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Students Outpace DeepRacer With IoT Algorithms

College of Charleston attendees teamed with Internet of Things company Logicalis to fine-tune a set of reinforced-learning tools that enable a vehicle, using wireless connectivity to a laptop or tablet, to learn its own route and respond with appropriate speed and direction settings.
By Claire Swedberg
Aug 30, 2019

Two College of Charleston data science students, under the guidance of technology company Logicalis, have completed what they call a competitive learning experience that employs reinforcement learning (RL) computing at the edge to help an autonomous vehicle learn its way around a track.

The team's Internet of Things (IoT)-based system employs Wi-Fi-based connectivity so that software, using Amazon Web Services' (AWS) DeepRacer cloud-based console, can communicate with the vehicle, capturing camera imagery to better understand the vehicle's path and location on roadways, as well as sending instructions to improve performance, such as acceleration and improved path selection. The competitive learning experience they developed enables their own system to out-perform the AWS-based model, the team reports. The student team's system thereby enables a vehicle or other device to learn its environment and respond, based on the data collected and interpreted, at the edge (using a tablet or laptop).

The DeepRacer
AWS held a DeepRacer inventing competition earlier this year at its AWS re:Invent conference, to challenge developers and data scientists to build autonomous vehicles that employ machine learning. Users can join the AWS DeepRacer League to compete for prizes and a chance to advance to the championship cup.

The DeepRacer is a one-eighteenth scale race car, explains Matthew Funderburg, Logicalis's services architect for IoT analytics, but it comes with the capacity to interact with its own surroundings and accomplish machine learning. Amazon calls it an integrated learning system for users to learn and explore reinforcement learning, and to experiment and build autonomous driving applications.

At Logicalis, Funderburg provides the technology company with solution development, building use cases with customers around the IoT. Initially, Mike Trojecki, Logicalis's IoT and analytics VP, asked Funderburg to mentor the two students, Eliza Starr and Joshua Turner. "I knew this project could be cool for them—and for me as well," he recalls.

Since Logicalis was launched in 2018, it has developed and deployed IoT solutions for health-care, manufacturing, government and education applications. As the company grows, Funderburg says, "We've been working on understanding what customer needs are and building out partnerships with some existing customers."

For the AWS DeepRacer program, Starr and Turner were focused on developing and fine-tuning the algorithm for the autonomous car so that it could use sensor data to detect where a vehicle is located on a track, and to use that data, along with machine learning—sharing that information and receiving prompts from the cloud-based software wirelessly—to train itself to stay on the track at top speed. Turner and Starr are both data sciences majors.

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