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/asharakeh/kitti_native_evaluation
Joint 3D Proposal Generation and Object Detection from View Aggregation
https://github.com/ashtawy/deeptraffic
https://github.com/asvath/mobile_robotics
https://github.com/ayush111111/cone_keypoint_regression
https://github.com/bbejczy/polarseg-kitti
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
https://github.com/benedictquartey/RollE
https://github.com/bentoml/openllm
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/berndpfrommer/basalt-headers
https://github.com/bitsauce/Carla-ppo
https://github.com/bobbyhaliwela/AutoRcCar
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/bobbyhaliwela/AutoRcCar-Behavioral-Clonning
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/bolianchen/nuscenes_depth
https://github.com/bostondiditeam/MV3D
Multi-View 3D Object Detection Network for Autonomous Driving
https://github.com/bouracha/Gen_Motion
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/bouracha/OoDMotion
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/bradenhurl/DeepGTAV-PreSIL
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradenhurl/GTAVisionExport-DepthExtractor
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradenhurl/PreSIL-tools
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/bradyz/2020_CARLA_challenge
https://github.com/bravegroup/fullysparsefusion
https://github.com/bryonkucharski/Learning-to-Drive-with-Reinforcement-Learning-and-Variational-Autoencoders
https://github.com/bryonkucharski/learning-to-drive-in-a-day-reproduction
https://github.com/brytsknguyen/M-LOAM
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
https://github.com/bsj1216/NNMPC
https://github.com/casper-hansen/autoawq
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/catta202000/CMRNet
CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps
https://github.com/cattaneod/CMRNet
CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps
https://github.com/chengguizi/basalt-headers
https://github.com/chengxuduan/advhmp
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/chenyicai-0611/YOLOX-Flask-deployment
https://github.com/cheonlian/DeepfakeBlock
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
https://github.com/chowkamlee81/CentrePointNet
https://github.com/chwilms/sos
Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
https://github.com/cjy1992/gym-carla
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
https://github.com/cmubig/SPEC
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.