Self-Driving Car Steering Angle Prediction Based on Image Recognition

Abstract

Self-driving vehicles have expanded dramatically over the last few years. Udacity has release a dataset containing, among other data, a set of images with the steering angle captured during driving. The Udacity challenge aimed to predict steering angle based on only the provided images. We explore two different models to perform high quality prediction of steering angles based on images using different deep learning techniques including Transfer Learning, 3D CNN, LSTM and ResNet. If the Udacity challenge was still ongoing, both of our models would have placed in the top ten of all entries.

9 pages 13 figures. Paper originally from CS231n (Stanford) 2017

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• Playing Atari with Deep Reinforcement Learning
• End to End Learning for Self-Driving Cars
• Densely Connected Convolutional Networks
• Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
• Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
• Learning a Driving Simulator

Cited by in corpus (8)

• Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks
• Real-time End-to-End Federated Learning: An Automotive Case Study
• Deep Image Orientation Angle Detection
• TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications
• Brain-Inspired Deep Imitation Learning for Autonomous Driving Systems
• N 2 C : Neural Network Controller Design Using Behavioral Cloning
• In-Vehicle Object Detection in the Wild for Driverless Vehicles
• Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting