Deep Learning and Autonomous Driving

Published: 09 Oct 2015 Category: deep_learning


(Toronto) CSC2541: Visual Perception for Autonomous Driving, Winter 2016

(MIT) 6.S094: Deep Learning for Self-Driving Cars

How to Land An Autonomous Vehicle Job: Coursework


An Empirical Evaluation of Deep Learning on Highway Driving

Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving

Optical Flow augmented Semantic Segmentation networks for Automated Driving

AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving

Design of Real-time Semantic Segmentation Decoder for Automated Driving

Hierarchical Multi-task Deep Neural Network Architecture for End-to-End Driving


DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

End to End Learning for Self-Driving Cars

End-to-End Deep Learning for Self-Driving Cars

Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?

BRAIN4CARS: Cabin Sensing for Safe and Personalized Driving

Brain4Cars: Sensory-Fusion Recurrent Neural Models for Driver Activity Anticipation

Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture

Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture

Long-term Planning by Short-term Prediction

Learning a Driving Simulator open-sources the data it used for its first successful driverless trips

Autonomous driving challenge: To Infer the property of a dynamic object based on its motion pattern using recurrent neural network

Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

Learning from Maps: Visual Common Sense for Autonomous Driving

SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks

  • intro: Accepted at the Deep Learning for Action and Interaction Workshop, 30th Conference on Neural Information Processing Systems (NIPS 2016)
  • arxiv:

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention

Virtual to Real Reinforcement Learning for Autonomous Driving

Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Deep Reinforcement Learning framework for Autonomous Driving

Systematic Testing of Convolutional Neural Networks for Autonomous Driving

MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving

CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving

LaneNet: Real-Time Lane Detection Networks for Autonomous Driving

Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision

Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability

Pixel and Feature Level Based Domain Adaption for Object Detection in Autonomous Driving

Multi-task Learning with Attention for End-to-end Autonomous Driving


Caffe-Autopilot: Car autopilot software that uses C++, BVLC Caffe, OpenCV, and SFML

Self Driving Car Demo

Autoware: Open-source software for urban autonomous driving

Open Sourcing 223GB of Driving Data

Machine Learning for RC Cars

Self Driving (Toy) Ferrari

Lane Finding Project for Self-Driving Car ND

Instructions on how to get your development environment ready for Udacity Self Driving Car (SDC) Challenges

DeepDrive: self-driving car AI

DeepDrive setup: Run a self-driving car simulator from the comfort of your own PC

DeepTesla: End-to-End Learning from Human and Autopilot Driving

DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

Autonomous Driving in Reality with Reinforcement Learning and Image Translation

End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception


Self-driving cars: How far away are we REALLY from autonomous cars?(7 Aug 2015)

Practice makes perfect: Driverless cars will learn from their mistakes(9 Oct 2015)

Eyes on the Road: How Autonomous Cars Understand What They’re Seeing

Human-in-the-loop deep learning will help drive autonomous cars

Using reinforcement learning in Python to teach a virtual car to avoid obstacles

Autonomous RC car using Raspberry Pi and Neural Networks

The Road Ahead: Autonomous Vehicles Startup Ecosystem

Deep Driving - A revolutionary AI technique is about to transform the self-driving car

Visualizations for regressing wheel steering angles in self driving cars with Keras