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/arplaboratory/vg_ssl

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VG-SSL: Benchmarking Self-supervised Representation Learning Approaches for Visual Geo-localization

https://github.com/artemsavkin/secogan

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Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation

https://github.com/aryan-at-ul/electronic-imaging-2025-paper-4492

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Image Segmentation: Inducing graph-based learning

https://github.com/aryan-garg/pecnet-pedestrian-trajectory-prediction

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G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System

https://github.com/asatheesh6561/mappo-lce

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A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control

https://github.com/ase2023paper/fmt

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Adversarial Feature Map Pruning for Backdoor

https://github.com/asguard-uci/ld-metric

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Towards Driving-Oriented Metric for Lane Detection Models

https://github.com/asharakeh/pod_compare

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A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving

https://github.com/ashu1069/perceptual-piercing

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Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions

https://github.com/ast-fortiss-tum/misbehaviour-prediction-with-uncertainty-quantification

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Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification

https://github.com/ast-fortiss-tum/perturbation-drive

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Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems

https://github.com/astra-vision/PaSCo

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PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness

https://github.com/astra-vision/SceneRF

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SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields

https://github.com/atapour/temporal-depth-segmentation

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Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach

https://github.com/atonderski/neuro-ncap

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NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving

https://github.com/atr-dbi/cityrefer

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CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data

https://github.com/auto-pilot/obstacle-avoidance

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Autonomous Driving Implementation in an Experimental Environment

https://github.com/autofuzz2020/autofuzz

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Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles

https://github.com/autonomous-viranjan/interactive-motion-planning

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Automated Lane Change via Adaptive Interactive MPC: Human-in-the-Loop Experiments

https://github.com/autonomousdrivingexaminer/mats-gym

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Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving

https://github.com/autonomousvehiclelaboratory/semvecnet

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SemVecNet: Generalizable Vector Map Generation for Arbitrary Sensor Configurations

https://github.com/autonomousvision/CaRL

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CaRL: Learning Scalable Planning Policies with Simple Rewards

https://github.com/autonomousvision/carla_garage

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Hidden Biases of End-to-End Driving Models

https://github.com/autonomousvision/kitti360labeltool

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KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D

https://github.com/autonomousvision/kitti360scripts

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KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D

https://github.com/autonomousvision/navsim

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NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking

https://github.com/autonomousvision/neat

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NEAT: Neural Attention Fields for End-to-End Autonomous Driving

https://github.com/autonomousvision/transfuser

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Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

https://github.com/autovision-cloud/ssl-lanes

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SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving

https://github.com/autumn999999/lusnar-dataset

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LuSNAR:A Lunar Segmentation, Navigation and Reconstruction Dataset based on Muti-sensor for Autonomous Exploration

https://github.com/av-lab/emt-dataset

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EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region

https://github.com/aveeslab/r-tod

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R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving

https://github.com/avikj/l4dc-mpc-ocd

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Optimal Cost Design for Model Predictive Control

https://github.com/avishags422/phantomsponges

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Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors

https://github.com/avmb/dialogllmscenic

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Dialogue-based generation of self-driving simulation scenarios using Large Language Models

https://github.com/awesomericky/complex-env-navigation

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Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation

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