Deep Learning for Autonomous Driving

Table of Contents

  1. Autonomous Driving Deep Learning Architecture Design

1.1 Modular Approach

1.2 End-to-end Approach

1.3 Hybrid Approach

2. Autonomous Driving Dataset, Simulation, and Testing

2.1 Kitti Dataset

2.2 Simulation

2.3 Testing and Evaluation

3. Deep Learning for Autonomous Driving

3.1 Convolutional Neural Network for Autonomous Driving

3.2 Recurrent Neural Network for Autonomous Driving

3.3 Deep Reinforcement Learning for Autonomous Driving

4. Modular Deep Learning Approach

4.1 Localization and Mapping

4.2 Object Detection and Tracking

4.3 Scene Understanding

4.4 Driver State Understanding

5. End-to-End Deep Learning Approach

5.1 Imitation Learning

5.2 Machine Controller Guided Deep Reinforcement Learning

5.3 Replanning for Deep Reinforcement Learning

6. Hybrid Approach

7. Deep Unsupervised Learning for Autonomous Driving

7.1 Generative Adversarial Network

7.2 Generative Adversarial Network for Autonomous Driving Simulation

7.3 Generative Adversarial Network for Optical Flow Estimation

7.4 Generative Adversarial Network for Car Accident Prediction