TY - CONF
T1 - Detecting and Tracking Unsafe Lane Departure Events for Predicting Driver Safety in Challenging Naturalistic Driving Data
AU - Riera, Luis
AU - Ozcan, Koray
AU - Merickel, Jennifer
AU - Rizzo, Mathew
AU - Sarkar, Soumik
AU - Sharma, Anuj
N1 - Funding Information:
1&5Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA lgriera@iastate.edu, soumiks@iastate.edu 2&6Institute for Transportation, Iowa State University, Ames, IA 50010, USA koray6@iastate.edu, anujs@iastate.edu 3&4Neurological Sciences, University of Nebraska Medical Center, Omaha, NE 68198, USA jennnifer.merickel@unmc.edu, matthew.rizzo@unmc.edu *This work was supported by Toyota Collaborative Safety Research Center and the National Institutes of Health (R01-AG017177).
Publisher Copyright:
© 2020 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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U2 - 10.1109/IV47402.2020.9304536
DO - 10.1109/IV47402.2020.9304536
M3 - Paper
AN - SCOPUS:85099881828
SP - 238
EP - 245
T2 - 31st IEEE Intelligent Vehicles Symposium, IV 2020
Y2 - 19 October 2020 through 13 November 2020
ER -