Faculty Advisor

Xinming Huang

Faculty Advisor

Yanhua Li

Faculty Advisor

Jie Fu


For a little over the past decade since the DARPA Grand Challenge in 2004 and the more successful Urban Challenge in 2007 autonomous vehicles have seen a surge in popularity with car manufacturers, and companies such as Google and Uber. Light Detection And Ranging (LiDAR) has been one of the major sensors in use to sense for acting on the surrounding environment instead of the classic radar which has a much narrower field of vision. However the cost of the higher end 3D LiDAR systems which started seeing use during the DARPA challenges still have the high cost of $70,000 a piece which is an issue when trying to design a consumer friendly system on a family car. This work aims to investigate alternate 2D LiDAR systems to the costly systems currently in use in many prototypes to find a cost efficient alternative that can detect and track obstacles in front of a vehicle. The introduction begins by summarizing some related prior works, particularly papers from after the Grand Challenge as well as some about the competition itself. Detection and tracking methods for point clouds generated by the LiDAR are explored including ways to search through the data in an efficient manner to meet real-time constraints. Some of the trade-offs in going from a 3D system to a 2D system and examined along with how some of the drawbacks can be mitigated.


Worcester Polytechnic Institute

Degree Name



Electrical & Computer Engineering

Project Type


Date Accepted





Object tracking, Self-driving, Navigation, Autonomous vehicle, LiDAR