Papers with Code 아카이브

Autonomous Driving

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 6
평가표: 4
보존된 평가 기록: 23

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/jsaric/panoptic-swiftnet

공식 코드

Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation

https://github.com/jsford64/jiffy-compression

공식 코드

Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences

https://github.com/juanb09111/finnforest

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FinnWoodlands Dataset

https://github.com/juanb09111/pandepth

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PanDepth: Joint Panoptic Segmentation and Depth Completion

https://github.com/julessanchez/3dlabelprop

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Domain generalization of 3D semantic segmentation in autonomous driving

https://github.com/julessanchez/coarselabel

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COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets

https://github.com/juliangebele/CARLANE

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CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains

https://github.com/julianolagana/mt3v2

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Can Deep Learning be Applied to Model-Based Multi-Object Tracking?

https://github.com/jumponthemoon/allweathernet

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AllWeatherNet:Unified Image Enhancement for Autonomous Driving under Adverse Weather and Lowlight-conditions

https://github.com/jun-cen/open_world_3d_semantic_segmentation

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Open-world Semantic Segmentation for LIDAR Point Clouds

https://github.com/junbao-zhou/track-no-forgetting

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TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation

https://github.com/junda24/afnet

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Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving

https://github.com/juntang-zhuang/ShelfNet

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ShelfNet for Fast Semantic Segmentation

https://github.com/juntang-zhuang/ShelfNet-lw-cityscapes

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ShelfNet for Fast Semantic Segmentation

https://github.com/justusren/exploring-adversarial-attacks-on-neural-networks

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Exploring Adversarial Attacks on Neural Networks: An Explainable Approach

https://github.com/juyebshin/instagram

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InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning

https://github.com/jwfangit/emem-ndt

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Vehicle Behavior Prediction by Episodic-Memory Implanted NDT

https://github.com/jxbbb/adapt

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ADAPT: Action-aware Driving Caption Transformer

https://github.com/jxr326/swinmcnet

공식 코드

Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection

https://github.com/jzuern/autograph-pub

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AutoGraph: Predicting Lane Graphs from Traffic Observations

https://github.com/kaanakan/slamp

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SLAMP: Stochastic Latent Appearance and Motion Prediction

https://github.com/kaavyarekanar/towards-a-performance-analysis-on-pre-trained-vqa-models-for-autonomous-driving

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Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving

https://github.com/kaibrach/Moment-Propagation

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Single Shot MC Dropout Approximation

https://github.com/kaichen1998/multisiam

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MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

https://github.com/kailaisun/indoor-depth-completion

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A Two-Stage Masked Autoencoder Based Network for Indoor Depth Completion

https://github.com/kailasdayanandan/dual_thinking

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Dual Thinking and Logical Processing -- Are Multi-modal Large Language Models Closing the Gap with Human Vision ?

https://github.com/kailliang/Lane-Change-Classification-and-Prediction-with-Action-Recognition-Networks

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Lane Change Classification and Prediction with Action Recognition Networks

https://github.com/kaist-avelab/k-lane

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K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways

https://github.com/kaist-avelab/k-radar

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K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions

https://github.com/kakaxi314/GuideNet

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Learning Guided Convolutional Network for Depth Completion

https://github.com/kamanphoebe/motiondetection

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Optical Flow Based Motion Detection for Autonomous Driving

https://github.com/kami93/CMU-DATF

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Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding

https://github.com/kaustubhsridhar/time-series-ood

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CODiT: Conformal Out-of-Distribution Detection in Time-Series Data

https://github.com/kcyu2014/lidar-camera-robust-benchmark

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Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection

https://github.com/kemo-huang/jmodt

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Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving

https://github.com/kev-in-ta/l2e

공식 코드

L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras

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