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/bethgelab/stylize-datasets

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Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

https://github.com/bhyang/diffusion-es

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Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following

https://github.com/bigcileng/bilateral-driving

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Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting

https://github.com/bit-da/rotta

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Robust Test-Time Adaptation in Dynamic Scenarios

https://github.com/biter0088/pc-nerf

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PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments

https://github.com/bitfsd/fsd_algorithm

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From perception to control: an autonomous driving system for a formula student driverless car

https://github.com/bjork-crypto/visled-querying

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Language-Driven Active Learning for Diverse Open-Set 3D Object Detection

https://github.com/bob-cheng/badpart

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BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks

https://github.com/bob-cheng/cl-fusionattack

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Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detection

https://github.com/bonbon-rj/instructdriver

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Instruct Large Language Models to Drive like Humans

https://github.com/borisivanovic/planningawareevaluation

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Injecting Planning-Awareness into Prediction and Detection Evaluation

https://github.com/boschresearch/deterministic-graph-deep-state-space-models

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Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems

https://github.com/boschresearch/direg

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Unsupervised Point Cloud Registration with Self-Distillation

https://github.com/boschresearch/issa

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Intra-Source Style Augmentation for Improved Domain Generalization

https://github.com/boschresearch/meta-adversarial-training

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Meta Adversarial Training against Universal Patches

https://github.com/boschresearch/nuscenes_knowledge_graph

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SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphs

https://github.com/bosszhe/emiff

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EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection

https://github.com/bosszhe/vimi

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VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection

https://github.com/boyagesmile/dna-depth

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Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth Estimation

https://github.com/bradyz/task-distillation

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Domain Adaptation Through Task Distillation

https://github.com/bravegroup/drive-wm

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Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving

https://github.com/bravegroup/law

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Enhancing End-to-End Autonomous Driving with Latent World Model

https://github.com/bshfang/self-supervised-motion

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Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals

https://github.com/byzhaoai/bm2cp

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BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities

https://github.com/c0ldstudy/pointsecguard

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On Adversarial Robustness of Point Cloud Semantic Segmentation

https://github.com/caipeide/VTGNet

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VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments

https://github.com/caizhongang/waymo_kitti_converter

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Leveraging Temporal Information for 3D Detection and Domain Adaptation

https://github.com/calvintanama/qd-driver-activity-reco

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Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments

https://github.com/cancaries/SceneCrafter

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Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving

https://github.com/caopulan/Mask2Former-LT

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Frequency-based Matcher for Long-tailed Semantic Segmentation

https://github.com/cap-ntu/baconian-project

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Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning

https://github.com/carlinds/splatad

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SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving

https://github.com/carljohanhoel/BayesianRLForAutonomousDriving

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Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation

https://github.com/carljohanhoel/EnsembleQuantileNetworks

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Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving

https://github.com/carolinewang01/naht

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N-Agent Ad Hoc Teamwork

https://github.com/carranza96/waymo-detection-optimization

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Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance

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