https://github.com/uddd821/hv-bev
공식 코드HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object Detection
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/uiuc-arc/cgt
공식 코드https://github.com/unibwtas/collisionpro
공식 코드Collision Probability Distribution Estimation via Temporal Difference Learning
https://github.com/unstable-zeros/learning-rocbfs
공식 코드Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations
https://github.com/uoe-agents/cema
공식 코드Causal Explanations for Sequential Decision-Making in Multi-Agent Systems
https://github.com/uoe-agents/ogrit
공식 코드Verifiable Goal Recognition for Autonomous Driving with Occlusions
https://github.com/usaywook/gin
공식 코드GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving
https://github.com/usc-gvl/agent-driver
공식 코드https://github.com/ut21/wsad-driving
공식 코드What Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised Horizon
https://github.com/utiasASRL/mm_masking
공식 코드Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights
https://github.com/uzh-rpg/DSEC
공식 코드https://github.com/valeoai/ConfidNet
공식 코드https://github.com/valeoai/LaRa
공식 코드LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation
https://github.com/valeoai/LearningByCheating
공식 코드End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances
https://github.com/valeoai/MFEval
공식 코드Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?
https://github.com/valeoai/MVRSS
공식 코드https://github.com/valeoai/VideoActionModel
공식 코드VaViM and VaVAM: Autonomous Driving through Video Generative Modeling
https://github.com/valeoai/WoodScape
공식 코드WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
https://github.com/valeoai/WoodScape/tree/master/omnidet
공식 코드OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving
https://github.com/valeoai/also
공식 코드ALSO: Automotive Lidar Self-supervision by Occupancy estimation
https://github.com/valeoai/cab
공식 코드https://github.com/valeoai/carrada_dataset
공식 코드CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
https://github.com/valeoai/mf_awta
공식 코드https://github.com/valeoai/octet
공식 코드https://github.com/valeoai/rangevit
공식 코드RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
https://github.com/valeoai/regents
공식 코드ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable
https://github.com/valeoai/scalr
공식 코드Three Pillars improving Vision Foundation Model Distillation for Lidar
https://github.com/valeoai/slidr
공식 코드Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
https://github.com/valeoai/v-max
공식 코드V-Max: A Reinforcement Learning Framework for Autonomous Driving
https://github.com/valeoai/valeo4cast
공식 코드https://github.com/valeoai/waffleiron
공식 코드Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation
https://github.com/valeoai/xmuda
공식 코드xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
https://github.com/valeoai/xmuda_journal
공식 코드Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation
https://github.com/vdigpku/bev-mae
공식 코드https://github.com/vdigpku/drivinggaussian
공식 코드DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes
https://github.com/vdigpku/henet
공식 코드HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.