RC: 86.91
Papers with Code · CC BY-SA 4.0
Autonomous Vehicles
Autonomous vehicles is the task of making a vehicle that can guide itself 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: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
벤치마크
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Dataset기록된 모델 예시MetricPaperCode
SEPT
MR (K=6): 0.1032minADE (K=1): 1.4412minFDE (K=1): 3.1777MR (K=1): 0.5154
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데이터셋
63개논문
A3DP: 20.21
A3DP: 22.85
Driving Score: 66.32Route Completion: 78.57Infraction penalty: 0.84
Driving Score: 61.846Route Completion: 94.459Infraction penalty: 0.640
Driving Score: 79.95Route Completion: 89.89Infraction penalty: 0.89
Driving Score: 76.18Route Completion: 88.23Infraction penalty: 0.84
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
Driving Score: 75.14Route Completion: 85.63Infraction penalty: 0.87
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.
하위 태스크
AD
Autonomous Driving
LD
Lane Detection
PD
Pedestrian Detection
PA
Pedestrian Attribute Recognition
TS
Traffic Sign Recognition
CL
CARLA Leaderboard 2.0
3C
3D Car Instance Understanding
SL
Simultaneous Localization and Mapping
AN
Autonomous Navigation
DA
Driver Attention Monitoring
FV
Fast Vehicle Detection
LC
Loop Closure Detection