https://github.com/deepmind/dm_hamiltonian_dynamics_suite
공식 코드Which priors matter? Benchmarking models for learning latent dynamics
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/deeprobust/DeepRobust
공식 코드Understanding Local Robustness of Deep Neural Networks under Natural Variations
https://github.com/deib-polimi/dillema
공식 코드DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation
https://github.com/denyingmxd/CVCDepth
공식 코드Towards Cross-View-Consistent Self-Supervised Surround Depth Estimation
https://github.com/dependable-ai/nn-dependability-kit
공식 코드nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems
https://github.com/dependable-intelligent-systems-lab/xwhy
공식 코드https://github.com/derrickxunu/cobevt
공식 코드CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers
https://github.com/detectRecog/ZoomNet
공식 코드ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection
https://github.com/devvansh1997/bgvp
공식 코드Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian Dataset
https://github.com/df-boy/t-corresnet
공식 코드https://github.com/dg-cavs/dg-coperception
공식 코드https://github.com/dgist-cvlab/flow4d
공식 코드Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimation
https://github.com/dimonenka/rl_policy_budget
공식 코드Personalized Reinforcement Learning with a Budget of Policies
https://github.com/dinggh0817/4d_radar_mot
공식 코드https://github.com/dingmike001/saliendet-open-detection
공식 코드https://github.com/dingmyu/doubly-robust-self-training
공식 코드https://github.com/dingpx/eai
공식 코드https://github.com/dipkamal/pasa
공식 코드https://github.com/dlr-mi/utrack
공식 코드https://github.com/dmn-sjk/ar-tta
공식 코드AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation
https://github.com/dmu-xmu/dsr
공식 코드https://github.com/dmu-xmu/sar
공식 코드Sequential Action-Induced Invariant Representation for Reinforcement Learning
https://github.com/dmytrozabolotnii/autoware_mini
공식 코드Pedestrian motion prediction evaluation for urban autonomous driving
https://github.com/donghao51/multiood
공식 코드MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities
https://github.com/dotchen/LAV
공식 코드https://github.com/dotchen/LearningByCheating
공식 코드https://github.com/dotchen/WorldOnRails
공식 코드https://github.com/dreossi/analyzeNN
공식 코드https://github.com/drl-casia/autonomous-driving
공식 코드Lane Change Decision-Making through Deep Reinforcement Learning
https://github.com/drosemei/rome
공식 코드RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation
https://github.com/dsbrown1331/vav-icml
공식 코드https://github.com/dtc111111/sn-lidar
공식 코드SN-LiDAR: Semantic Neural Fields for Novel Space-time View LiDAR Synthesis
https://github.com/duke-vision/semantic-unsup-flow-release
공식 코드SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving
https://github.com/dulucas/simlpe
공식 코드https://github.com/duolu/CAROM
공식 코드CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos
https://github.com/dvij542/offtersim
공식 코드WROOM: An Autonomous Driving Approach for Off-Road Navigation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.