Self-Driving Cars
120 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
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Libraries
Use these libraries to find Self-Driving Cars models and implementations
Lost and Found
comma.ai
Talk2Car
Shifts
DDAD
BDD-X
SOD
ELAS
SESIV
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.
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.
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.
VisualBackProp: efficient visualization of CNNs
devansh20la/VisualBackprop
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We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction.
PointPainting: Sequential Fusion for 3D Object Detection
Song-Jingyu/PointPainting
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CVPR 2020
Surprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the literature.
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
xk-huang/yet-another-vectornet
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CVPR 2020
Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e. g. pedestrians and vehicles) and road context information (e. g. lanes, traffic lights).
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.
