https://github.com/pmcgrath249/DMMFODS
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Papers with Code 아카이브
Autonomous Driving
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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/pmcgrath249/DeepCVLab
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/pmcgrath249/DeepCV_Lab
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/pmj110119/yolox
https://github.com/poodarchu/Det3D
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
https://github.com/pranit570/salientobjectsdetection
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/preetam1997/Self-Driven-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/probabilistic-jsma/probabilistic-jsma
https://github.com/programmingLearner/MATF-architecture-details
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
https://github.com/purduedigitaltwin/talk2drive
Personalized Autonomous Driving with Large Language Models: Field Experiments
https://github.com/purewater0901/carCalibration
https://github.com/qiaozhijian/vLPD-Net
A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition
https://github.com/qiyan98/social-nce-stgcnn
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/quan-dao/practical-collab-perception
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
https://github.com/qwenlm/qwen2-vl
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/qy1994-0919/cfpnet
https://github.com/rahul263-stack/Self-Driving-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/ran-weii/cleanil
https://github.com/rasmith/vllm
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/rehamessameltagoury/Urban-Self-Driving-Car
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception
https://github.com/renmengye/rec-attend-public
https://github.com/resuldagdanov/DeFIX
https://github.com/resuldagdanov/self-improving-RL
https://github.com/retis-ai/patchattacktool
https://github.com/robin-chan/decision-rules
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
https://github.com/robin-karlsson0/dslp
Lane Graph Estimation for Scene Understanding in Urban Driving
https://github.com/rohanchandra30/TrackNPred
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/romi514/TLSGAN
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/rsy6318/CorrI2P
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
https://github.com/runjia0124/coconet
https://github.com/sachinsharma9780/Master-Thesis-Free-Space-Detetction-for-Autonomous-Driving-using-Deep-Learning
Enhanced free space detection in multiple lanes based on single CNN with scene identification
https://github.com/safetylab24/FusionCVCP
https://github.com/saitejavn/lgsvl-scenarios
https://github.com/samadiamir/IE2E
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
https://github.com/sancarlim/densetnt-intent
DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
https://github.com/sanjeewk/ppnet
PointPillars: Fast Encoders for Object Detection from Point Clouds
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.