Autonomous Vehicles by duong Nguyen https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a Được tạo với những rung cảm mãnh liệt en-us 2021-12-05 14:37:11 UTC 2021-12-15 06:17:39 UTC hello@padlet.com Autonomous Vehicles thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929600904 Group 2 members:
Phạm Minh Chiến 20194491
Nguyễn Thành Dương 20194531

]]> 2021-12-05 14:42:42 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929600904 thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929602942 2021-12-05 14:44:44 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929602942 thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929603598 2021-12-05 14:45:25 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929603598 A self-driving car’s view of the world often includes bounding boxes — cars, pedestrians and stop signs neatly encased in red and green rectangles.In the real world, however, not everything fits in a box.For highly complex driving scenarios, such as a construction zone marked by traffic cones, a sofa chair or other road debris in the middle of the highway, or a pedestrian unloading a moving van with cargo sticking out the back, it’s helpful for the vehicle’s perception software to provide a more detailed understanding of its surroundings. thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929603890 2021-12-05 14:45:43 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929603890 thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929604359 2021-12-05 14:46:01 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1929604359 thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1941859603 2021-12-11 15:57:16 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1941859603 thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1941859892 2021-12-11 15:57:36 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1941859892 Editor’s note: This is the latest post in our NVIDIA DRIVE Labs series, which takes an engineering-focused look at individual autonomous vehicle challenges and how NVIDIA DRIVE addresses them. Catch up on all of our automotive posts, here.A self-driving car’s view of the world often includes bounding boxes — cars, pedestrians and stop signs neatly encased in red and green rectangles.In the real world, however, not everything fits in a box.For highly complex driving scenarios, such as a construction zone marked by traffic cones, a sofa chair or other road debris in the middle of the highway, or a pedestrian unloading a moving van with cargo sticking out the back, it’s helpful for the vehicle’s perception software to provide a more detailed understanding of its surroundings.Such fine-grained results can be obtained by segmenting image content with pixel-level accuracy, an approach known as panoptic segmentation.With panoptic segmentation, the image can be accurately parsed for both semantic content (which pixels represent cars vs. pedestrians vs. drivable space), as well as instance content (which pixels represent the same car vs. different car objects).Planning and control modules can use panoptic segmentation results from the perception system to better inform autonomous driving decisions. For example, the detailed object shape and silhouette information helps improve object tracking, resulting in a more accurate input for both steering and acceleration. It can also be used in conjunction with dense (pixel-level) distance-to-object estimation methods to help enable high-resolution 3D depth estimation of a scene.Single DNN ApproachNVIDIA’s approach achieves pixel-level semantic and instance segmentation of a camera image using a single, multi-task learning deep neural network. This approach enables us to train a panoptic segmentation DNN that understands the scene as a whole versus piecewise.It’s also efficient. Just one end-to-end DNN can extract all this rich perception information while achieving per-frame inference times of approximately 5ms on our embedded in-car NVIDIA DRIVE AGX platform. This is much faster than state-of-the-art segmentation methods.DRIVE AGX makes it possible to simultaneously run the panoptic segmentation DNN along with many other DNN networks and perception functions, localization, and planning and control software in real time. thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1947298033 2021-12-14 15:26:56 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1947298033 thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1947299782 2021-12-14 15:27:33 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1947299782 Work assignment thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1947322446 - Chiến:
+History of autunomous vehicle
+How to self- driving car work?
- Dương:

+Advantages and disadvantages of autonomous vehicles

+Challenges of AI in Autonomous Vehicles

]]> 2021-12-14 15:35:07 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1947322446 slide thanhduongqwaszx https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1947555170
vượt quá giới hạn]]> 2021-12-14 17:05:02 UTC https://padlet.com/thanhduongqwaszx/e1qplonk2gzf3x4a/wish/1947555170