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/fidler-lab/social-driving
https://github.com/filippogiruzzi/semantic_segmentation
https://github.com/fmahoudeau/ShelfNet-Human-Pose-Estimation
https://github.com/fpthink/stnet
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
https://github.com/fpthink/v2b
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
https://github.com/franpin/topologic
TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes
https://github.com/fudan-zvg/roadnetworktransformer
https://github.com/fundamentalvision/BEVFormer
https://github.com/garrickbrazil/SDS-RCNN
Illuminating Pedestrians via Simultaneous Detection & Segmentation
https://github.com/gdaosu/awesome-pointcloudregistration
https://github.com/gengshan-y/high-res-stereo
https://github.com/geodekid/frnn
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
https://github.com/georghess/neurad
SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
https://github.com/ghimiredhikura/Complex-YOLO-V3
https://github.com/ghimiredhikura/Complex-YOLOv3
https://github.com/gloria111/YOLOX_paddle
https://github.com/gogojjh/M-LOAM
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
https://github.com/google-research/deeplab2
https://github.com/google-research/human-scene-transformer
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/govvijaycal/confidence_aware_predictions
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
https://github.com/gowriaddepalli/ML_DL_Research_collab_base
Deep Reinforcement Learning framework for Autonomous Driving
https://github.com/gymoon10/Instance-Segmentation-with-SpatialEmbedding-CA
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
https://github.com/haiyang-w/dsvt
UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation
https://github.com/haiyang-w/git
UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation
https://github.com/halajun/vdo_slam
https://github.com/halimasoulb/human-pose-estimation-3d-lidar
https://github.com/hamidmousavi0/reliable-relu-toolbox
https://github.com/haosulab/ManiSkill-Learn
https://github.com/hasanirtiza/PedesFormer-SwinTransformer-For-Pedestrian-Detection
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
https://github.com/hbzhang/AwesomeSelfDriving
Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving
https://github.com/hejiawei2023/ug3dmot
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
https://github.com/henry1iu/tnt-trajectory-prediction
https://github.com/henry1iu/tnt-trajectory-predition
https://github.com/heshameraqi/Dynamic-CIL-Driving
Dynamic Conditional Imitation Learning for Autonomous Driving
https://github.com/hisfog/SfMNeXt-Impl
SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.