Conduite autonome

Enrichir des workflows de Deep Learning avec des applications de conduite autonome

Appliquez le Deep Learning à des applications de conduite autonome en utilisant Deep Learning Toolbox™ avec Automated Driving Toolbox™.

Applications

Ground Truth Labeler

Label ground truth data for automated driving applications

Rubriques

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.

Exemples présentés

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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