Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/mvaldenegro/keras-uncertainty
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
https://github.com/nanzhaogang/contrib/tree/master/application/multimodal-trajectory-predictions-for-autonomous-driving-using-deep-convolutional-networks
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/nautilusPrime/autodrive_ddpg
https://github.com/neurai-lab/mt-sfmlearner
Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics
https://github.com/newsun-boki/yolovx-openvino-c--video-infer
https://github.com/newsun-boki/yolox-openvino-video-infer
https://github.com/nikkkkhil/self-driving-car-implementaion-based-on-nvidia-paper-using-keras
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/nikosRp/Road-Obstacle-Recognition-Using-Images-And-Machine-Learning
https://github.com/notmariekondo/notmariekondo
https://github.com/nv-tlabs/planning-centric-metrics
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes
https://github.com/nvidia-ai-iot/lidar_ai_solution
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/nvlabs/adaptive-prediction
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
https://github.com/nvlabs/catk
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
https://github.com/nvlabs/parametricbev
https://github.com/nvlabs/vila
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/nvr-avg/adaptive-prediction
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
https://github.com/nvr-avg/trajdata
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
https://github.com/obo/lego
https://github.com/oloocki/scan2lod3
https://github.com/omni-us/squeezedet-keras
https://github.com/open-edge-platform/geti
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmpose
https://github.com/opendrivelab/hdgt
https://github.com/opendrivelab/occnet
https://github.com/opendrivelab/st-p3
ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning
https://github.com/opplieam/Self-Driving-Car
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/optimalscale/detgpt
https://github.com/p-mc-grath/DMMFODS
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/pandamax/Lane-Detection-Based-PINet
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/pascalxia/driver_attention_prediction
https://github.com/paulesta55/PINet
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/pgchui/openpilot_in_carla
Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems
https://github.com/piazzesiNiccolo/myLbc
https://github.com/pistachio0812/YOLOX
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.