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/trailab/uncertaintytrack

공식 코드

UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking

https://github.com/tranleanh/adas_od_enhance

공식 코드

Robustness Enhancement of Object Detection in Advanced Driver Assistance Systems (ADAS)

https://github.com/tree985/treble-counterfactual-vlms

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Treble Counterfactual VLMs: A Causal Approach to Hallucination

https://github.com/tri-ml/haicu

공식 코드

Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty

https://github.com/trungmanhhuynh/GPRAR

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GPRAR: Graph Convolutional Network based Pose Reconstruction and Action Recognition for Human Trajectory Prediction

https://github.com/trustai/robustbev

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A Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye View Detection

https://github.com/tryhiseyyysum/menol

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Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving

https://github.com/tsattler/visuallocalizationbenchmark

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Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis

https://github.com/tsigalko18/transferability-testing-sdcs

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Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems

https://github.com/tsinghua-mars-lab/statetransformer

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Large Trajectory Models are Scalable Motion Predictors and Planners

https://github.com/ttuhamg/driveditfit

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DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous Driving

https://github.com/tudelft-iv-students/offline-tracking-with-object-permanence

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Offline Tracking with Object Permanence

https://github.com/tudelft-iv/voteflow

공식 코드

VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow

https://github.com/tue-mps/refnet

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A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data

https://github.com/tulerfeng/PlanKD

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On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving

https://github.com/tum-autonomousdriving/autonomous-driving-simulator

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GarchingSim: An Autonomous Driving Simulator with Photorealistic Scenes and Minimalist Workflow

https://github.com/tum-avs/fm-for-scenario-generation-analysis

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Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis

https://github.com/tum-avs/from-words-to-collisions

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From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios

https://github.com/tum-avs/occlusionawaremotionplanning

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From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving

https://github.com/tumftm/flexcloud

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FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps

https://github.com/tumftm/miscalibrationdetection

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Cal or No Cal? -- Real-Time Miscalibration Detection of LiDAR and Camera Sensors

https://github.com/tumftm/sim2realdistributionaligneddataset

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Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level

https://github.com/tumftm/snapshot

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Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments

https://github.com/turingmotors/nuscenes-mqa

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NuScenes-MQA: Integrated Evaluation of Captions and QA for Autonomous Driving Datasets using Markup Annotations

https://github.com/tusen-ai/TSTTC

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TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios

https://github.com/tusen-ai/echofusion

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Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion

https://github.com/tusen-ai/sst

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Super Sparse 3D Object Detection

https://github.com/tusimple/LiDAR_RCNN

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LiDAR R-CNN: An Efficient and Universal 3D Object Detector

https://github.com/tusimple/simpledet

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SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition

https://github.com/tusimple/sst

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Embracing Single Stride 3D Object Detector with Sparse Transformer

https://github.com/typistchen/spikmamba

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SpikMamba: When SNN meets Mamba in Event-based Human Action Recognition

https://github.com/uark-cviu/fredom

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FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding

https://github.com/uber-research/LaneGCN

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Learning Lane Graph Representations for Motion Forecasting

https://github.com/ucd-dare/cardreamer

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CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving

https://github.com/ucla-mobility/AutoVLA

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AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning

https://github.com/ucla-mobility/v2v4real

공식 코드

V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception

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