# 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.

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

15 May 2017

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

12,410

# Accelerating 3D Deep Learning with PyTorch3D

16 Jul 2020

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

5,398

# nuScenes: A multimodal dataset for autonomous driving

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)

1,818

# LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving

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.

1,602

# Neural circuit policies enabling auditable autonomy

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.

1,066

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

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.

1,015

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

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).

1,015

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

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.

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

27 May 2017

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

916

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

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.

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