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/zhangming8/yolox-pytorch
https://github.com/zhejz/trafficbotsv1.5
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/zhengyinan-air/diffusion-planner
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
https://github.com/zhixinlai/llmboostmedical
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
https://github.com/zhulf0804/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/zhyever/PatchFusion
https://github.com/zhyever/patchrefiner
https://github.com/zion-king/Center-based-3D-Object-Detection-and-Tracking
https://github.com/ziyadsheeba/qfat
https://github.com/ziyuan400/video_segmentation
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/zkyntu/UnLanedet
CLRerNet: Improving Confidence of Lane Detection with LaneIoU
https://github.com/zlw9161/carrada-rac
CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
https://github.com/zlw9161/pkc
CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
https://github.com/zoeShao/CSC420-Project
https://github.com/zqzqz/AdvTrajectoryPrediction
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
https://github.com/zwc662/SequentialAttack
https://gitlab.com/VladyslavUsenko/basalt-headers
https://gitlab.com/birder/birder
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.