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/kaist-vclab/sphere-stereo
https://github.com/kakaoenterprise/Learning-Debiased-Disentangled
https://github.com/kalebbennaveed/Trajectory-Planning-for-Autonomous-Vehicles-Using-HRL
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning
https://github.com/kanji95/carla-nav-tool
Ground then Navigate: Language-guided Navigation in Dynamic Scenes
https://github.com/kav-institute/mdn_trajectory_forecasting
https://github.com/kazuki-shin/l5kit
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/kbrodt/waymo-motion-prediction-2021
MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving
https://github.com/kfragkedaki/pedestrian_project
https://github.com/kinglintianxia/KittiSeg
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/kinglintianxia/MultiNet
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/kisna-aryan/YOLOX
https://github.com/kit-mrt/road-barlow-twins
MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving
https://github.com/kjboggess/ijcai23
Toward Policy Explanations for Multi-Agent Reinforcement Learning
https://github.com/klintan/pytorch-lanenet
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
https://github.com/konyul/mmdet3d
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/krishnapalS/Human-Pose-Estimation-using-ShelfNet
https://github.com/krullgit/realsense_3d_detection
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/ksm26/spatiotemporal-predictions
https://github.com/kth-rpl/dynamicmap_benchmark
https://github.com/kujason/ip_basic
Joint 3D Proposal Generation and Object Detection from View Aggregation
https://github.com/kumgleb/Lyft_motion_prediction
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/kun-g/Scraping-Github-trending
https://github.com/lazyn1997/YOLOX
https://github.com/lcckkkhaha/Suprise-Adequacy-Implementation
Guiding Deep Learning System Testing using Surprise Adequacy
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/efficientvit
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/yolox
https://github.com/lexfridman/deeptraffic
https://github.com/lh-wang/ACC
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/lightwheelai/street-gaussians-ns
Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
https://github.com/likui01/LDNet
LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor
https://github.com/linxi159/KittiSeg
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/liuyuan000/yolox_sar
https://github.com/livox-sdk/livox_detection
https://github.com/llmbev/talk2bev
Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving
https://github.com/lly00412/sednet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.