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/ZikangZhou/HiVT
https://github.com/ZiyiLiubird/GRIP_Plus_Plus
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
https://github.com/aaronfriedman6/MV3D_VoxelNet
Multi-View 3D Object Detection Network for Autonomous Driving
https://github.com/abhi1kumar/M3D-RPN
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/abhigoku10/CenterPoint_PC
https://github.com/abhileshborode/Behavorial-Clonng-Self-driving-cars
Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues
https://github.com/adverml/advopenood
https://github.com/ahmadelsallab/MultiCheXNet
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/ahmadelsallab/lidargan
LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving
https://github.com/aidriver/ChauffeurNet
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
https://github.com/aimotive/mm_training
aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception
https://github.com/airalcorn2/paved2paradise
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/ajoshi944/Segmentation-severstal-steel
Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic Segmentation
https://github.com/aksoyeren/salsanet
SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving
https://github.com/aldipiroli/semantic_spray_dataset
SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions
https://github.com/alexdominguez09/pedestrian_direction
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/aliansgp/RL-SAC-CARLA
Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning
https://github.com/alibaba/EasyCV
https://github.com/alin256/multi-mode-prediction-with-mtp-loss
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/aljosaosep/kitti-track-collection
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
https://github.com/alvinwan/shiftresnet-cifar
https://github.com/amansinha/neural-bridge
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
https://github.com/amiryanj/socialways
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/anastasiawd/brightvo
https://github.com/ancorso/InterpretableValidation.jl
https://github.com/ankur-rc/autodrive_ddpg
https://github.com/anonymousjack/hijacking
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking
https://github.com/antoinedemathelin/openood
https://github.com/apolloauto/apollo-model-yolox
https://github.com/apostovan21/vnncomp2023
Architecturing Binarized Neural Networks for Traffic Sign Recognition
https://github.com/araffin/learning-to-drive-in-5-minutes
https://github.com/arashjavan/deeplio
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
https://github.com/argoverse/argoverse-api
https://github.com/arlo0o/uniscene-unified-occupancy-centric-driving-scene-generation
UniScene: Unified Occupancy-centric Driving Scene Generation
https://github.com/as791/ZOO_Attack_PyTorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.