Autonomous Vehicle Control Using a Single Camera and DNNs: Experiences from the Udacity Race Team

Paper #:
  • 2018-01-0035

  • 2018-04-03
Autonomous vehicle development has benefited from sanctioned competitions dating back to the original 2004 DARPA Grand Challenge. Since these competitions, fully autonomous vehicles have become much closer to significant real-world use with the majority of research focused on reliability, safety and cost reduction. Our research details the recent challenges experienced at the 2017 Self Racing Cars event where a team of international Udacity students worked together over a 6 week period, from team selection to race day. The team’s goal was to provide real-time vehicle control of steering, braking, and throttle through an end-to-end deep neural network. Multiple architectures were tested and used including convolutional neural networks (CNN) and recurrent neural networks (RNN). We began our work by modifying a Udacity driving simulator to collect data and develop training models which we implemented and trained on a laptop GPU. Then, in the two days between car delivery and the start of the competition, a customized neural network using Keras and Tensorflow was developed. The deep learning network algorithm predicted car steering angles using a single front-facing camera. Training and deployment on the vehicle was completed using two GTX 1070s since a cloud GPU computing instance was neither available nor feasible. Using the proposed methods and working within the competition’s strict requirements, we completed several semi-autonomous laps and the team remained competitive. The results of the competition indicated that autonomous vehicle command and control is achievable using a single-camera under a limited engineering development time. However, this approach lacks enough robustness and therefore, a semantic segmentation network was developed using feature extraction from the YOLOv2 network and the CamVid dataset with a correction for the unbalanced occurrence of the different classes. Currently 31 classes can be reliably detected and classified allowing for a more complex and robust decision making architecture.
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