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/shidahe/semidense-lines

공식 코드

Incremental 3D Line Segment Extraction from Semi-dense SLAM

https://github.com/shifts-project/shifts

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Shifts 2.0: Extending The Dataset of Real Distributional Shifts

https://github.com/shighghyujie/newpatch-rl

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Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks

https://github.com/shihe98/hcb

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Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense

https://github.com/shilinyan99/panovos

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PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation

https://github.com/shinke-li/pointcvar

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Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification

https://github.com/shivamkumarpanda/agronav

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Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection

https://github.com/showlab/sois

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Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization

https://github.com/shqww/polarrcnn

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Polar R-CNN: End-to-End Lane Detection with Fewer Anchors

https://github.com/shreyasc-13/star

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Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation

https://github.com/shruti-jadon/Semantic-Segmentation-Loss-Functions

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A survey of loss functions for semantic segmentation

https://github.com/shubha07m/On-device-computer-vision-experiments-with-IoT

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MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLO

https://github.com/shubhamagarwal12/warlearn

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WARLearn: Weather-Adaptive Representation Learning

https://github.com/shuhaolii/tits24lanelevel-traffic-benchmark

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Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline

https://github.com/shuhongll/sgs-slam

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SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM

https://github.com/shuishida/langprop

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LangProp: A code optimization framework using Large Language Models applied to driving

https://github.com/shunyo/predicting-optimal-value-functions

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Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning

https://github.com/sibozhang/Depth-Guided-Inpainting

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DVI: Depth Guided Video Inpainting for Autonomous Driving

https://github.com/sijieaaa/hypliloc

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HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion

https://github.com/sijieaaa/prfusion

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PRFusion: Toward Effective and Robust Multi-Modal Place Recognition with Image and Point Cloud Fusion

https://github.com/sijieaaa/robustloc

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RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments

https://github.com/simeonallmendinger/collafuse_imagen

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CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI

https://github.com/simondemoreau/led

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LED: Light Enhanced Depth Estimation at Night

https://github.com/simula-complex/deepqtest

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DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data

https://github.com/simula-complex/epitester

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EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism

https://github.com/sinashish/mensa_mtda

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MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds

https://github.com/sinatayebati/radial_mae

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Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D Sensing

https://github.com/singhay/DeepPredictiveMotionTracking

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Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

https://github.com/sinop97/drive_sim_road_generation

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Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability

https://github.com/sisl/AutomotivePOMDPs.jl

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Decomposition Methods with Deep Corrections for Reinforcement Learning

https://github.com/sisl/AutonomousMerging.jl

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Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

https://github.com/sisl/MultiAgentVariationalOcclusionInference

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Multi-Agent Variational Occlusion Inference Using People as Sensors

https://github.com/sisl/PedestrianAvoidancePOMDP.jl

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Pedestrian Collision Avoidance System for Scenarios with Occlusions

https://github.com/sisl/betazero.jl

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BetaZero: Belief-State Planning for Long-Horizon POMDPs using Learned Approximations

https://github.com/sisl/interpretableselfawareprediction

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Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction

https://github.com/sisl/lopr

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LOPR: Latent Occupancy PRediction using Generative Models

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