Papers with Code 아카이브

Autonomous Vehicles

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

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.

<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/happytianhao/tade

공식 코드

Improving Bird's Eye View Semantic Segmentation by Task Decomposition

https://github.com/happyw1nd/scorelidar

공식 코드

Distilling Diffusion Models to Efficient 3D LiDAR Scene Completion

https://github.com/harborsarah/cafnet

공식 코드

CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimation

https://github.com/harborsarah/get-up

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GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling

https://github.com/harinduravin/dualcam

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DualCam: A Novel Benchmark Dataset for Fine-grained Real-time Traffic Light Detection

https://github.com/harpreets652/highway-traffic-anomaly

공식 코드

Anomalous Motion Detection on Highway Using Deep Learning

https://github.com/harshangrjn/laplacianopt.jl

공식 코드

Optimal Robust Network Design: Formulations and Algorithms for Maximizing Algebraic Connectivity

https://github.com/hasanirtiza/Pedestron

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Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection

https://github.com/hasibzunair/rsud20k

공식 코드

RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving

https://github.com/hatins/deoe

공식 코드

Detecting Every Object from Events

https://github.com/heathcliff-saku/viewfool_

공식 코드

ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints

https://github.com/heitorrapela/HalluciDet

공식 코드

HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged Information

https://github.com/heitorrapela/mipa

공식 코드

MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection

https://github.com/hekmak/Conf-net

공식 코드

Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map Prediction

https://github.com/hemangchawla/3d-groundtruth-traffic-sign-positions

공식 코드

Crowdsourced 3D Mapping: A Combined Multi-View Geometry and Self-Supervised Learning Approach

https://github.com/hendrycks/anomaly-seg

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Scaling Out-of-Distribution Detection for Real-World Settings

https://github.com/henggao12/mmras_plus

공식 코드

Multi-modality Anomaly Segmentation on the Road

https://github.com/hengli/camodocal

공식 코드

3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

https://github.com/henryhcliu/codrivevlm

공식 코드

CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion Planning for Future Autonomous Mobility on Demand Systems

https://github.com/henryzxu/lane-verification

공식 코드

Model-Agnostic Defense for Lane Detection against Adversarial Attack

https://github.com/hermannsblum/fishyscapes

공식 코드

The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation

https://github.com/herolab-uga/matsee

공식 코드

Object-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots

https://github.com/heyuepeng/pfllibvsp

공식 코드

FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction

https://github.com/hfutml/calibration-mllm

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Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models

https://github.com/hht1996ok/admap

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ADMap: Anti-disturbance framework for reconstructing online vectorized HD map

https://github.com/hht1996ok/fastmap

공식 코드

FastMap: Fast Queries Initialization Based Vectorized HD Map Reconstruction Framework

https://github.com/hidayetersindursun/Imitation-Learning-for-Autonomous-Driving-Insights-from-Real-World-Testing

공식 코드

Imitation Learning for Autonomous Driving: Insights from Real-World Testing

https://github.com/hieutrong512/Yoloz

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YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles

https://github.com/himanshugupta1009/extended_space_navigation_pomdp

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Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning

https://github.com/hirotomusiker/clrernet

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CLRerNet: Improving Confidence of Lane Detection with LaneIoU

https://github.com/hitsz-nrsl/de6d

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Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness

https://github.com/hku-mars/gs-sdf

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GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction

https://github.com/hku-mars/loam_livox

공식 코드

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

https://github.com/hku-mars/r3live

공식 코드

R$^3$LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator

https://github.com/hkust-aerial-robotics/dsp

공식 코드

Trajectory Prediction with Graph-based Dual-scale Context Fusion

https://github.com/hkust-aerial-robotics/simpl

공식 코드

SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.