NVIDIA DRIVE Networks
NVIDIA DRIVE™ Networks deliver deep neural network (DNN) solutions for obstacle, path, and wait condition perception. Each network enables situational awareness of different aspects of the vehicle’s surroundings, such as obstacles, lanes, intersections, signs, and lights. DRIVE Networks modules include optimized functionality to precondition the input, run inference on a GPU or Deep Learning Accelerator (DLA), and post-process the network output for its consumption by the perception modules.
DRIVE Networks are ideal for the following objectives:
- Developing perception algorithms for obstacles, path, and wait conditions
- Detecting objects, collision-free space, lanes, and other traffic actors/agents
DriveNet is used for obstacle and wait perception. It detects and classifies objects such as vehicles (including cars and trucks), pedestrians, traffic lights, traffic signs, and bicycles.
ClearSightNet determines if the camera view is blocked. It can predict three classes (clean, blur, blocked).
OpenRoadNet detects free space around the vehicle. It distinguishes the boundary that separates obstacles from the driveable collision-free space.
PathNet predicts full geometry of drivable paths in images and on the three-dimensional road surface, regardless of the presence of lane markings.
MapNet detects visual landmarks such as lanes and poles. It can detect features useful for path perception, as well as localization.
LaneNet detects and classifies lanes, including the vehicle’s ego-lane, adjacent lanes, and non-adjacent lanes.
WaitNet detects an intersection and estimates the distance.
LightNet classifies traffic lights (color, solid, and arrows) detected by DriveNet.
SignNet classifies traffic signs detected by DriveNet, for US and EU.
Developing with DRIVE Networks
How to set up
You will need:
- Host Development PC
- Optional: DRIVE AGX Developer Kit
- DRIVE Software, available through the NVIDIA DRIVE Developer Program for DRIVE AGX
- Install DRIVE Networks using the SDK Manager.
- Experiment with DriveWorks samples.
- Download DRIVE Software documentation
- For the executables: /usr/local/driveworks/bin/
- For source code: /usr/local/driveworks/samples/sr
- To cross-compile and experiment with samples on a DRIVE AGX System instead of the host PC, try the cross compilation tutorial.
How to develop
|Development Tasks||Getting Started|
|Use the deep neural networks included with DRIVE Networks to build obstacle, path, and wait perception algorithms.||
There are several samples included in the “samples” section of DriveWorks SDK Reference guide contained in the DRIVE Software Documentation.
For more detail and examples on how to use the DRIVE Networks APIs, refer to the following samples: