Self-Driving Cars

154 papers with code • 0 benchmarks • 14 datasets

Self-driving cars : the task of making a car that can drive itself without human guidance.

( Image credit: Learning a Driving Simulator )


Use these libraries to find Self-Driving Cars models and implementations

Most implemented papers

End to End Learning for Self-Driving Cars

marsauto/europilot 25 Apr 2016

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

ermolenkodev/keras-salient-object-visualisation 25 Apr 2017

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 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

vita-epfl/openpifpaf 3 Mar 2021

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 CVPR 2016

The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes.

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

mit-han-lab/spvnas ECCV 2020

Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely.

Learning a Driving Simulator

commaai/research 3 Aug 2016

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 16 Nov 2016

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.

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

PRBonn/semantic-kitti-api ICCV 2019

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.

3D Packing for Self-Supervised Monocular Depth Estimation

TRI-ML/packnet-sfm CVPR 2020

Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception.