Browse > Computer Vision > Autonomous Vehicles

# Autonomous Vehicles Edit

55 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.

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.

8,144

# 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.

627

# 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.

557

# nuScenes: A multimodal dataset for autonomous driving

26 Mar 2019traveller59/second.pytorch

In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.

542

# 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.

403

# Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control

16 Oct 2017flow-project/flow

Flow is a new computational framework, built to support a key need triggered by the rapid growth of autonomy in ground traffic: controllers for autonomous vehicles in the presence of complex nonlinear dynamics in traffic.

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# Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult.

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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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# Argoverse: 3D Tracking and Forecasting With Rich Maps

We use 3D object tracking to mine for more than 300k interesting vehicle trajectories to create a trajectory forecasting benchmark.

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# Joint Monocular 3D Vehicle Detection and Tracking

26 Nov 2018ucbdrive/3d-vehicle-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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