https://github.com/kevin-thu/epona
공식 코드Epona: Autoregressive Diffusion World Model for Autonomous Driving
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/kevin2431/ectlo
공식 코드ECTLO: Effective Continuous-time Odometry Using Range Image for LiDAR with Small FoV
https://github.com/khaclinh/fisheyepp4av
공식 코드FisheyePP4AV: A privacy-preserving method for autonomous vehicles on fisheye camera images
https://github.com/kieran514/dyna-dm
공식 코드Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth Maps
https://github.com/kingwmk/ganet
공식 코드https://github.com/kit-mrt/future-motion
공식 코드JointMotion: Joint Self-Supervision for Joint Motion Prediction
https://github.com/kli58/ROIAL
공식 코드https://github.com/kosieram21/trajflow
공식 코드TrajFlow: A Generative Framework for Occupancy Density Estimation Using Normalizing Flows
https://github.com/koyeongmin/PINet
공식 코드Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/koyeongmin/PINet_new
공식 코드Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/krabs-h/ts-cgnet
공식 코드TS-CGNet: Temporal-Spatial Fusion Meets Centerline-Guided Diffusion for BEV Mapping
https://github.com/kshitij3112/evlpsnet
공식 코드https://github.com/kth-rpl/deflow
공식 코드https://github.com/kth-rpl/detect_vlm
공식 코드Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models
https://github.com/kth-rpl/drivecmd_llm
공식 코드Human-Centric Autonomous Systems With LLMs for User Command Reasoning
https://github.com/kth-rpl/rmp
공식 코드https://github.com/kth-rpl/seflow
공식 코드SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
https://github.com/kth-rpl/ssf
공식 코드https://github.com/kuanchihhuang/monodtr
공식 코드MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer
https://github.com/kujason/avod
공식 코드Joint 3D Proposal Generation and Object Detection from View Aggregation
https://github.com/kulikovv/DeepColoring
공식 코드https://github.com/kumuji/ugains
공식 코드https://github.com/kwwcv/selfmotion
공식 코드https://github.com/kwwcv/ssmp
공식 코드Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
https://github.com/kyk120/pointfix
공식 코드PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation
https://github.com/kztakemoto/mmllm
공식 코드https://github.com/l-scofano/stag
공식 코드https://github.com/l1997i/DurLAR
공식 코드https://github.com/l1997i/lim3d
공식 코드Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation
https://github.com/l1997i/rapid_seg
공식 코드RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
https://github.com/lab176344/TrafficScenarios-RFAPsClustering
공식 코드https://github.com/lab176344/Traffic_Sceanrios-VoteBasedEVT
공식 코드https://github.com/lab206/echotrack
공식 코드EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving
https://github.com/labsaint/tsi-gan
공식 코드https://github.com/labshuhanggu/perldiff
공식 코드PerLDiff: Controllable Street View Synthesis Using Perspective-Layout Diffusion Models
https://github.com/lakpa-tamang9/margin_ood
공식 코드Margin-bounded Confidence Scores for Out-of-Distribution Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.