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/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

https://github.com/clarahoffmann/commaai

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Marginally calibrated response distributions for end-to-end learning in autonomous driving

https://github.com/claud1234/clft

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CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in Autonomous Driving

https://github.com/claud1234/fcn_transformer_object_segmentation

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CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in Autonomous Driving

https://github.com/cleanlab/cleanlab

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Estimating label quality and errors in semantic segmentation data via any model

https://github.com/clemgris/igdrivsim

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IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving

https://github.com/climbingdaily/hsc4d

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HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR

https://github.com/cloud0723/offline-mlirl

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When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

https://github.com/cloudcjf/ccl

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CCL: Continual Contrastive Learning for LiDAR Place Recognition

https://github.com/cmlab-korea/awesome-3d-low-level-vision

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R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision

https://github.com/cmubig/seal

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SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation

https://github.com/cocoboom/fsd-bev

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FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object Detection

https://github.com/cocoon2wong/Vertical

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View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums

https://github.com/code-Assasin/A_Little_fog_for_a_Large_Turn

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A Little Fog for a Large Turn

https://github.com/coexistence-project/routerl

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RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles

https://github.com/cognitive-robots/causal-natural-language-explanations

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Textual Explanations for Automated Commentary Driving

https://github.com/cognitive-robots/gce_vbai_lrp_paper_resources

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Generating Causal Explanations of Vehicular Agent Behavioural Interactions with Learnt Reward Profiles

https://github.com/cognitive-robots/rai-leaderboard

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S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving

https://github.com/cognitive-robots/scm-aes-paper-resources

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Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between Agents

https://github.com/cognitive-robots/temporal-cd-evaluation-paper-resources

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Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour

https://github.com/cogsys-tuebingen/CBMOT

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Score refinement for confidence-based 3D multi-object tracking

https://github.com/coirl/coirl_code

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Inverse Reinforcement Learning in Contextual MDPs

https://github.com/collaborativeperception/v2xverse

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Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System

https://github.com/columbia-icsl/tdbench

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TDBench: Benchmarking Vision-Language Models in Understanding Top-Down Images

https://github.com/comedialkb/learning_with_passive_optical_nonlinear_mapping

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Nonlinear optical encoding enabled by recurrent linear scattering

https://github.com/commonroad/crgeo

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Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric

https://github.com/comp-hci-lab/conversationalXAV

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What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions

https://github.com/concept-fusion/concept-fusion

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ConceptFusion: Open-set Multimodal 3D Mapping

https://github.com/continental/empirical-bayes-analysis-of-object-trajectory

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An Empirical Bayes Analysis of Object Trajectory Representation Models

https://github.com/continental/ep-diffuser

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EP-Diffuser: An Efficient Diffusion Model for Traffic Scene Generation and Prediction via Polynomial Representations

https://github.com/continental/everything-polynomial

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Improving Out-of-Distribution Generalization of Trajectory Prediction for Autonomous Driving via Polynomial Representations

https://github.com/continental/seed4d

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SEED4D: A Synthetic Ego--Exo Dynamic 4D Data Generator, Driving Dataset and Benchmark

https://github.com/continental/uncertainty-detection-autolabeling

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Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection

https://github.com/coolsunxu/sophie

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SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints

https://github.com/coperception/robosac

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Among Us: Adversarially Robust Collaborative Perception by Consensus

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