Automated Driving Using Deep Learning

Extend deep learning workflows with automated driving applications

Apply deep learning to automated driving applications by using Deep Learning Toolbox™ together with Automated Driving Toolbox™.

Apps

Ground Truth Labeler

Label ground truth data for automated driving applications

Topics

Train a Deep Learning Vehicle Detector
This example shows how to train a vision-based vehicle detector using deep learning.
Create Occupancy Grid Using Monocular Camera and Semantic Segmentation
This example shows how to estimate free space around a vehicle and create an occupancy grid using semantic segmentation and deep learning.
Automate Ground Truth Labeling for Semantic Segmentation(Automated Driving Toolbox)
Use a pretrained semantic segmentation algorithm to segment an image, and use this algorithm to automate ground truth labeling.
Train Deep Learning Semantic Segmentation Network Using 3-D Simulation Data
This example shows how to use 3-D simulation data to train a semantic segmentation network and fine-tune it to real-world data using generative adversarial networks (GANs).
Track Multiple Vehicles Using a Camera(Automated Driving Toolbox)
Detect and track multiple vehicles with a monocular camera mounted in a vehicle.

Featured Examples

Train Deep Learning Semantic Segmentation Network Using 3-D Simulation Data

Use 3-D simulation data to train a semantic segmentation network and fine-tune it to real-world data using generative adversarial networks (GANs).

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Train a Deep Learning Vehicle Detector

Train a vision-based vehicle detector using deep learning.

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Create Occupancy Grid Using Monocular Camera and Semantic Segmentation

Estimate free space around a vehicle and create an occupancy grid using semantic segmentation and deep learning. You then use this occupancy grid to create a vehicle costmap, which can be used to plan a path.

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