Autonomous Driving

1011 papers with code • 4 benchmarks • 65 datasets

Autonomous driving is the task of driving a vehicle without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)


Use these libraries to find Autonomous Driving models and implementations

Most implemented papers

YOLOX: Exceeding YOLO Series in 2021

Megvii-BaseDetection/YOLOX 18 Jul 2021

In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

MarvinTeichmann/MultiNet 22 Dec 2016

While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving.

nuScenes: A multimodal dataset for autonomous driving

nutonomy/nuscenes-devkit CVPR 2020

Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

BichenWuUCB/squeezeDet 4 Dec 2016

In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.

PointPillars: Fast Encoders for Object Detection from Point Clouds

nutonomy/second.pytorch CVPR 2019

These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

koyeongmin/PINet 16 Feb 2020

In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system.

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

maudzung/Complex-YOLOv4-Pytorch 16 Mar 2018

We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.

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.

The Double Sphere Camera Model

ethz-asl/kalibr 24 Jul 2018

We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i. e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians.

Scalability in Perception for Autonomous Driving: Waymo Open Dataset

open-mmlab/mmdetection3d CVPR 2020

In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.