STEPCONE 2020
  • HOME
  • PAPER PRESENTATION
    • Computer Science Engineering
    • Chemical Engineering
    • Civil Engineering
    • Electronics and communication Engineering
    • Electrical and Electronics Engineering
    • Information Technology
    • Mechanical Engineering
  • WORKSHOPS
    • PEGA
    • AR AND VR
    • HUMANOID ROBOT
    • Advancements in ultra lightweight concrete
    • Industrial Automation
    • System Design Using Labview
    • LoRa Deployment for Smart Cities
    • Sustainable Solar Energy
    • Autonomous Vehicle Technology
    • Programming And Hands On Experience with CNC
    • Data Science
  • SPOTLIGHTS
    • AI Hackathon
    • Codeathon
    • Idea Factory
    • Drone Voyage
    • Industry Defined Problems
    • Project Design Contest
    • GO-KART Championship
    • Youth Talk
    • Robo Contest
    • Mock Uno
  • TECHNICAL EVENTS
    • Debugging Contest
    • Blind Coding
    • Akruthi
    • Technomancy
    • Circuit Crackers
    • Circuit Routing
    • Fox Hunt
    • Tech Quiz
    • Query Cracking
    • Code Quest
    • Rocket Car
    • Design Doh
  • ACCOMMODATION
  • REGISTRATION
    • User Registration
    • Event Resgistration
    • Pay Online
  • ABOUT
Scroll Down

Autonomous Vehicle Technology

Description

An autonomous vehicle is a vehicle that can guide itself without human conduction. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. An autonomous car is also known as a driverless car, robot car, self-driving car or autonomous vehicle. Two of the most talked about self-driving advancements come from Google and Tesla. They take different approaches: Google is using lidar (a radar-like technology that uses light instead of radio waves) sensor technology and going straight to cars without steering wheels or foot pedals. Self-driving cars combine a variety of sensors to perceive their surroundings, such as radar, lidar, sonar, GPS, odometry and inertial measurement units. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage. The current state-of- the-art computer vision techniques for autonomous driving involves deep learning using convolutional neural networks (CNN). Rather than having humans describe objects – lanes, curbs, traffic signs – which the system tries to detect, CNN based deep learning systems analyze vast quantities of images and recognizes patterns of objects. Besides being efficient, CNN-based computer vision is robust, able to handle situations such as inadequate lane markings as well as poor visibility situations caused by inclement weather or poor roadside lighting.

  • DETAILS

    Registration Fee per head: Rs. 650/-

  • CONTACT

    Mr. K.Tarun +919182123797

    Ms. M. Prathyusha +919398683192

    Mr.G. Abhishek +916304086497

    Mr.Vishnu +919182959553

  • REGISTRATION

    REGISTER NOW

  • COURSE CONTENT

    DOWNLOAD PDF

Address
  • GMR Institute of Technology, GMR Nagar, Rajam-532 127, Srikakulam Dist,
    Andhra Pradesh.
  • stepcone2019@gmrit.org
  • 08941-251592
Site Map
  • About
  • Paper Presentation
  • Workshops
  • Spotlights
  • Technical Events
  • Registration
  • Culturals
  • Accommodation
Maps
Follow us on

STEPCONE 2020