ArticleInternational journal of pattern recognition and artificial intelligence2018
Detection of Lane-Change Events in Naturalistic Driving Videos.
Article in International journal of pattern recognition and artificial intelligence, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles.Computational intelligence and neuroscience · 2022Article
- Evaluation of the Driving Safety of Visually Impaired Bioptic Drivers Based on Critical Events in Naturalistic Driving.Translational vision science & technology · 2020Article
- Driving Safety of Cognitively-Impaired Drivers Based on Near Collisions in Naturalistic Driving.Journal of Alzheimer's disease reports · 2020Article
- Detecting and Tracking Unsafe Lane Departure Events for Predicting Driver Safety in Challenging Naturalistic Driving Data.IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles SymposiumArticle
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Authors and funding
3 authors.
Funding
Abstract
Lane changes are important behaviors to study in driving research. Automated detection of lane-change events is required to address the need for data reduction of a vast amount of naturalistic driving videos. This paper presents a method to deal with weak lane-marker patterns as small as a couple of pixels wide. The proposed method is novel in its approach to detecting lane-change events by accumulating lane-marker candidates over time. Since the proposed method tracks lane markers in temporal domain, it is robust to low resolution and many different kinds of interferences. The proposed technique was tested using 490 h of naturalistic driving videos collected from 63 drivers. The lane-change events in a 10-h video set were first manually coded and compared with the outcome of the automated method. The method's sensitivity was 94.8% and the data reduction rate was 93.6%. The automated procedure was further evaluated using the remaining 480-h driving videos. The data reduction rate was 97.4%. All 4971 detected events were manually reviewed and classified as either true or false lane-change events. Bootstrapping showed that the false discovery rate from the larger data set was not significantly different from that of the 10-h manually coded data set. This study demonstrated that the temporal processing of lane markers is an effcient strategy for detecting lane-change events involving weak lane-marker patterns in naturalistic driving.
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