Parking Occupancy Inference With LiDAR Sensors

Parking is a cumbersome part of auto travel in urban areas, primarily due to lack of information on the location of available spots. Sensors can be deployed to detect occupancy, but they often fail due to their high costs and detection inefficiencies in outdoor spaces. This project pursues a feasibility study of using LIDAR sensors, which overcome some deficiencies, for parking detection. LIDARs have a wide field of view, are robust to outdoor disturbances, and can be provided at cost given the recent advancements in the autonomous vehicle industry. The steps of the project include: (i) review types of LIDARS and their properties, (ii) process data collected from LIDARS, and (iii) develop data analytics solutions for interpreting LIDAR data.

Faculty Supervisor:

Mehdi Nourinejad

Student:

Elham Heydarigharaei

Partner:

CurbLab Inc.

Discipline:

Engineering - civil

Sector:

Transportation and warehousing

University:

York University