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/catonetwo/collaborative-perception-in-autonomous-driving

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Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

https://github.com/cav-research-lab/safe-reinforcement-learning-using-symbolic-logical-programming-for-autonomous-highway-driving

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Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach

https://github.com/cavalli1234/nefsac

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NeFSAC: Neurally Filtered Minimal Samples

https://github.com/cdb342/alocc

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ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction

https://github.com/cfzd/uniformer

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UniFusion: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird's-Eye-View

https://github.com/cfzd/unifusion

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UniFusion: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird's-Eye-View

https://github.com/cgchrfchscyrh/MixedTrafficControl

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Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning

https://github.com/cgcl-codes/numbod

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NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors

https://github.com/ch-sa/labelCloud

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labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds

https://github.com/charliesong1999/flashdreamer

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Enhancing Monocular 3D Scene Completion with Diffusion Model

https://github.com/chauncygu/safe-reinforcement-learning-baselines

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A Review of Safe Reinforcement Learning: Methods, Theory and Applications

https://github.com/chaytonmin/OFF-Net

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ORFD: A Dataset and Benchmark for Off-Road Freespace Detection

https://github.com/chaytonmin/Off-Road-Freespace-Detection

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ORFD: A Dataset and Benchmark for Off-Road Freespace Detection

https://github.com/chaytonmin/occ-bev

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UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving

https://github.com/chaytonmin/occupancy-mae

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Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

https://github.com/chaytonmin/uniscene

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UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving

https://github.com/chaytonmin/voxel-mae

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Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

https://github.com/chenghuang66/s2tnet

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S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving

https://github.com/chengjt1999/mv-mos

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MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segmentation

https://github.com/chengweialan/desire-gs

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DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes

https://github.com/chengzhag/pansplat

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PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting

https://github.com/chenhongyiyang/widthformer

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WidthFormer: Toward Efficient Transformer-based BEV View Transformation

https://github.com/chensiweithu/whales

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WHALES: A Multi-agent Scheduling Dataset for Enhanced Cooperation in Autonomous Driving

https://github.com/chenxin-dlut/seqtrackv2

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Awesome Multi-modal Object Tracking

https://github.com/chequanghuy/TwinLiteNet

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TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars

https://github.com/chequanghuy/TwinLiteNetPlus

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TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation

https://github.com/chisyliu/GnnPmbTracker

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GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles

https://github.com/chreisinger/ViLGOD

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Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection

https://github.com/christopherbrix/vnncomp2023

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Architecturing Binarized Neural Networks for Traffic Sign Recognition

https://github.com/christopherbrix/vnncomp2023_benchmarks

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Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition

https://github.com/chuduanfeng/charms

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CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous Driving

https://github.com/chuhengwei/ki_gan

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KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections

https://github.com/cidanshi/nitedr-nighttime-image-de-raining

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NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes

https://github.com/cjy1992/detect-loc-map

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End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning

https://github.com/cjy1992/interp-e2e-driving

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Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

https://github.com/ckaestne/seaibib

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Software Engineering for AI-Based Systems: A Survey

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