https://github.com/poodarchu/efg
공식 코드TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 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/pramodv1993/VAST
공식 코드Visual Probing and Correction of Object Recognition Models with Interactive user feedback
https://github.com/pranav-chib/end-to-end-autonomous-driving
공식 코드Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey
https://github.com/pranav-chib/recent-advancements-in-end-to-end-autonomous-driving-using-deep-learning
공식 코드Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey
https://github.com/praveen-palanisamy/macad-gym
공식 코드Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning
https://github.com/prbonn/3diss
공식 코드Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving
https://github.com/princeton-computational-imaging/DirtyPixels
공식 코드Dirty Pixels: Towards End-to-End Image Processing and Perception
https://github.com/prishita123/curriculum_rl_for_driving
공식 코드https://github.com/prismformore/multi-task-transformer
공식 코드Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth Estimation
https://github.com/priyankkalgaonkar/Image-Classification-with-CondenseNeXt-for-ARM-Based-Computing-Platforms
공식 코드Image Classification with CondenseNeXt for ARM-Based Computing Platforms
https://github.com/probabilistic-jsmas/probabilistic-jsmas
공식 코드https://github.com/proroklab/xaer
공식 코드Explanation-Aware Experience Replay in Rule-Dense Environments
https://github.com/prs-eth/DeFlow
공식 코드https://github.com/psosnin/AbstractGradientTraining
공식 코드Certified Robustness to Data Poisoning in Gradient-Based Training
https://github.com/public-bots/gaussianpretrain
공식 코드https://github.com/public-bots/tigdistill-bev
공식 코드TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation
https://github.com/purduedigitaltwin/lampilot
공식 코드LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs
https://github.com/pxiangwu/MotionNet
공식 코드https://github.com/pyun-ram/FL3D
공식 코드https://github.com/qcraftai/cross-modal-ssl
공식 코드Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
https://github.com/qcraftai/distill-bev
공식 코드DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation
https://github.com/qcraftai/pillar-motion
공식 코드Self-Supervised Pillar Motion Learning for Autonomous Driving
https://github.com/qcraftai/simtrack
공식 코드Exploring Simple 3D Multi-Object Tracking for Autonomous Driving
https://github.com/qcraftai/tip
공식 코드https://github.com/qiantianwen/nuscenes-qa
공식 코드NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario
https://github.com/qiongwu86/joint-optimization-of-aoi-and-energy-consumption-in-nr-v2x-system-based-on-drl
공식 코드https://github.com/qiongwu86/ppo-based-vehicle-control-for-ramp-merging-scheme-assisted-by-enhanced-c-v2x
공식 코드PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X
https://github.com/qiujumin/don-rl
공식 코드Decision-making and control with diffractive optical networks
https://github.com/qizs-bit/gspr
공식 코드GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous Driving
https://github.com/qizs-bit/lrfusionpr
공식 코드LRFusionPR: A Polar BEV-Based LiDAR-Radar Fusion Network for Place Recognition
https://github.com/qualityminds/cistgcn
공식 코드Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting
https://github.com/quan-dao/apro3d-net
공식 코드Attention-based Proposals Refinement for 3D Object Detection
https://github.com/quangle2110/GAN_Mask-RCNN
공식 코드Instance Semantic Segmentation Benefits from Generative Adversarial Networks
https://github.com/qzhb/ubrv2a
공식 코드https://github.com/radar-lab/Lidar_Camera_Automatic_Calibration
공식 코드https://github.com/radar-lab/transrad
공식 코드TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.