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Bmw Videolar

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HALP (Hardware-Aware Latency Pruning), is a new method designed to adapt convolutional neural networks (CNNs) and transformer-based architectures for real-time performance. In this video, learn how HALP optimizes pre-trained models to maximize compute utilization.

The concept of "3D occupancy prediction" is critical to the development of safe and robust self-driving systems. In this episode, we go beyond the traditional bird's eye view approach and showcase NVIDIA's 3D perception technology, which won the 3D Occupancy Prediction Challenge at CVPR 2023.

Early Grid Fusion (EGF) is a new technique that enhances near-field obstacle avoidance in automatic parking assist. EGF combines machine-learned cameras and ultrasonic sensors to accurately detect and perceive surrounding obstacles, providing a 360-degree surround view.

Precise environmental perception is critical for autonomous vehicle (AV) safety, especially when handling unseen conditions. In this episode of DRIVE Labs, we discuss a Vision Transformer model called SegFormer, which generates robust semantic segmentation while maintaining high efficiency. This video introduces the mechanism behind SegFormer that enables its robustness and efficiency.

Testing autonomous vehicles (AVs) in potential near-accident scenarios is critical for evaluating safety, but is difficult and unsafe to do in the real world. In this episode of DRIVE Labs, we discuss a new method from NVIDIA researchers called STRIVE (Stress-Test Drive), which automatically generates potential accident scenarios in simulation for AVs.

Understanding speed limit signs may seem like a straightforward task, but it can quickly become more complex in situations in which different restrictions apply to different lanes, or when driving in a new country. This episode of DRIVE Labs shows how AI-based live perception can help AVs better understand the complexities of speed limit signs, using both explicit and implicit cues.

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.

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.

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.

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.

In the latest edition of NVIDIA DRIVE Dispatch, learn about generating 4D reconstruction from a single drive as well as PredictionNet, a deep neural network (DNN) that can be used for predicting future behavior and trajectories of road agents in autonomous vehicle applications. We also take a look at testing for the New Car Assessment Program (NCAP) with NVIDIA DRIVE Sim.

See the latest advances in autonomous vehicle perception from NVIDIA DRIVE. In this dispatch, we use ultrasonic sensors to detect the height of surrounding objects in low-speed areas such as parking lots. RadarNet DNN detects drivable free space, while the Stereo Depth DNN estimates the environment geometry.

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.

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.

Contudo, aps analisar embargos de declarao apresentados pelas empresas, o Tribunal do Cade esclareceu omisses da deciso proferida em abril e, por maioria, decidiu autorizar a operao a partir da celebrao de um novo ACC.

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