Detecting and Tracking Unsafe Lane Departure Events for Predicting Driver Safety in Challenging Naturalistic Driving Data

Luis Riera, Koray Ozcan, Jennifer Merickel, Mathew Rizzo, Soumik Sarkar, Anuj Sharma

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations


Our goal is to improve driver safety predictions in at-risk medical or aging populations from naturalistic driving video data. To meet this goal, we developed a novel model capable of detecting and tracking unsafe lane departure events (e.g., changes and incursions), which may occur more frequently in at-risk driver populations. The model detects and tracks roadway lane markings in challenging, low-resolution driving videos using a semantic lane detection pre-processor (Mask R-CNN) utilizing the driver's forward lane region, demarking the convex hull that represents the driver's lane. The hull centroid is tracked over time, improving lane tracking over approaches which detect lane markers from single video frames. The lane time series was denoised using a Fix-lag Kalman filter. Preliminary results show promise for robust lane departure event detection. Overall recall for detecting lane departure events was 81.82%. The F1 score was 75% (precision 69.23%) and 70.59% (precision 62.07%) for left and right lane departures, respectively. Future investigations include exploring (1) horizontal offset as a means to detect lead vehicle proximity, even when image perspectives are known to have a chirp effect and (2) Long Short Term Memory (LSTM) models to detect peaks instead of a peak detection algorithm.

Original languageEnglish (US)
Number of pages8
StatePublished - 2020
Event31st IEEE Intelligent Vehicles Symposium, IV 2020 - Virtual, Las Vegas, United States
Duration: Oct 19 2020Nov 13 2020


Conference31st IEEE Intelligent Vehicles Symposium, IV 2020
Country/TerritoryUnited States
CityVirtual, Las Vegas

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Modeling and Simulation


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