Pavement Distress Detection Using Conventional Unmanned Autonomous Vehicle LiDAR

In Montreal, pavement distresses are causing serious problem to the road network with more than half of the road considered in a bad and a very bad shape. Many pavement inspection methods are developed in order to inspect, detect, locate, and classify pavement distresses; however, these methods are not efficient in term of time, cost, and accuracy. In our project, we aim to develop a new approach in detecting, classifying, and locating pavement distresses using conventional unmanned autonomous vehicle LiDAR. This approach will create a new platform involving large number of vehicles equipped with LiDAR in detecting pavement distresses with no extra cost, less time, and more detection accuracy than the traditional methods.

Intern: 
Nizar Tarabay
Faculty Supervisor: 
Maarouf Saad
Province: 
Quebec
Partner: 
Partner University: 
Program: