Current

Intermediate

Sensor Integration with NVIDIA DRIVE

NVIDIA Deep Learning Institute (DLI) · NVIDIA · Updated March 2026

Platform rating

4.6/5

Champ rating

8.5/10

Duration

4 hours self-paced

Classes

14

Learn sensor integration for autonomous vehicles using NVIDIA DRIVE. Cover camera, lidar, and radar fusion for self-driving systems.

What you'll get

Integrate multiple sensor types for autonomous driving

Implement camera-lidar-radar fusion

Use NVIDIA DRIVE for perception tasks

Build robust sensor pipelines

Course Curriculum

4 chapters

Fit

Best for

Autonomy Engineers

Self-Driving Developers

IoT Engineers

Systems Engineers

Not ideal for

Learners seeking only entry-level overviews

Prerequisites & pricing

Prerequisites

Python and basic ML

Pricing

$30

Certification

Certificate

Autonomous Vehicles

Sensor Fusion

NVIDIA DRIVE

Lidar

Robotics

ShareCompare

Add to StackGo to Course

Alternatives to Sensor Integration with NVIDIA DRIVE

Compare

Current

Self Driving and ROS 2 - Map & Localization

Udemy · Antonio Brandi

4.4

8.2/10

25 hours video

Create a ROS2 Self-Driving robot with Python and C++. Master Robot Localization, Mapping and SLAM.

$12.99

ViewAddCompare

State estimation and localization for self-driving cars from University of Toronto covering Kalman filters, LiDAR, and sensor fusion techniques.

Master multi-sensor fusion for autonomous vehicles: combine LiDAR point clouds, camera images, and radar data for robust perception.

Comprehensive course on Kalman filters, extended Kalman filters, and unscented Kalman filters for robotics state estimation and tracking.