Modelling Autonomous Driving and Obstacle Avoidance Using Multi-Modal Fusion Transformer Framework

Authors : Abhinav Singh; Nishant Prakash; Kanishk Bhaskar; Krishnan Rangarajan; Aditya Raj

Volume/Issue : Volume 8 - 2023, Issue 2 - February

Google Scholar :

Scribd :


The papers that we are surveying have many methods that have been presented with different solutions for autonomous driving. One of the few novel representations helps in proving the reasoning for imitation learning in a certain scene where the cameras are used to highlight a certain location which coordinates to waypoints and semantics. In this method the camera follows the car and will show the waypoints at a certain distance ahead of the car at all times while the car is moving. The papers have used attention fields to compress two-dimensional images with features which are best suited for cognitive processing on a discrete aspect of information or in other words obstacles that may appear in front of the car. Therefore, the other model being a Multi-Modal Fusion Transformer is used to combine two separate datasets such as image data and topography data from cameras and distance sensors respectively using attention mechanism. This helps in integrating image data and the topography data that is being received through the camera and distance sensors. The distance sensor maps the surface of all the surroundings where the car is being driven.

Keywords : End-to-End Autonomous Driving, Transformer, 2D Imaging, Self-Attention Model, Imitation Learning.


Paper Submission Last Date
29 - February - 2024

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.