DeepAI AI Chat
Log In Sign Up

Ignition: An End-to-End Supervised Model for Training Simulated Self-Driving Vehicles

06/29/2018
by   Rooz Mahdavian, et al.
Stanford University
0

We introduce Ignition: an end-to-end neural network architecture for training unconstrained self-driving vehicles in simulated environments. The model is a ResNet-18 variant, which is fed in images from the front of a simulated F1 car, and outputs optimal labels for steering, throttle, braking. Importantly, we never explicitly train the model to detect road features like the outline of a track or distance to other cars; instead, we illustrate that these latent features can be automatically encapsulated by the network.

READ FULL TEXT

page 1

page 3

page 4

page 6

05/16/2019

Autonomous Vehicle Control: End-to-end Learning in Simulated Urban Environments

In recent years, considerable progress has been made towards a vehicle's...
04/25/2016

End to End Learning for Self-Driving Cars

We trained a convolutional neural network (CNN) to map raw pixels from a...
03/24/2020

Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)

Automated vehicles' neural networks suffer from overfit, poor generaliza...
03/30/2017

Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention

Deep neural perception and control networks are likely to be a key compo...
05/16/2022

Bridging Sim2Real Gap Using Image Gradients for the Task of End-to-End Autonomous Driving

We present the first prize solution to NeurIPS 2021 - AWS Deepracer Chal...
02/16/2022

Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars

Convolutional Neural Networks (CNNs) are vulnerable to misclassifying im...
03/23/2023

NVAutoNet: Fast and Accurate 360^∘ 3D Perception For Self Driving

Robust real-time perception of 3D world is essential to the autonomous v...