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COSense: collaborative and opportunistic sensing of road events by vehicles’ cameras

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Abstract

The road event (e.g., traffic accidents) usually causes traffic jams, especially during rush hours, and drivers will be very interested to see what happened and how it goes. We propose the framework named COSense for collecting photos of road events from cameras in vehicles. Different to existing applications, CoSense uses a human-machine collaborative way to opportunistically collect photos. Firstly, after the event is reported by some people’s smart phones, its location can be estimated. And by the aid of this location information, the vehicle-mounted smart camera (e.g., driving recorder) will take photos of the events opportunistically as soon as the vehicle comes by the event. Because the vehicle speed is too fast to take photos of road events, CoSense uses a self-adjustment photo collection method for the vehicle-mounted smart camera. Based on predicting the position of the vehicle in advance, the shooting direction of the camera can be adjusted to obtain more photos that can cover the event. Secondly, in order to push high-quality events pictures to drivers, CoSense selects photos by analyzing both the reliability of the photo-taking command and photos’ time stamps. CoSense is evaluated under different conditions, and experimental results relying on eighty real-world datasets demonstrate the effectiveness of COSense.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://www.amap.com/.

  2. https://seeclickfix.com/.

  3. \(T=200\) milliseconds in this paper.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61972092, 61902068), and the National Key Research and Development Program of China (No. 2018AAA0101100).

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Foshan University, Foshan, 528000, Guangdong, China

    Weizhao Zhong

  2. School of Electronic Information and Engineering, Foshan University, Foshan, 528000, Guangdong, China

    Huihui Chen, Zhixin Pan, Chundi Zheng & Aiguo Wang

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  1. Weizhao Zhong
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  2. Huihui Chen
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  3. Zhixin Pan
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  4. Chundi Zheng
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Correspondence to Huihui Chen.

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Zhong, W., Chen, H., Pan, Z. et al. COSense: collaborative and opportunistic sensing of road events by vehicles’ cameras. CCF Trans. Pervasive Comp. Interact. 5, 276–287 (2023). https://doi.org/10.1007/s42486-023-00126-9

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