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/memberre/asyncdriver
공식 코드Asynchronous Large Language Model Enhanced Planner for Autonomous Driving
https://github.com/menandro/vfs
공식 코드Real-Time Variational Fisheye Stereo without Rectification and Undistortion
https://github.com/mengmengliu1998/LAformer
공식 코드LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints
https://github.com/mengmengliu1998/gatraj
공식 코드GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model
https://github.com/mengyuanchen21/awesome-evidential-deep-learning
공식 코드A Comprehensive Survey on Evidential Deep Learning and Its Applications
https://github.com/metro-smiles/ProgGen
공식 코드https://github.com/mgineer117/meta-cpo
공식 코드https://github.com/mh0797/interplan
공식 코드Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
https://github.com/mi3labucm/coooler
공식 코드https://github.com/mia-cong/swift
공식 코드https://github.com/miaoqiaowei/awesome-4d
공식 코드https://github.com/miaoxiong2320/neighboragg
공식 코드https://github.com/michaelkoelle/qmarl-evo
공식 코드Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
https://github.com/michigan-traffic-lab/DECODE
공식 코드DECODE: Domain-aware Continual Domain Expansion for Motion Prediction
https://github.com/michigan-traffic-lab/lightemma
공식 코드LightEMMA: Lightweight End-to-End Multimodal Model for Autonomous Driving
https://github.com/mickeykang16/ev3dod
공식 코드Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras
https://github.com/mileyan/Pseudo_Lidar_V2
공식 코드Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
https://github.com/mileyan/pseudo-LiDAR_e2e
공식 코드https://github.com/mileyan/pseudo_lidar
공식 코드https://github.com/mingfeicheng/drivetester
공식 코드DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing
https://github.com/mingyuliu1/autonomous_driving_datasets
공식 코드A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
https://github.com/mirkozaff/aa-sgan
공식 코드AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
https://github.com/mit-han-lab/bevfusion
공식 코드BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
https://github.com/mit-han-lab/efficientvit
공식 코드EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/mit-han-lab/llm-awq
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/mit-han-lab/torchsparse
공식 코드https://github.com/mit-spark/perceptionmonitoring
공식 코드https://github.com/mit-wu-lab/mapf-autonomous-driving
공식 코드https://github.com/mitkina/EnvironmentPrediction
공식 코드Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning
https://github.com/mlvccn/pla_sm_videopred
공식 코드Pair-wise Layer Attention with Spatial Masking for Video Prediction
https://github.com/mmocking/riders
공식 코드https://github.com/modanesh/leader
공식 코드LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty
https://github.com/modestyachts/certainty_equiv_perception_control
공식 코드https://github.com/mohamedac29/P2AT
공식 코드P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation
https://github.com/mohamedyd/AutoCure
공식 코드AutoCure: Automated Tabular Data Curation Technique for ML Pipelines
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.