Self-Driving Cars
169 papers with code • 0 benchmarks • 15 datasets
Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
Benchmarks
These leaderboards are used to track progress in Self-Driving Cars
Libraries
Use these libraries to find Self-Driving Cars models and implementations
DDAD
Lost and Found
Shifts
Talk2Car
BDD-X
Argoverse 2 Motion Forecasting
SOD
HPD
OC
ELAS
Most implemented papers
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.
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
This eliminates the need for human engineers to anticipate what is important in an image and foresee all the necessary rules for safe driving.
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.
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
mindspore-ai/models
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CVPR 2020
We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds.
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
mit-han-lab/spvnas
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ECCV 2020
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely.
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.
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.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Editable Neural Networks
We empirically demonstrate the effectiveness of this method on large-scale image classification and machine translation tasks.
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.
