Current Projects
Formalizing Human-Machine Communication in the context of Autonomous Vehicles
While driving behavior is generally governed by the nature and driving objectives of the driver, there are many situations (typically in crowded traffic conditions) where tacit communication between the drivers and pedestrians govern the overall driving behavior, significantly enhancing driving safety. We intend to study and formalize the communication pattern between human drivers and pedestrians, as also investigate effective communication mechanisms between an autonomous vehicle and humans. Current autonomous vehicles engage in decision making that is primarily driven by on-board or external sensory information, and do not explicitly consider communication with pedestrians. We will incorporate the formalized communications from this study into decision making algorithms of an autonomous vehicle. Use of the results of this study would lead to improved safety of both autonomous vehicles as well as conventional vehicles.
Our goal is to develop and deploy Autonomous Shuttles on Texas A&M Campus and other private campuses such as hotels, golf courses etc. To this end we are interested in developing robust localization, mapping, obstacle avoidance and control algorithms.
One required ability for autonomous vehicles is to correctly identify street signs. This project investigates the feasibility of using a LIDAR sensor to detect, and classify signs for autonomous vehicles. Current popular methods for sign detection are vision based, however, in case of low visibility, a LIDAR detection method can be used instead.
1/10th Scaled Car High Speed Waypoint Following
This project focuses on creating a 1/10th scale platform capable of high speed waypoint following. This will allow testing of control algorithms in a low risk environment.
Past Projects
Large scale 3D mapping using LIDAR
In this project, we used the Iterative Closest Point (ICP) algorithm to build a mapping pipeline that can construct 3D maps from LIDAR data. This pipeline has been tested on data from Velodyne and RIEGL LIDARs. The Velodyne LIDAR (VLP16) was mounted on a car and was driven around, while GPS was NOT used. Final trajectories and maps were constructed from this LIDAR data.
