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/ying-fu/lmot

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Multi-Object Tracking in the Dark

https://github.com/yinghuixing/ms-detr

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MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization

https://github.com/yinglv1106/cainet

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Context-Aware Interaction Network for RGB-T Semantic Segmentation

https://github.com/yinjunbo/is-fusion

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IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

https://github.com/yinjunbo/proficientteachers

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Semi-supervised 3D Object Detection with Proficient Teachers

https://github.com/yinjunbo/ssda3d

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SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud

https://github.com/yixiao1/Action-Based-Representation-Learning

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Action-Based Representation Learning for Autonomous Driving

https://github.com/yizhou-wang/RODNet

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RODNet: Radar Object Detection Using Cross-Modal Supervision

https://github.com/yjzhai-cs/fedrav

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FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles

https://github.com/yjzhai-cs/vehicle-10

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FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles

https://github.com/ylwhxht/l4dr

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L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection

https://github.com/ynw2021/fend

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FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction

https://github.com/yonghyun-ahn/line-out-of-distribution-detection-by-leveraging-important-neurons

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LINe: Out-of-Distribution Detection by Leveraging Important Neurons

https://github.com/youkely/InfrasCal

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Infrastructure-based Multi-Camera Calibration using Radial Projections

https://github.com/youmi-zym/go-slam

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GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction

https://github.com/youngfly/Dynamic-Adjustment-Fusion

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Explore the LiDAR-Camera Dynamic Adjustment Fusion for 3D Object Detection

https://github.com/youngskkim/CRN

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CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception

https://github.com/youngzhou1999/smartpretrain

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SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction

https://github.com/youquanl/m3net

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Multi-Space Alignments Towards Universal LiDAR Segmentation

https://github.com/youshaamurhij/ofmpnet

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OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment

https://github.com/yuangao-tum/riskaware-scenario-analyse

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From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios

https://github.com/yuanpengtu/sleeg

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Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes

https://github.com/yuantianyuan01/presight

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PreSight: Enhancing Autonomous Vehicle Perception with City-Scale NeRF Priors

https://github.com/yuantianyuan01/streammapnet

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StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction

https://github.com/yuanyuan-yuan/gcert

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Precise and Generalized Robustness Certification for Neural Networks

https://github.com/yuanyunshuang/cosense3d

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StreamLTS: Query-based Temporal-Spatial LiDAR Fusion for Cooperative Object Detection

https://github.com/yuanyunshuang/fpv_rcnn

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Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving

https://github.com/yuanyunshuang/gevbev

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Generating Evidential BEV Maps in Continuous Driving Space

https://github.com/yuchen2199/explainable-driver-attention-prediction

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Where, What, Why: Towards Explainable Driver Attention Prediction

https://github.com/yueatsprograms/ttt_cifar_release

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Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

https://github.com/yueatsprograms/ttt_imagenet_release

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Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

https://github.com/yuedumingz/PointCore

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PointCore: Efficient Unsupervised Point Cloud Anomaly Detector Using Local-Global Features

https://github.com/yueruchen/Pedestrian-Synthesis-GAN

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Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

https://github.com/yuhaozhang7/NGD-SLAM

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NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU

https://github.com/yuhengliu02/control-3d-scene

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Controllable 3D Outdoor Scene Generation via Scene Graphs

https://github.com/yujun0-0/mma-net

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VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection

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