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/tudelft-iv-students/offline-tracking-with-object-permanence

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Offline Tracking with Object Permanence

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

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VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow

https://github.com/tudelft/faith

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FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic 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/tufts-ai-robotics-group/mmvp

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A Framework for Multisensory Foresight for Embodied Agents

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/tum-cps-hn/data-driven-reachability-analysis-for-piecewise-affine-system

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Data-Driven Reachability Analysis for Piecewise Affine Systems

https://github.com/tum-traffic-dataset/coopdet3d

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TUMTraf V2X Cooperative Perception Dataset

https://github.com/tum-traffic-dataset/tum-traffic-dataset-dev-kit

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TUMTraf V2X Cooperative Perception Dataset

https://github.com/tumftm/dpft

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DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection

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/anchor3dlane

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Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection

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/twankim/avod_ssn

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On Single Source Robustness in Deep Fusion Models

https://github.com/tydpan/gpc

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Pre-Training LiDAR-Based 3D Object Detectors Through Colorization

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/uber-research/normative-uncertainty

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Reinforcement Learning Under Moral Uncertainty

https://github.com/uber/MARVIN

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Multi-Agent Routing Value Iteration Network

https://github.com/ucaszyp/monoocc

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MonoOcc: Digging into Monocular Semantic Occupancy Prediction

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

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