https://github.com/clarahoffmann/commaai
공식 코드Marginally calibrated response distributions for end-to-end learning in autonomous driving
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/claud1234/clft
공식 코드CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in Autonomous Driving
https://github.com/claud1234/fcn_transformer_object_segmentation
공식 코드CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in Autonomous Driving
https://github.com/clemgris/igdrivsim
공식 코드IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving
https://github.com/climbingdaily/hsc4d
공식 코드https://github.com/cloud0723/offline-mlirl
공식 코드https://github.com/cloudcjf/ccl
공식 코드CCL: Continual Contrastive Learning for LiDAR Place Recognition
https://github.com/cmlab-korea/awesome-3d-low-level-vision
공식 코드R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
https://github.com/cmubig/seal
공식 코드https://github.com/cocoboom/fsd-bev
공식 코드FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object Detection
https://github.com/cognitive-robots/rai-leaderboard
공식 코드S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving
https://github.com/cognitive-robots/scm-aes-paper-resources
공식 코드https://github.com/cognitive-robots/temporal-cd-evaluation-paper-resources
공식 코드Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
https://github.com/coirl/coirl_code
공식 코드https://github.com/collaborativeperception/v2xverse
공식 코드Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System
https://github.com/commonroad/crgeo
공식 코드https://github.com/comp-hci-lab/conversationalXAV
공식 코드https://github.com/concept-fusion/concept-fusion
공식 코드https://github.com/continental/empirical-bayes-analysis-of-object-trajectory
공식 코드An Empirical Bayes Analysis of Object Trajectory Representation Models
https://github.com/continental/everything-polynomial
공식 코드https://github.com/continental/seed4d
공식 코드SEED4D: A Synthetic Ego--Exo Dynamic 4D Data Generator, Driving Dataset and Benchmark
https://github.com/continental/uncertainty-detection-autolabeling
공식 코드Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection
https://github.com/coperception/robosac
공식 코드Among Us: Adversarially Robust Collaborative Perception by Consensus
https://github.com/core-robotics-lab/icct
공식 코드Learning Interpretable, High-Performing Policies for Autonomous Driving
https://github.com/core9724/awesome-lane-detection
공식 코드https://github.com/cps-il/verifiable-od
공식 코드https://github.com/cram3r95/mapfe4mp
공식 코드Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction
https://github.com/cristianocapone/biodreaming
공식 코드Towards biologically plausible Dreaming and Planning in recurrent spiking networks
https://github.com/csjfwang/forecast-peft
공식 코드Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models
https://github.com/cskkxjk/vampire
공식 코드Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving
https://github.com/ctu-vras/t-concord3d
공식 코드Teachers in concordance for pseudo-labeling of 3D sequential data
https://github.com/cuge1995/u6da
공식 코드Adversarial samples for deep monocular 6D object pose estimation
https://github.com/cuiyx1720/awesome-cot4ad
공식 코드Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
https://github.com/cure-lab/contranet
공식 코드https://github.com/cvlab-unibo/d4-dbst
공식 코드https://github.com/cvlab-unibo/mm2d3d
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.