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/sisl/ngsim_env

공식 코드

Multi-Agent Imitation Learning for Driving Simulation

https://github.com/sisl/perceptionadaptivestresstesting

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How Do We Fail? Stress Testing Perception in Autonomous Vehicles

https://github.com/sisl/sceneinformer

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Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments

https://github.com/sixcorepeach/mcam

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MCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding

https://github.com/sj-li/minet

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Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform

https://github.com/sj-li/unpnet

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Rethinking 3D LiDAR Point Cloud Segmentation

https://github.com/sjtu-visys/textslam

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TextSLAM: Visual SLAM with Semantic Planar Text Features

https://github.com/skokec/detectron-traffic-signs

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Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

https://github.com/skygoo2000/adm

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ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties

https://github.com/skyhehe123/msf

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MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences

https://github.com/sled-group/dorothie

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DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents

https://github.com/sled-group/driVLMe

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DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experiences

https://github.com/slothfulxtx/TransLoc3D

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TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields

https://github.com/smartroboticslab/mid-fusion

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MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM

https://github.com/smbonilla/gaussianpancakes

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Gaussian Pancakes: Geometrically-Regularized 3D Gaussian Splatting for Realistic Endoscopic Reconstruction

https://github.com/snorkel-team/snorkel-tutorials

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Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices

https://github.com/snt-arg/ecg2o

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ecg2o: A Seamless Extension of g2o for Equality-Constrained Factor Graph Optimization

https://github.com/snu-drl/drcpo

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DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion

https://github.com/sobhan-moosavi/DCRNN

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Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification

https://github.com/soc-ucsd/deep-lcc

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DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

https://github.com/sof-danny/spannotation

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Spannotation: Enhancing Semantic Segmentation for Autonomous Navigation with Efficient Image Annotation

https://github.com/sonebu/vehicular-vlp-simulations

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Vehicular Visible Light Positioning for Collision Avoidance and Platooning: A Survey

https://github.com/songw-zju/HASSC

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Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation

https://github.com/songw-zju/Meta-RangeSeg

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Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation

https://github.com/songw-zju/lidar2map

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LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation

https://github.com/songw-zju/scribble2scene

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Label-efficient Semantic Scene Completion with Scribble Annotations

https://github.com/soniabaee/MEDIRL-EyeCar

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MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

https://github.com/soniabaee/eyeCar

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MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

https://github.com/sotif-avlab/pesotif

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PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarios

https://github.com/sotif-avlab/sind

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SIND: A Drone Dataset at Signalized Intersection in China

https://github.com/soundai2016/survey_acoustic_world_models

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A Survey on World Models Grounded in Acoustic Physical Information

https://github.com/sparkssss/DistroFair

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Distribution-aware Fairness Test Generation

https://github.com/spla-tam/splatam

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SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM

https://github.com/sr42-dev/pedestrian-intention-fuzzy-classifier

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MonoPIC - A Monocular Low-Latency Pedestrian Intention Classification Framework for IoT Edges Using ID3 Modelled Decision Trees

https://github.com/sranc3/m-bev

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M-BEV: Masked BEV Perception for Robust Autonomous Driving

https://github.com/sreeramsa/drivesim

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Probing Multimodal LLMs as World Models for Driving

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