Browse > Computer Vision > Autonomous Vehicles

# Autonomous Vehicles Edit

80 papers with code · Computer Vision

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 )

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

# 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.

9,751

# nuScenes: A multimodal dataset for autonomous driving

26 Mar 2019traveller59/second.pytorch

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

#2 best model for 3D Object Detection on nuScenes (using extra training data)

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

26 Mar 2018NVIDIA-AI-IOT/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.

704

# Joint 3D Proposal Generation and Object Detection from View Aggregation

6 Dec 2017kujason/avod

We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.

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# Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving

Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications.

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# 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.

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# Flow: A Modular Learning Framework for Autonomy in Traffic

16 Oct 2017flow-project/flow

To enable the study of the full diversity of traffic settings, we first propose to decompose traffic control tasks into modules, which may be configured and composed to create new control tasks of interest.

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# 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.

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# Joint Monocular 3D Vehicle Detection and Tracking

The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.

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# DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

14 Jun 2018BertaBescos/DynaSLAM

And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.

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