VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System


  • Abhinav Kumar University of Utah
  • Aishwarya Gupta Conduent Labs
  • Bishal Santra Indian Institute of Technology
  • KS Lalitha Conduent Labs
  • Manasa Kolla Conduent Labs
  • Mayank Gupta Conduent Labs
  • Rishabh Singh Conduent Labs




High Occupancy Vehicle/High Occupancy Tolling (HOV/HOT) lanes are operated based on voluntary HOV declarations by drivers. A majority of these declarations are wrong to leverage faster HOV lane speeds illegally. It is a herculean task to manually regulate HOV lanes and identify these violators. Therefore, an automated way of counting the number of people in a car is prudent for fair tolling and for violator detection.

In this paper, we propose a Vehicle Passenger Detection System (VPDS) which works by capturing images through Near Infrared (NIR) cameras on the toll lanes and processing them using deep Convolutional Neural Networks (CNN) models. Our system has been deployed in 3 cities over a span of two years and has served roughly 30 million vehicles with an accuracy of 97% which is a remarkable improvement over manual review which is 37% accurate. Our system can generate an accurate report of HOV lane usage which helps policy makers pave the way towards de-congestion.




How to Cite

Kumar, A., Gupta, A., Santra, B., Lalitha, K., Kolla, M., Gupta, M., & Singh, R. (2019). VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9498-9503. https://doi.org/10.1609/aaai.v33i01.33019498



IAAI Technical Track: Emerging Papers