Deep Neural Network approach for navigation of Autonomous Vehicles

Mayank Raj, Narendra V G

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about 'Autonomous Vehicles' amongst the major tech giants such as Google, Uber and Tesla. Numerous approaches have been adopted to solve this problem which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from comma.ai dataset. A heatmap was used to check for correlation among the features and finally four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers followed by five dense layers. Finally, the calculated values were tested against the labelled data where mean squared error was used as a performance metric.

Original languageEnglish
Title of host publication2021 6th International Conference for Convergence in Technology, I2CT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728188768
DOIs
Publication statusPublished - 02-04-2021
Event6th International Conference for Convergence in Technology, I2CT 2021 - Pune, India
Duration: 02-04-202104-04-2021

Publication series

Name2021 6th International Conference for Convergence in Technology, I2CT 2021

Conference

Conference6th International Conference for Convergence in Technology, I2CT 2021
CountryIndia
CityPune
Period02-04-2104-04-21

All Science Journal Classification (ASJC) codes

  • Management of Technology and Innovation
  • Artificial Intelligence
  • Computer Science Applications
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Instrumentation

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