Papers with Code 아카이브

Autonomous Driving

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 6
평가표: 4
보존된 평가 기록: 23

Autonomous driving is the task of driving a vehicle 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: 0.5">(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)</span>

코드

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

https://github.com/alniyatrui/cafe-ad

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CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous Driving

https://github.com/alphaplustt/daocc

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DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction

https://github.com/alshami52/COOOL_benchmark

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COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving

https://github.com/alshedivat/keras-gp

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Learning Scalable Deep Kernels with Recurrent Structure

https://github.com/alshedivat/kgp

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Learning Scalable Deep Kernels with Recurrent Structure

https://github.com/alvariseppanen/4denoisenet

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4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds

https://github.com/alvariseppanen/smednet

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Multi-Echo Denoising in Adverse Weather

https://github.com/amai-gsu/mambev

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MamBEV: Enabling State Space Models to Learn Birds-Eye-View Representations

https://github.com/amaralibey/mixvpr

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MixVPR: Feature Mixing for Visual Place Recognition

https://github.com/amirhosseinzlf/smarla

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SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents

https://github.com/amirhosseinzlf/starla

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A Search-Based Testing Approach for Deep Reinforcement Learning Agents

https://github.com/amnondrory/lidarregistration

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Stress-Testing Point Cloud Registration on Automotive LiDAR

https://github.com/amy-choi/AttackDefenseYOLO

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Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios

https://github.com/andreaceccarelli/metrics_model

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Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures

https://github.com/andreaconti/lidar-confidence

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Unsupervised confidence for LiDAR depth maps and applications

https://github.com/andrewcaunes/ia4markings

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seg_3D_by_PC2D: Multi-View Projection for Domain Generalization and Adaptation in 3D Semantic Segmentation

https://github.com/andyj1/doubly-contrastive-semseg

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Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather

https://github.com/anita-hu/sim2real-lane-detection

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Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving

https://github.com/anita-hu/simulanes

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Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving

https://github.com/anonoymous9423013/anonymous_paper

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Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systems

https://github.com/anonymouse-9c53tp182bvz/vireo

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Leveraging Depth and Language for Open-Vocabulary Domain-Generalized Semantic Segmentation

https://github.com/ansonlcy/TSceneJAL

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TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object Detection

https://github.com/antonioo-c/geodrive

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GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control

https://github.com/anxq/dapt

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Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimation

https://github.com/apanariello4/DistFormer

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DistFormer: Enhancing Local and Global Features for Monocular Per-Object Distance Estimation

https://github.com/apostovan21/binarizedneuralnetwork

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Architecturing Binarized Neural Networks for Traffic Sign Recognition

https://github.com/apple2373/min-seg-road

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Minimizing Supervision for Free-space Segmentation

https://github.com/aras62/vision-based-prediction

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Deep Learning for Vision-based Prediction: A Survey

https://github.com/arcaninez/odm3d

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ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object Detection

https://github.com/argoai/argoverse-api

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Argoverse: 3D Tracking and Forecasting with Rich Maps

https://github.com/argoverse/av2-api

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Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting

https://github.com/aron-lin/seqtrack3d

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SeqTrack3D: Exploring Sequence Information for Robust 3D Point Cloud Tracking

https://github.com/artemsavkin/secogan

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Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation

https://github.com/aryan-at-ul/electronic-imaging-2025-paper-4492

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Image Segmentation: Inducing graph-based learning

https://github.com/asguard-uci/ld-metric

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Towards Driving-Oriented Metric for Lane Detection Models

https://github.com/asharakeh/pod_compare

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A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving

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