Applications of Machine Learning Algorithms In Lidar-Based Autonomous Vehicles
School Name
South Carolina Governor's School for Science & Mathematics
Grade Level
12th Grade
Presentation Topic
Computer Science
Presentation Type
Mentored
Abstract
Vehicle manufacturers such as Toyota and Ford plan to develop mostly-autonomous vehicles within the next decade. To ensure the utmost safety for all that share the road, the autonomous driving algorithms must be accurate. The goal of this research was to develop an accurate machine learning algorithm able to navigate paths using infrared laser data from Light Detection and Ranging, LiDAR for short, and to test its capability to learn from various training data. Automation was implemented in a remote-controlled car which consisted of various sensors including LiDAR, camera, and inertial measurement unit. Cardboard boxes were used to construct tracks for testing the control-based driving algorithm and gathering data for training the neural network. Trained neural networks suggest that diverse data consisting of both human driving and control-based driving provides the best training for machine learning.
Recommended Citation
Li, Daniel, "Applications of Machine Learning Algorithms In Lidar-Based Autonomous Vehicles" (2019). South Carolina Junior Academy of Science. 53.
https://scholarexchange.furman.edu/scjas/2019/all/53
Location
Founders Hall 140 A
Start Date
3-30-2019 9:00 AM
Presentation Format
Oral Only
Group Project
No
Applications of Machine Learning Algorithms In Lidar-Based Autonomous Vehicles
Founders Hall 140 A
Vehicle manufacturers such as Toyota and Ford plan to develop mostly-autonomous vehicles within the next decade. To ensure the utmost safety for all that share the road, the autonomous driving algorithms must be accurate. The goal of this research was to develop an accurate machine learning algorithm able to navigate paths using infrared laser data from Light Detection and Ranging, LiDAR for short, and to test its capability to learn from various training data. Automation was implemented in a remote-controlled car which consisted of various sensors including LiDAR, camera, and inertial measurement unit. Cardboard boxes were used to construct tracks for testing the control-based driving algorithm and gathering data for training the neural network. Trained neural networks suggest that diverse data consisting of both human driving and control-based driving provides the best training for machine learning.
