Nakul Garg
Aakanksha Chowdhery
ABSTRACT
Driver assistance systems, that rely on vehicular sensors such as cameras, LIDAR and other on-board diagnostic sensors, have progressed rapidly in recent years to increase road safety. Road conditions in developing countries like India are chaotic where roads are not well maintained and thus vehicular sensors alone do not suffice in detecting impending collisions. In this paper, we investigate a collaborative driver assistance system "DRIZY: DRIve eaSY" for such scenarios where inference is drawn from on-board camera feed to alert drivers of obstacles ahead and the cloud uses GPS sensor data uploaded by all vehicles to alert drivers of vehicles in potential collision trajectory. Thus, we combine computer vision and vehicle-to-cloud communication to create comprehensive situational awareness. We prototype our system to consider two types of collisions: vehicle-to-vehicle collisions based on uploading GPS sensor data of vehicles to cloud and vehicle-to-pedestrian collisions based on detecting pedestrians from vehicle's dashboard camera feed. Sensor data processing in each vehicle occurs on smartphone for GPS values which are then uploaded to cloud and on raspberry pi3 for video feeds to make a cost-effective solution. Experiments over both 4G and wireless networks in India show that collaborative driver assistance is feasible in low traffic density within acceptable driver reaction time of <5 sec, but can be limited by the time to process compute-intensive video feeds in real-time. We investigate novel ways to optimize the processing to find an acceptable trade-off.
- Navneet Dalal and Bill Triggs. {n. d.}. Histograms of oriented gradients for human detection. In CVPR 2005. Google Scholar
Digital Library
- World Health Organization. 2015. Global status report on road safety 2015. World Health Organization.Google Scholar
- Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. {n. d.}. You only look once: Unified, real-time object detection. In CVPR 2016.Google ScholarCross Ref
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. {n. d.}. Faster R-CNN: Towards real-time object detection with region proposal networks. In NIPS 2015. Google ScholarDigital Library
- C Carl Robusto. 1957. The cosine-haversine formula. The American Mathematical Monthly 64, 1 (1957), 38--40.Google ScholarCross Ref
- Paul Viola and Michael Jones. {n. d.}. Rapid object detection using a boosted cascade of simple features. In CVPR 2001.Google ScholarCross Ref
Index Terms
- Poster: DRIZY: Collaborative Driver Assistance Over Wireless Networks
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