About

Autonomous vehicles is the task of making a vehicle that can guide itself 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: AirSim )

Benchmarks

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Subtasks

Datasets

Greatest papers with code

AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

15 May 2017Microsoft/AirSim

Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process.

AUTONOMOUS VEHICLES

Accelerating 3D Deep Learning with PyTorch3D

16 Jul 2020facebookresearch/pytorch3d

We address these challenges by introducing PyTorch3D, a library of modular, efficient, and differentiable operators for 3D deep learning.

AUTONOMOUS VEHICLES

Commands 4 Autonomous Vehicles (C4AV) Workshop Summary

18 Sep 2020makcedward/nlpaug

In this work, we deviate from recent, popular task settings and consider the problem under an autonomous vehicle scenario.

4 AUTONOMOUS VEHICLES VISUAL GROUNDING

LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving

7 May 2020lgsvl/simulator

Testing autonomous driving algorithms on real autonomous vehicles is extremely costly and many researchers and developers in the field cannot afford a real car and the corresponding sensors.

AUTONOMOUS DRIVING

nuScenes: A multimodal dataset for autonomous driving

CVPR 2020 traveller59/second.pytorch

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

Ranked #22 on 3D Object Detection on nuScenes (using extra training data)

3D OBJECT DETECTION AUTONOMOUS DRIVING

On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

26 Mar 2018NVIDIA-Jetson/redtail

Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving.

AUTONOMOUS VEHICLES STEREO DEPTH ESTIMATION

Neural circuit policies enabling auditable autonomy

13 Oct 2020mlech26l/keras-ncp

A central goal of artificial intelligence in high-stakes decision-making applications is to design a single algorithm that simultaneously expresses generalizability by learning coherent representations of their world and interpretable explanations of its dynamics.

AUTONOMOUS VEHICLES DECISION MAKING

Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV

15 Sep 2019hku-mars/loam_livox

LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot's pose and build high-precision, high-resolution maps of the surrounding environment.

AUTONOMOUS NAVIGATION

LiDAR-Camera Calibration using 3D-3D Point correspondences

27 May 2017ankitdhall/lidar_camera_calibration

With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors.

AUTONOMOUS VEHICLES