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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

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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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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