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Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines.

The fuel called E-85 can be burned effectively in engines similar to the engines currently mass-produced for use with gasoline. Since the ethanol component of this fuel is produced from crops such as corn and sugar cane, the fuel is almost fully renewable. The different physical and chemical properties of E-85, however, do require certain modifications to the common gasoline engine. The Windsor - St. Clair team has focused their attention to modifications that will improve fuel efficiency and reduce tailpipe emissions. Other modifications were also performed to ensure that the vehicle would still operate with the same power and driveability as its gasoline counterpart.