Papers with Code 아카이브

Autonomous Vehicles

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

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/aimotive/aimotive_dataset

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aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception

https://github.com/aimotive/aimotive_tl_ts_dataset

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Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving

https://github.com/aioz-ai/fadnet

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Deep Federated Learning for Autonomous Driving

https://github.com/air-discover/driving-thinking-dataset

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Driving Style Alignment for LLM-powered Driver Agent

https://github.com/air-discover/llm-augmented-mtr

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Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving

https://github.com/air-discover/lode

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LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR

https://github.com/air-discover/multi-alignment-drivng-agent

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Driving Style Alignment for LLM-powered Driver Agent

https://github.com/air-thu/dair-rcooper

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RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception

https://github.com/air-thu/dair-v2x

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DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection

https://github.com/air-thu/dair-v2x-seq

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V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting

https://github.com/air-thu/univ2x

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End-to-End Autonomous Driving through V2X Cooperation

https://github.com/air-thu/v2x-graph

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Learning Cooperative Trajectory Representations for Motion Forecasting

https://github.com/airalcorn2/paved2paradise

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Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World

https://github.com/airl-iisc/MRFP

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MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation

https://github.com/airl-iisc/indraeye

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SAGA: Semantic-Aware Gray color Augmentation for Visible-to-Thermal Domain Adaptation across Multi-View Drone and Ground-Based Vision Systems

https://github.com/airlab-polimi/c-slam

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Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping

https://github.com/airotau/pointpillarshailoinnoviz

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Real-Time 3D Object Detection Using InnovizOne LiDAR and Low-Power Hailo-8 AI Accelerator

https://github.com/airou-lab/SynthCRAV

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Synthesizing and Identifying Noise Levels in Autonomous Vehicle Camera Radar Datasets

https://github.com/ais-bonn/lidar_mars_registration

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Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization

https://github.com/aitor-martinez-seras/snn-automotive-object-detection

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Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discovery

https://github.com/aizazsharif/benchmarking-qrs-2022

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Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment

https://github.com/aizazsharif/remav

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ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events

https://github.com/ajaychawda58/id_cnn

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Dimensionality of datasets in object detection networks

https://github.com/ajedgley/rebound

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ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning

https://github.com/ajhamdi/SADA

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SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

https://github.com/ajhamdi/semantic-robustness

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Towards Analyzing Semantic Robustness of Deep Neural Networks

https://github.com/akanuasiegbu/bipoco

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BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection

https://github.com/aklein1995/drise_dmfpp_ddeletion

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On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial Applications

https://github.com/akshaygopalkr/em-vlm4ad

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Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving

https://github.com/akshaymehra24/riskaversedg

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On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization

https://github.com/akumar2709/fides_asiaccs

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A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference

https://github.com/alafage/hlt-ens

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Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting

https://github.com/alanli1997/slim-neck-by-gsconv

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Slim-neck by GSConv: A lightweight-design for real-time detector architectures

https://github.com/alanwxz/nuc-net

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NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentation

https://github.com/albermax/interpretable_ai_book__sw_chapter

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Software and application patterns for explanation methods

https://github.com/albertopolito/CarSNN

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CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.