Autonomous Vehicles

236 papers with code • 1 benchmarks • 24 datasets

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: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )

Greatest papers with code

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

Microsoft/AirSim 15 May 2017

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

facebookresearch/pytorch3d 16 Jul 2020

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

Autonomous Vehicles

nuScenes: A multimodal dataset for autonomous driving

open-mmlab/mmdetection3d CVPR 2020

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

Ranked #68 on 3D Object Detection on nuScenes (using extra training data)

3D Object Detection Autonomous Driving

LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving

lgsvl/simulator 7 May 2020

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

Neural circuit policies enabling auditable autonomy

mlech26l/keras-ncp 13 Oct 2020

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

Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios

eleurent/highway-env 9 Jul 2021

Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors.

Autonomous Navigation Motion Planning

Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic

eleurent/highway-env 12 May 2021

On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs).

Autonomous Vehicles Curriculum Learning +1

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

hku-mars/loam_livox 15 Sep 2019

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

ankitdhall/lidar_camera_calibration 27 May 2017

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

Autonomous Vehicles Translation

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

NVIDIA-AI-IOT/redtail 26 Mar 2018

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