Previously, Electrical Engineering senior design students converted a standard go kart into a Smart Kart that features an all-electric, drive-by-wireless system.

Starting in 2021, the Smart Kart will be converted into a fully autonomous Charger Kart by integrating sensing and computing elements from our previous design projects. The sensor suite will include a 16-beam Velodyne LIDAR, dual FLIR cameras, and a 24GHz radar system.

Dr. David J. Coe ( coed@uah.edu )

Students in the Masters of Software Engineering (MSSE) studio course are developing a custom path planning module for integration into the CARLA autonomous vehicle simulation environment.

Under Construction. Check back soon...

Dr. Jeffrey H. Kulick ( kulickj@uah.edu )

Students in the Masters of Software Engineering (MSSE) studio course are leveraging the CARLA autonomous vehicle simulation environment to construct an annunciator that will help humans drive more safely.

Under Construction. Check back soon...

Dr. Jeffrey H. Kulick ( kulickj@uah.edu )

A team of computer engineering senior design students shall develop an autonomous model car that can autonomously platoon with other autonomous vehicles. In Fall 2018, the students shall refine project requirements and produce an initial design. In Spring 2019, the students shall focus on implementation and testing of their solution.

Dr. David J. Coe ( coed@uah.edu )

Students in the Masters of Software Engineering (MSSE) studio course are working with data produced by a 16-channel Velodyne LIDAR and a pair of high-resolution FLIR cameras to obtain object classification and velocity vectors for each object recognized by a neural network. The object recognizer software has been derived from last year’s software engineering studio project to develop an image synthesis and neural net training system. Once completed this data will be input to an AI based path planning system to help plan safe paths for the vehicle

Dr. Jeffrey H. Kulick ( kulickj@uah.edu )

Several groups of K-12 summer camp students explored the use of supervised learning to train autonomous cars. Using a fleet of Donkey Cars constructed by Jason Winningham, each student manually drove a car to assist in the gathering of training data for the neural network. The driving data samples were then moved to a server for execution of the training algorithm. The trained algorithm was then deployed back to the model autonomous car to evaluate its driving performance. This work was made possible by a generous grant from the Toyota Foundation.

Dr. Rhonda K. Gaede ( gaeder@uah.edu )

Jason Winningham ( jason.winningham@uah.edu )

A team of senior design students explored the use of synthesized data for training autonomous cars. As part of this project, the students constructed a virtual model of the UAH Engineering Building and trained a neural network to navigate the virtual model. Subsequently, the students deployed the trained neural network on an autonomous model car to evaluate the effectiveness of the training.

Dr. David J. Coe ( coed@uah.edu )

Dr. Jeffrey H. Kulick ( kulickj@uah.edu )

As a capstone project for the UAH Masters in Software Engineering (MSSE) degree, teams of graduate students generated synthetic imagery that they used to train image classifiers to recognize street signs, pedestrians, and potholes. Classification accuracy was assessed using imagery gathered from dash cam footage.

Dr. Jeffrey H. Kulick ( kulickj@uah.edu )

Our autonomous car simulation test bed augments open source driving simulation software with the ability to simulate a variety of cyber attacks against either a human or AI driver.

Dr. David J. Coe ( coed@uah.edu )

Dr. Jeffrey H. Kulick ( kulickj@uah.edu )

Jason Winningham ( jason.winningham@uah.edu )