The NVIDIA DRIVE™ Software team is constantly innovating, developing redundant and diverse deep neural networks for safe and robust self-driving systems that are transforming the industry.
Select tab below for an inside look at the process.
Short-form videos that dive into specific self-driving algorithms.
Diverse and redundant sensors, such as camera and radar, are necessary for AV perception. However, radar sensors that leverage only traditional processing may not be up to the task. In this DRIVE Labs video, we show how AI can address the shortcomings of traditional radar signal processing in distinguishing moving and stationary objects to bolster AV perception.
In this DRIVE Labs episode, we show how DRIVE IX perceives driver attention, activity, emotion, behavior, posture, speech, gesture and mood. Driver perception is a key aspect of the platform that enables the AV system to ensure a driver is alert and paying attention to the road. It also enables the AI system to perform cockpit functions that are more intuitive and intelligent.
In this DRIVE Labs episode, we show how software-defined AI techniques can be used to significantly improve performance and functionality of our light source perception deep neural network (DNN) — increasing range, adding classification capabilities and more — in a matter of weeks.
Self-driving cars rely on AI to anticipate traffic patterns and safely maneuver in a complex environment. In this DRIVE Labs episode, we demonstrate how our PredictionNet deep neural network can predict future paths of other road users using live perception and map data.
Handling intersections autonomously presents a complex set of challenges for self-driving cars. Earlier in the DRIVE Labs series, we demonstrated how we detect intersections, traffic lights and traffic signs with the WaitNet DNN. And how we classify traffic light state and traffic sign type with the LightNet and SignNet DNNs. In this episode, we go further to show how NVIDIA uses AI to perceive the variety of intersection structures that an autonomous vehicle could encounter on a daily drive.
Active learning makes it possible for AI to automatically choose the right training data. An ensemble of dedicated DNNs goes through a pool of image frames, flagging frames that it finds to be confusing. These frames are then labeled and added to the training dataset. This process can improve DNN perception in difficult conditions, such as nighttime pedestrian detection.
Traditional methods for processing lidar data pose significant challenges, such as the ability to detect and classify different types of objects, scenes and weather conditions, as well as limitations in performance and robustness. Our multi-view LidarNet deep neural network uses multiple perspectives, or views, of the scene around the car to address these lidar processing challenges.
Localization is a critical capability for autonomous vehicles, computing their three dimensional (3D) location inside of a map, including 3D position, 3D orientation, and any uncertainties in these position and orientation values. In this DRIVE Labs, we show how our localization algorithms make it possible to achieve high accuracy and robustness using mass market sensors and HD maps.
Watch how we evolved our LaneNet DNN into our high-precision MapNet DNN. This evolution includes an increase in detection classes to also cover road markings and vertical landmarks (e.g. poles) in addition to lane line detection. It also leverages end-to-end detection that provides faster in-car inference.
The ability to detect and react to objects all around the vehicle makes it possible to deliver a comfortable and safe driving experience. In this DRIVE Labs video, we explain why it is essential to have a sensor fusion pipeline which can combine camera and radar inputs for robust surround perception.
For highly complex driving scenarios, it’s helpful for the autonomous vehicle’s perception system to provide a more detailed understanding of its surroundings. With our panoptic segmentation DNN approach, we can obtain such fine-grained results by segmenting image content with pixel-level accuracy.
High beam lights can increase night-time visibility range of standard headlights significantly; however, they can create hazardous glare to other drivers. We've trained a camera-based deep neural network (DNN) — called AutoHighBeamNet — to automatically generate control outputs for the vehicle’s high beam light system, increasing night time driving visibility and safety.
Feature tracking estimates the pixel-level correspondences and pixel-level changes among adjacent video frames, providing critical temporal and geometric information for object motion/velocity estimation, camera self-calibration and visual odometry.
Our ParkNet deep neural network can detect an open parking spot under a variety of conditions. Watch how it handles both indoor and outdoor spaces, separated by single, double or faded lane markings, as well as differentiates between occupied, unoccupied and partially obscured spots.
This special edition DRIVE Labs episode shows how NVIDIA DRIVE AV Software combines the essential building blocks of perception, localization, and planning/control to drive autonomously on public roads around our headquarters in Santa Clara, Calif.
NVIDIA DRIVE AV software uses a combination of DNNs to classify traffic signs and lights. Watch how our LightNet DNN classifies traffic light shape (e.g. solid versus arrow) and state (i.e. color), while the SignNet DNN identifies traffic sign type.
Our Safety Force Field (SFF) collision avoidance software acts as an independent supervisor on the actions of the vehicle’s primary planning and control system. SFF double-checks controls that were chosen by the primary system, and if it deems them to be unsafe, it will veto and correct the primary system’s decision.
Deep neural network (DNN) processing has emerged as an important AI-based technique for lane detection. Our LaneNet DNN increases lane detection range, lane edge recall, and lane detection robustness with pixel-level precision.
Computing distance to objects using image data from a single camera can create challenges when it comes to hilly terrain. With the help of deep neural networks, autonomous vehicles can predict 3D distances from 2D images.
See how we use our six-camera setup to see 360 degrees around the car and track objects as they move in the surrounding environment.
Autonomous vehicles must use computational methods and sensor data, such as a sequence of images, to figure out how an object is moving in time.
ClearSightNet DNN is trained to evaluate cameras’ ability to see clearly and determine causes of occlusions, blockages and reductions in visibility.
Learn how the WaitNet DNN is able to detect intersections without using a map.
This trio of DNNs builds and evaluates confidence for center path and lane line predictions, as well as lane changes/splits/merges.
Brief updates from our AV fleet, highlighting new breakthroughs.
In this episode of NVIDIA DRIVE Dispatch, we show advances in traffic motion prediction, road marking detection, 3D synthetic data visualization and more.
In this episode of NVIDIA DRIVE Dispatch, we show advances in driveable path perception, camera and radar localization, parking space detection and more.
In this episode of NVIDIA DRIVE Dispatch, we show advances in synthetic data for improved DNN training, radar-only perception to predict future motion, MapStream creation for crowdsourced HD maps and more.
See the latest advances in DepthNet, road marking detection, multi-radar egomotion estimation, cross-camera feature tracking, and more.
Explore progress in parking spot detection, 3D location in landmark detection, our first autonomous drive using an automatically generated MyRoute map and road plane, and suspension estimation.
Check out advances in scooter classification and avoidance, traffic light detection, 2D cuboid stability, 3D freespace from camera annotations, lidar perception pipeline, and headlight/tail light/street light perception.
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