Browse > Computer Vision > Autonomous Vehicles

Autonomous Vehicles

71 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 )

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

Greatest papers with code

Argoverse: 3D Tracking and Forecasting With Rich Maps

CVPR 2019 argoai/argoverse-api

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

3D MULTI-OBJECT TRACKING AUTONOMOUS VEHICLES MOTION FORECASTING OBJECT TRACKING

Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

CVPR 2018 ethz-asl/hf_net

Visual localization enables autonomous vehicles to navigate in their surroundings and augmented reality applications to link virtual to real worlds.

AUTONOMOUS VEHICLES POSE ESTIMATION VISUAL LOCALIZATION

Embedded real-time stereo estimation via Semi-Global Matching on the GPU

13 Oct 2016dhernandez0/sgm

Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles.

AUTONOMOUS VEHICLES DISPARITY ESTIMATION

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

CVPR 2017 huangshiyu13/RPNplus

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

PEDESTRIAN DETECTION

TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

6 Nov 2018ApolloScapeAuto/dataset-api

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.).

AUTONOMOUS VEHICLES TRAFFIC PREDICTION TRAJECTORY PREDICTION

LEAF: A Benchmark for Federated Settings

3 Dec 2018TalwalkarLab/leaf

Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.

AUTONOMOUS VEHICLES META-LEARNING MULTI-TASK LEARNING

Sparse and noisy LiDAR completion with RGB guidance anduncertainty

arXiv 2019 wvangansbeke/Sparse-Depth-Completion

For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions.

AUTONOMOUS VEHICLES DEPTH COMPLETION DEPTH ESTIMATION

Sparse and noisy LiDAR completion with RGB guidance and uncertainty

14 Feb 2019wvangansbeke/Sparse-Depth-Completion

However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input.

AUTONOMOUS VEHICLES DEPTH COMPLETION DEPTH ESTIMATION

LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking

19 Apr 2019bernwang/latte

2) One-click annotation: Instead of drawing 3D bounding boxes or point-wise labels, we simplify the annotation to just one click on the target object, and automatically generate the bounding box for the target.

AUTONOMOUS VEHICLES SENSOR FUSION