https://github.com/aimotive/aimotive-dataset-loader
공식 코드aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception
Papers with Code 아카이브
Autonomous Driving
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- 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/aimotive/aimotive_dataset
공식 코드aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception
https://github.com/aimotive/aimotive_tl_ts_dataset
공식 코드Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving
https://github.com/aioz-ai/fadnet
공식 코드https://github.com/air-discover/driving-thinking-dataset
공식 코드https://github.com/air-discover/llm-augmented-mtr
공식 코드Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving
https://github.com/air-discover/lode
공식 코드LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR
https://github.com/air-discover/multi-alignment-drivng-agent
공식 코드https://github.com/air-thu/dair-rcooper
공식 코드RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception
https://github.com/air-thu/dair-v2x
공식 코드DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
https://github.com/air-thu/dair-v2x-seq
공식 코드https://github.com/air-thu/univ2x
공식 코드https://github.com/air-thu/v2x-graph
공식 코드Learning Cooperative Trajectory Representations for Motion Forecasting
https://github.com/airl-iisc/MRFP
공식 코드https://github.com/airl-iisc/indraeye
공식 코드https://github.com/airotau/pointpillarshailoinnoviz
공식 코드Real-Time 3D Object Detection Using InnovizOne LiDAR and Low-Power Hailo-8 AI Accelerator
https://github.com/aitor-martinez-seras/snn-automotive-object-detection
공식 코드https://github.com/aizazsharif/benchmarking-qrs-2022
공식 코드https://github.com/ajaychawda58/id_cnn
공식 코드https://github.com/ajhamdi/SADA
공식 코드SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications
https://github.com/ajhamdi/semantic-robustness
공식 코드Towards Analyzing Semantic Robustness of Deep Neural Networks
https://github.com/akanuasiegbu/bipoco
공식 코드BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection
https://github.com/aklein1995/drise_dmfpp_ddeletion
공식 코드https://github.com/akshaygopalkr/em-vlm4ad
공식 코드https://github.com/akshaymehra24/riskaversedg
공식 코드On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
https://github.com/akumar2709/fides_asiaccs
공식 코드A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference
https://github.com/alafage/hlt-ens
공식 코드Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting
https://github.com/alanwxz/nuc-net
공식 코드NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation
https://github.com/albermax/interpretable_ai_book__sw_chapter
공식 코드https://github.com/albertopolito/CarSNN
공식 코드https://github.com/alexanderswerdlow/BEVGen
공식 코드https://github.com/alextimans/conformal-od
공식 코드Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction
https://github.com/alfredgu001324/MapBEVPrediction
공식 코드Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention
https://github.com/alfredgu001324/mapuncertaintyprediction
공식 코드Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
https://github.com/alibaba/easyrobust
공식 코드COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts
https://github.com/allenchen1998/rad
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.