Sensing Accident-Prone Features in Urban Scenes for Proactive Driving and Accident Prevention
نویسندگان
چکیده
In urban cities, visual information on and along roadways is likely to distract drivers lead missing traffic signs other accident-prone (AP) features. To avoid accidents due these cues, this paper proposes a notification of AP-features based real-time images obtained via dashcam. For purpose, Google Street View around accident hotspots (areas dense occurrence) identified by real-accident dataset are used train novel attention module classify given scene into an hotspot or non-hotspot (area sparse occurrence). The proposed leverages channel, point, spatial-wise learning top different CNN backbones. This leads better classification results more certain with contextual knowledge when compared backbones alone. Our achieves up 92% accuracy. capability detecting the model were analyzed comparative study three class activation map (CAM) methods, which inspect specific causing decision. outputs CAM methods processed image processing pipeline extract only that explainable notified using system. Range experiments was performed prove efficacy Ablation taking 9.61%, average, total area in each sample increased chance be classified as 21.8%.
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ژورنال
عنوان ژورنال: IEEE Transactions on Intelligent Transportation Systems
سال: 2023
ISSN: ['1558-0016', '1524-9050']
DOI: https://doi.org/10.1109/tits.2023.3271395