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/Trusted-AI/AIX360

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Leveraging Latent Features for Local Explanations

https://github.com/Tsinghua-MARS-Lab/DenseTNT

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DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution

https://github.com/Tsinghua-MARS-Lab/Occ3D

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Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving

https://github.com/Tsinghua-MARS-Lab/ViP3D

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ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries

https://github.com/TuSimple/LiDAR_SOT

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Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences

https://github.com/Turoad/clrnet

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CLRNet: Cross Layer Refinement Network for Lane Detection

https://github.com/Twizwei/bddoia_project

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Explainable Object-induced Action Decision for Autonomous Vehicles

https://github.com/UMBCvision/Universal-Litmus-Patterns

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Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

https://github.com/UMich-BipedLab/LiDARTag

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LiDARTag: A Real-Time Fiducial Tag System for Point Clouds

https://github.com/UMich-BipedLab/global_pose_estimation_for_optimal_shape

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Optimal Target Shape for LiDAR Pose Estimation

https://github.com/USTCAVSA/TSL

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Traffic Scenario Logic: A Spatial-Temporal Logic for Modeling and Reasoning of Urban Traffic Scenarios

https://github.com/UTAustin-SwarmLab/NeuS-V

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Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification

https://github.com/UVA-BezzoRobotics-AMRLab/cbf_tracking

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Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments

https://github.com/Uehwan/rmd

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Revisiting Self-Supervised Monocular Depth Estimation

https://github.com/UsmanMaqbool/why-so-deep

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Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition

https://github.com/VCCIV/BLVD

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BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving

https://github.com/VDIGPKU/OpenAD

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OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection

https://github.com/VLISLAB/360-DL-Survey

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Deep Learning for Omnidirectional Vision: A Survey and New Perspectives

https://github.com/VRI-UFPR/BeyondSight

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BEyond observation: an approach for ObjectNav

https://github.com/VRU-intention/casr

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Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation

https://github.com/VSlabRobotics/PFSD

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PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured Environments

https://github.com/ValentinaZangirolami/DRL

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Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning

https://github.com/VeriDeep/DLV

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Safety Verification of Deep Neural Networks

https://github.com/VerwimpEli/CLAD

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CLAD: A realistic Continual Learning benchmark for Autonomous Driving

https://github.com/VisualAIKHU/SAMPD

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Multispectral Pedestrian Detection with Sparsely Annotated Label

https://github.com/W-zx-Y/DANNet

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DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

https://github.com/WangLibo1995/GeoSeg

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Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

https://github.com/WangYueFt/pillar-od

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Pillar-based Object Detection for Autonomous Driving

https://github.com/WaterScenes/USVTrack-Baseline-YOLO-Tracking

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WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces

https://github.com/Weafre/CNeT

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Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model

https://github.com/WeikaiTan/Toronto-3D

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Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

https://github.com/Weixy21/ABNet

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ABNet: Attention BarrierNet for Safe and Scalable Robot Learning

https://github.com/WesleyHsieh0806/TAO-Amodal

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TAO-Amodal: A Benchmark for Tracking Any Object Amodally

https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning

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Tree-based Intelligent Intrusion Detection System in Internet of Vehicles

https://github.com/WhiteByeBye/Post-Quantum-Secure-Blockchained-Federated-Learning

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Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing

https://github.com/Willy1005/2025-IJCAI-RDML

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Reliable Disentanglement Multi-view Learning Against View Adversarial Attacks

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