two smartphones (Huawei P20, Samsung Galaxy S9), a GPS-equipped GoPro Max, and a USB GPS receiver (GlobalSat BU343-s4). The timestamps of 215 lane changes, acting as the ground truth, were manually extracted from the forward-facing GoPro camera footage. After connecting the GPS trajectories to the road using Mapbox Map Matching API, lane changes were identified based on the exceedance of a lateral translation threshold in set time windows. The number of true positives, false positives, true negatives, and false negatives with respect to the ground truth were tabulated, and the overall accuracy of the lane-change classification was found to be 90%. The method appears promising for highway engineering and traffic behavior research that use floating car data, but there may be limited applicability to real-time advisory systems due to the occasional occurrence of false positives.

• 2022-03-21 first online

• 2022-05-03 published, posted

4TU.ResearchData

• This research is funded by Transitions and Behaviour grant 403.19.243 (“Towards Safe Mobility for All: A Data-Driven Approach”), provided by the Netherlands Organization for Scientific Research (NWO).

Department of Cognitive Robotics, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology