Self-Driving Cars
93 papers with code • 0 benchmarks • 11 datasets
Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
Benchmarks
comma.ai
Lost and Found
BDD-X
Talk2Car
DDAD
ELAS
Ward2ICU
SESIV
Shifts
Greatest papers with code
Fast Algorithms for Convolutional Neural Networks
XiaoMi/mace
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CVPR 2016
The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes.
Learning a Driving Simulator
commaai/research
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Comma. ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road.
End to End Learning for Self-Driving Cars
The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.
Leveraging Latent Features for Local Explanations
As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level.
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e. g., a person's body joints) in multiple frames.
Ranked #4 on
Multi-Person Pose Estimation
on COCO
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
To minimize the number of cameras needed for surround perception, we utilize fisheye cameras.
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion
TRI-ML/packnet-sfm
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CVPR 2021
Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars.
3D Packing for Self-Supervised Monocular Depth Estimation
TRI-ML/packnet-sfm
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CVPR 2020
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception.
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
agrimgupta92/sgan
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CVPR 2018
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
Ranked #12 on
Trajectory Prediction
on Stanford Drone
MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
The basis for most vision based applications like robotics, self-driving cars and potentially augmented and virtual reality is a robust, continuous estimation of the position and orientation of a camera system w. r. t the observed environment (scene).
