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 )


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.

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.

PointPainting: Sequential Fusion for 3D Object Detection

Song-Jingyu/PointPainting 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 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

peikexin9/deepxplore 18 May 2017

First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.