https://github.com/liuzuxin/fsrl
공식 코드Datasets and Benchmarks for Offline Safe Reinforcement Learning
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/liuzuxin/osrl
공식 코드Datasets and Benchmarks for Offline Safe Reinforcement Learning
https://github.com/liw95/lightloc
공식 코드LightLoc: Learning Outdoor LiDAR Localization at Light Speed
https://github.com/liyingyanucas/wote
공식 코드End-to-End Driving with Online Trajectory Evaluation via BEV World Model
https://github.com/liyuhang166/deep_channel_prior
공식 코드https://github.com/lizhuoran0/2025-ieee-network-chirpdelaydopplermodulationisac
공식 코드https://github.com/lkeab/gsnet
공식 코드GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision
https://github.com/lmd0311/awesome-world-model
공식 코드The Role of World Models in Shaping Autonomous Driving: A Comprehensive Survey
https://github.com/lmd0311/hermes
공식 코드HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation
https://github.com/lmomoy/cenet
공식 코드A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
https://github.com/lmomoy/chnet
공식 코드A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
https://github.com/lmy98129/vlpd
공식 코드VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision
https://github.com/longzhong-lin/eda
공식 코드EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
https://github.com/loong8888/WAIR
공식 코드https://github.com/lorebianchi98/fg-clip
공식 코드Is CLIP the main roadblock for fine-grained open-world perception?
https://github.com/louisccc/av_av
공식 코드Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
https://github.com/louisyxlu/usrnet
공식 코드https://github.com/lrp-ivc/stsbench
공식 코드https://github.com/ltp1995/gpvl
공식 코드Generative Planning with 3D-vision Language Pre-training for End-to-End Autonomous Driving
https://github.com/lty2226262/Car_studio
공식 코드Car-Studio: Learning Car Radiance Fields from Single-View and Endless In-the-wild Images
https://github.com/lucaballotta/vrem-fl
공식 코드VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
https://github.com/lucastabelini/PolyLaneNet
공식 코드https://github.com/lucasyang567/metaocc
공식 코드https://github.com/luciferbobo/d4nerf
공식 코드Detachable Novel Views Synthesis of Dynamic Scenes Using Distribution-Driven Neural Radiance Fields
https://github.com/lulor/ood-class-separation
공식 코드Large Class Separation is not what you need for Relational Reasoning-based OOD Detection
https://github.com/luludak/deltann
공식 코드https://github.com/lxtgh/awesome-segmenation-with-transformer
공식 코드https://github.com/lxtgh/awesome-segmentation-with-transformer
공식 코드https://github.com/lynn-yu/bi-mapper
공식 코드Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving
https://github.com/lynn-yu/genmapping
공식 코드https://github.com/lynn-yu/hierdamap
공식 코드HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors
https://github.com/lywang3081/MRDC
공식 코드https://github.com/lzqw/c-hac
공식 코드https://github.com/lzzzzzm/ii-world
공식 코드$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
https://github.com/maciejzieba/HyperFlow
공식 코드https://github.com/macrohuang1993/ESNet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.