Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/code-implementation1/Code9/tree/main/YOLOX
https://github.com/code-implementation1/Code9/tree/main/YOLOv3
https://github.com/codearxiv/continuous-K-SVD
Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
https://github.com/coinse/sadl
Guiding Deep Learning System Testing using Surprise Adequacy
https://github.com/coperception/coperception
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
https://github.com/csqiangwen/Video_Waterdrop_Removal_in_Driving_Scenes
Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenes
https://github.com/cuifc1997/nuscene-study
https://github.com/cvg/Hierarchical-Localization
From Coarse to Fine: Robust Hierarchical Localization at Large Scale
https://github.com/cxmomo/Neighbor-Vote
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting
https://github.com/d-eremeev/SC-GAN
https://github.com/dC4rlos/TFM
https://github.com/dablack2020/Seniordes
https://github.com/dadung/G2D
https://github.com/daeheepark/PathPredictNusc
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/daeheepark/t4p
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
https://github.com/darpa-l2m/l2metrics
https://github.com/davyneven/fastSceneUnderstanding
https://github.com/deepcs233/visual-cot
LMDrive: Closed-Loop End-to-End Driving with Large Language Models
https://github.com/deepsense-ai/carla-birdeye-view
Simulation-based reinforcement learning for real-world autonomous driving
https://github.com/deepsense-ai/carla-real-traffic-scenarios
https://github.com/demul/image_segmentation_project
A Review on Deep Learning Techniques Applied to Semantic Segmentation
https://github.com/divyakraman/Domain-Adaptive-Knowledge-Distillation-for-Driving-Scene-Semantic-Segmentation
Domain Adaptive Knowledge Distillation for Driving Scene Semantic Segmentation
https://github.com/dvlab-research/VoxelNeXt
https://github.com/dvornikita/blitznet
https://github.com/efficient-large-model/vila
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/enot-autodl/lpcv-2023
https://github.com/ensta-u2is/deeplabv3plus-muad-pytorch
https://github.com/erdos-project/pylot
https://github.com/ermolenkodev/keras-salient-object-visualisation
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/eth-ait/spl
https://github.com/ethz-asl/segmatch
SegMap: Segment-based mapping and localization using data-driven descriptors
https://github.com/f1yfisher/drivedreamer2
DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation
https://github.com/facebookresearch/SegmPred
https://github.com/feibryantkit/lf-tracy
OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation
https://github.com/feixue94/pram
PRAM: Place Recognition Anywhere Model for Efficient Visual Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.