Autonomous Vehicles
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dachengxiaocheng/NDT-Transformer
공식 코드https://github.com/dadecampo/SafeRLAUV
공식 코드Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
https://github.com/dailyl/sim2real_autonomous_vehicle
공식 코드https://github.com/daniel-bogdoll/ad-datasets
공식 코드Ad-datasets: a meta-collection of data sets for autonomous driving
https://github.com/daniel-bogdoll/anomaly_datasets
공식 코드Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
https://github.com/daniel-bogdoll/deep_generative_models
공식 코드Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models
https://github.com/daniel-bogdoll/model_contradictions
공식 코드Complementary Learning for Real-World Model Failure Detection
https://github.com/daniel-bogdoll/unknown_objects_roads
공식 코드Multimodal Detection of Unknown Objects on Roads for Autonomous Driving
https://github.com/daniel-xsy/bev-attack
공식 코드On the Adversarial Robustness of Camera-based 3D Object Detection
https://github.com/daniellli/lion
공식 코드https://github.com/daniellli/pad
공식 코드https://github.com/danielming123/inversematrixvt3d
공식 코드InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction
https://github.com/danielming123/occfusion
공식 코드OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Prediction
https://github.com/danini/superansac
공식 코드https://github.com/danjos95/dade
공식 코드https://github.com/daodaofr/AlignPS
공식 코드https://github.com/daooshee/HLA-Face-Code
공식 코드HLA-Face: Joint High-Low Adaptation for Low Light Face Detection
https://github.com/darl-libsignal/openti
공식 코드Open-TI: Open Traffic Intelligence with Augmented Language Model
https://github.com/darl-libsignal/promptgat
공식 코드Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
https://github.com/darl-libsignal/syntrac
공식 코드SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras
https://github.com/darl-libsignal/ugat
공식 코드https://github.com/darrenjkt/SEE-VCN
공식 코드Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
https://github.com/dasgringuen/assetto_corsa_gym
공식 코드A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
https://github.com/data-and-decision-lab/defix
공식 코드https://github.com/data-and-decision-lab/self-improving-RL
공식 코드https://github.com/datadrivenwheels/pcd_python
공식 코드https://github.com/datongjie/bevspread
공식 코드https://github.com/datvuthanh/HybridNets
공식 코드https://github.com/davezdeng8/tlfpad
공식 코드https://github.com/davidqiu1993/Hackauton2018_DolanWins
공식 코드Adaptive Performance Assessment For Drivers Through Behavioral Advantage
https://github.com/davin-qi530/dfvo
공식 코드DFVO: Learning Darkness-free Visible and Infrared Image Disentanglement and Fusion All at Once
https://github.com/davyneven/SpatialEmbeddings
공식 코드Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
https://github.com/daweidavidwang/mixedtrafficcontrol
공식 코드https://github.com/dcmlr/groundgrid
공식 코드GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation
https://github.com/dd1github/xai_for_imbalanced_learning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.