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/huaiyuanxu/3d-occupancy-perception

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A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective

https://github.com/huang-yh/gaussianformer

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GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Prediction

https://github.com/huang-yh/selfocc

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SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction

https://github.com/huangzhe885/codiff

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CoDiff: Conditional Diffusion Model for Collaborative 3D Object Detection

https://github.com/huangzhe885/roco

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RoCo:Robust Collaborative Perception By Iterative Object Matching and Pose Adjustment

https://github.com/huanzhang12/ZOO-Attack

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ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

https://github.com/huawei-noah/SMARTS

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SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

https://github.com/hugotessier-lab/neural-network-shrinking

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Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU

https://github.com/huixiancheng/PCB-RandNet

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PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scene

https://github.com/huixiancheng/cenet

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CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving

https://github.com/hustvl/alphadrive

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AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning

https://github.com/hustvl/diffusiondrive

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DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving

https://github.com/hustvl/gausstr

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GaussTR: Foundation Model-Aligned Gaussian Transformer for Self-Supervised 3D Spatial Understanding

https://github.com/hustvl/gkt

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Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer

https://github.com/hustvl/lanegap

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Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction

https://github.com/hustvl/maptr

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MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction

https://github.com/hustvl/mim4d

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MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation Learning

https://github.com/hustvl/senna

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Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving

https://github.com/hustvl/symphonies

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Symphonize 3D Semantic Scene Completion with Contextual Instance Queries

https://github.com/hustvl/vad

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VAD: Vectorized Scene Representation for Efficient Autonomous Driving

https://github.com/hustvl/vma

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VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene

https://github.com/hustvl/yolop

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YOLOP: You Only Look Once for Panoptic Driving Perception

https://github.com/huyjj/ADUNet/blob/main/SwinIR.py

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Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal

https://github.com/hvxlab/hvdetfusion

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HVDetFusion: A Simple and Robust Camera-Radar Fusion Framework

https://github.com/hxmap/hrmapnet

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Enhancing Vectorized Map Perception with Historical Rasterized Maps

https://github.com/hxmap/mapqr

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Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction

https://github.com/hxzhong1997/fsba

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Few-Shot Backdoor Attacks on Visual Object Tracking

https://github.com/hyan-yao/lilodriver

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LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios

https://github.com/hysonlab/pointdistill

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Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing

https://github.com/hzxie/awesome-3d-scene-generation

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3D Scene Generation: A Survey

https://github.com/iceory/reo

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Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial Transformation

https://github.com/icey-zhang/E2E-MFD

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E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection

https://github.com/ido90/CeSoR

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Efficient Risk-Averse Reinforcement Learning

https://github.com/ifnspaml/CIL_Segmentation

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Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old Labels

https://github.com/ifnspaml/cbna

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Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation

https://github.com/ika-rwth-aachen/occuq

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OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction

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