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/awskhalil/oscar

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PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles

https://github.com/ayesha-ishaq/drivelmm-o1

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DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding

https://github.com/ayesha-ishaq/open3dtrack

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Open3DTrack: Towards Open-Vocabulary 3D Multi-Object Tracking

https://github.com/aymanbegh/hybrid-gcnn-cnn

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A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection Inference

https://github.com/b-faye/lightmdetr

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A Lightweight Modular Framework for Low-Cost Open-Vocabulary Object Detection Training

https://github.com/badger-rl/stop

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Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces

https://github.com/baimamboukar/hazardous-asteroid-classification

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Explainable Deep-Learning Based Potentially Hazardous Asteroids Classification Using Graph Neural Networks

https://github.com/baimingc/delay-aware-MARL

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Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments

https://github.com/bambvalley/p2u-slam

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P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty

https://github.com/banafshebamdad/InCrowd-VI

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InCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating SLAM in Indoor Pedestrian-Rich Spaces for Human Navigation

https://github.com/bark-simulator/bark

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Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving

https://github.com/bark-simulator/bark-ml

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Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving

https://github.com/barrybarry-smith/driv3r

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Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving

https://github.com/bassam-motional/Reasonable-Crowd

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The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior

https://github.com/batuceng/diffusion-layer-robustness-pc

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PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification

https://github.com/baumanndominik/ergodic_rl

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Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations

https://github.com/bchimagine/DeepPredictiveMotionTracking

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Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

https://github.com/bcv-uniandes/stride

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STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction

https://github.com/bdd100k/bdd100k

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BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

https://github.com/bdvisl/driveinsight

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Exploring the Causality of End-to-End Autonomous Driving

https://github.com/benatfroemming/REU-2024-Lane-Following

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Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles

https://github.com/benbergner/ips

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Iterative Patch Selection for High-Resolution Image Recognition

https://github.com/bender97/waffleandrange

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Exploiting Local Features and Range Images for Small Data Real-Time Point Cloud Semantic Segmentation

https://github.com/benliu31492/solving-reward-collecting-problems-with-uavs-a-comparison-of-online-optimization-and-q-learning

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Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learning

https://github.com/bernwang/latte

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LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking

https://github.com/bethgelab/imagecorruptions

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Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

https://github.com/bethgelab/mmdetection

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Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

https://github.com/bethgelab/robust-detection-benchmark

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Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

https://github.com/bethgelab/stylize-datasets

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Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

https://github.com/bhyang/diffusion-es

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Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following

https://github.com/bigD233/AMFD

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AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection

https://github.com/bigcileng/bilateral-driving

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Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting

https://github.com/billcai/treepedia_dl_public

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Quantifying Urban Canopy Cover with Deep Convolutional Neural Networks

https://github.com/bipin-saha/bnvd

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Bangladeshi Native Vehicle Detection in Wild

https://github.com/bit-da/rotta

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Robust Test-Time Adaptation in Dynamic Scenarios

https://github.com/bit-mjy/cvtnet

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CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data

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