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/peakpang/ugp

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Unlocking Generalization Power in LiDAR Point Cloud Registration

https://github.com/pedrormconde/uncertainty_calibration_object_detection

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A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection

https://github.com/peidongli/ssr

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Does End-to-End Autonomous Driving Really Need Perception Tasks?

https://github.com/pengtaojiang/tsp6k

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Traffic Scene Parsing through the TSP6K Dataset

https://github.com/pengwangucla/DeLS-3D

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The ApolloScape Open Dataset for Autonomous Driving and its Application

https://github.com/pengxuanyang/uncad

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UncAD: Towards Safe End-to-end Autonomous Driving via Online Map Uncertainty

https://github.com/petrichor625/batraj-behavior-aware-model

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BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

https://github.com/petrichor625/hltp

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A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving

https://github.com/petrichor625/talk2car_cavg

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GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models

https://github.com/pgao-lab/amga

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Towards Adaptive Meta-Gradient Adversarial Examples for Visual Tracking

https://github.com/phflot/tfake

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T-FAKE: Synthesizing Thermal Images for Facial Landmarking

https://github.com/phi-wol/hydra

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Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception

https://github.com/phibenz/double-targeted-uap.pytorch

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Double Targeted Universal Adversarial Perturbations

https://github.com/pholypeng/pnas-mot

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PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search

https://github.com/phyllish/dvdet

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Aerial Monocular 3D Object Detection

https://github.com/pjlab-ADG/neuralsim

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StreetSurf: Extending Multi-view Implicit Surface Reconstruction to Street Views

https://github.com/pjlab-adg/3dtrans

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AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset

https://github.com/pjlab-adg/awesome-knowledge-driven-ad

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Towards Knowledge-driven Autonomous Driving

https://github.com/pjlab-adg/gpt4v-ad-exploration

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On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving

https://github.com/pjlab-adg/leapad

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Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving

https://github.com/pjlab-adg/oasim

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OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving

https://github.com/pjlab-adg/pcsim

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ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation

https://github.com/pjlab-adg/sensorscalibration

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OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving

https://github.com/pkunlp-icler/pca-eval

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PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain

https://github.com/placeforyiming/divide-and-merge-lidar-panoptic-cluster

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A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation

https://github.com/placeforyiming/iccvw21-lidar-panoptic-segmentation-tradicv-survey-of-point-cloud-cluster

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A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation

https://github.com/plai-group/flexible-video-diffusion-modeling

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Flexible Diffusion Modeling of Long Videos

https://github.com/plankson/csirl

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Curricular Subgoals for Inverse Reinforcement Learning

https://github.com/pmclSF/DeepCompress

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DeepCompress: Efficient Point Cloud Geometry Compression

https://github.com/pmj110119/renderocc

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RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision

https://github.com/pnnl/SAMIAm

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SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation

https://github.com/pnnnnnnn/uni-prevpredmap

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Uni-PrevPredMap: Extending PrevPredMap to a Unified Framework of Prior-Informed Modeling for Online Vectorized HD Map Construction

https://github.com/poetrywanderer/CF-NeRF

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Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification

https://github.com/pointscoder/awesome-3d-object-detection-for-autonomous-driving

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3D Object Detection for Autonomous Driving: A Comprehensive Survey

https://github.com/pointscoder/gpt-driver

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GPT-Driver: Learning to Drive with GPT

https://github.com/poodarchu/Class-balanced-Grouping-and-Sampling-for-Point-Cloud-3D-Object-Detection

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Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

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