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/abduallahmohamed/FollowMe

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FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings

https://github.com/abduallahmohamed/Social-STGCNN

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Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

https://github.com/abduallahmohamed/skeleton-graph

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Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs

https://github.com/abdulhannankhan/f2dnet

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F2DNet: Fast Focal Detection Network for Pedestrian Detection

https://github.com/abhishekbaghel11/Traffic-light-detection-in-adverse-weather-conditions

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Snowy Scenes,Clear Detections: A Robust Model for Traffic Light Detection in Adverse Weather Conditions

https://github.com/adap/flower

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FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

https://github.com/adept-thu/RoboFusion

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RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAM

https://github.com/adept-thu/dual-radar

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Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving

https://github.com/adlab-autodrive/bevfusion

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BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework

https://github.com/adlab-autodrive/bevheight

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BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection

https://github.com/adrianbojko/consinv-dataset

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Self-Improving SLAM in Dynamic Environments: Learning When to Mask

https://github.com/aerialroboticsgroup/forest-vo

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ForestVO: Enhancing Visual Odometry in Forest Environments through ForestGlue

https://github.com/aervisloc/vpair

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VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments

https://github.com/againerju/tcg_recognition

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Traffic Control Gesture Recognition for Autonomous Vehicles

https://github.com/agao8/STEAD

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STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications

https://github.com/agent-sgs/pillarnet

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PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection

https://github.com/agi-inc/agisdk

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REAL: Benchmarking Autonomous Agents on Deterministic Simulations of Real Websites

https://github.com/agi-inc/real

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REAL: Benchmarking Autonomous Agents on Deterministic Simulations of Real Websites

https://github.com/agiresearch/asb

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Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents

https://github.com/agrimgupta92/sgan

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Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

https://github.com/aguilarjose11/pbrlneurips

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RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning

https://github.com/ahydchh/impromptu-vla

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Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models

https://github.com/ai-it-avs/robustmat

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RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challenging Environments

https://github.com/ai-secure/crop

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CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

https://github.com/ai-secure/safeauto

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SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models

https://github.com/ai4ce/EUVS-Benchmark

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Extrapolated Urban View Synthesis Benchmark

https://github.com/ai4ce/NYC-Event-VPR

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NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments

https://github.com/ai4ce/occ4cast

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LiDAR-based 4D Occupancy Completion and Forecasting

https://github.com/aiasd/fused

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Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems

https://github.com/aicps/hydrafusion

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HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception

https://github.com/aicps/rs2g

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RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding

https://github.com/aicps/sg-collision-prediction

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Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

https://github.com/aierlab/multitask

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Gradient-Guided Parameter Mask for Multi-Scenario Image Restoration Under Adverse Weather

https://github.com/aigeeksgroup/dc-scene

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DC-Scene: Data-Centric Learning for 3D Scene Understanding

https://github.com/aimi-lab/robust-pose-estimator

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Learning How To Robustly Estimate Camera Pose in Endoscopic Videos

https://github.com/aimotive/aimotive-dataset-loader

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aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception

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