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/sum1lim/PU-Ray
공식 코드PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surface
https://github.com/sunh18/monosim
공식 코드https://github.com/sunnyhelen/jperceiver
공식 코드JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes
https://github.com/sustech-isus/sustechgan
공식 코드SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous Driving
https://github.com/suwesh/parallel-perception-network
공식 코드Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing
https://github.com/svip-lab/planedepth
공식 코드PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes
https://github.com/swc-17/drivecamsim
공식 코드DriveCamSim: Generalizable Camera Simulation via Explicit Camera Modeling for Autonomous Driving
https://github.com/swc-17/sparsedrive
공식 코드SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
https://github.com/sxjyjay/msmdfusion
공식 코드https://github.com/synsin0/come
공식 코드COME: Adding Scene-Centric Forecasting Control to Occupancy World Model
https://github.com/synsin0/effocc
공식 코드EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy Network
https://github.com/synsin0/srcn3d
공식 코드SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking
https://github.com/synsin0/streamingflow
공식 코드https://github.com/syqlyx/StructVPR
공식 코드https://github.com/syscv/lidar_snow_sim
공식 코드https://github.com/syscv/trafficbots
공식 코드TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
https://github.com/t3as/mad-arl
공식 코드https://github.com/tabguigui/fiptr
공식 코드https://github.com/taco-group/LangCoop
공식 코드https://github.com/taco-group/autotrust
공식 코드AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving
https://github.com/taco-group/drama-x
공식 코드DRAMA-X: A Fine-grained Intent Prediction and Risk Reasoning Benchmark For Driving
https://github.com/taco-group/genai4ad
공식 코드Generative AI for Autonomous Driving: Frontiers and Opportunities
https://github.com/taco-group/openemma
공식 코드OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving
https://github.com/taco-group/stamp
공식 코드STAMP: Scalable Task And Model-agnostic Collaborative Perception
https://github.com/taeckyung/SoTTA
공식 코드https://github.com/taesikgong/note
공식 코드NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
https://github.com/taghikhah/quantod
공식 코드Quantile-based Maximum Likelihood Training for Outlier Detection
https://github.com/tanvirnwu/trifuse
공식 코드LoLI-Street: Benchmarking Low-Light Image Enhancement and Beyond
https://github.com/tarashakhurana/emergent-occ-forecasting
공식 코드Differentiable Raycasting for Self-supervised Occupancy Forecasting
https://github.com/tasl-lab/uniocc
공식 코드UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving
https://github.com/taveraantonio/pixda
공식 코드Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
https://github.com/tb-ad/tb-bench-110k-250k
공식 코드https://github.com/tbenciolini/safe-and-non-conservative-trajectory-planning
공식 코드https://github.com/tchittesh/fovea
공식 코드FOVEA: Foveated Image Magnification for Autonomous Navigation
https://github.com/temporalrobrisk/temporal-robustness-risk
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.