https://bitbucket.org/fafz/bayesian-icp
공식 코드Speeding Up Iterative Closest Point Using Stochastic Gradient Descent
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://bitbucket.org/fafz/stein-icp
공식 코드Stein ICP for Uncertainty Estimation in Point Cloud Matching
https://git.uwaterloo.ca/a9sarkar/traffic_behavior_modeling
공식 코드https://github.com/1989Ryan/Semantic_SLAM
공식 코드Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
https://github.com/2019chengong/offline_rl_poisoner
공식 코드BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
https://github.com/343gltysprk/ovow
공식 코드From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects
https://github.com/3dperceptionlab/toad
공식 코드https://github.com/3lis/rnn_vae
공식 코드On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks
https://github.com/4dvlab/wildrefer
공식 코드https://github.com/7uheng/occood
공식 코드https://github.com/983632847/WebUAV-3M
공식 코드https://github.com/983632847/awesome-multimodal-object-tracking
공식 코드https://github.com/AHupuJR/RFNet
공식 코드https://github.com/AI-secure/Provable-Training-and-Verification-Approaches-Towards-Robust-Neural-Networks
공식 코드https://github.com/AI-secure/VeriGauge
공식 코드https://github.com/AIR-DISCOVER/Driving-Evaluation-Dataset
공식 코드A Comprehensive LLM-powered Framework for Driving Intelligence Evaluation
https://github.com/AIR-DISCOVER/Driving-Intellenge-Evaluation-Framework
공식 코드A Comprehensive LLM-powered Framework for Driving Intelligence Evaluation
https://github.com/AIR-DISCOVER/FreeAD
공식 코드https://github.com/ASU-APG/Targeted-Physical-Adversarial-Attacks-on-AD
공식 코드Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns
https://github.com/AdaCompNUS/GAMMA
공식 코드https://github.com/AdaCompNUS/despot
공식 코드https://github.com/AmirhoseinCh/DynamicPatch
공식 코드https://github.com/AnnnnnieZhang/M3D
공식 코드https://github.com/ApolloAuto/apollo
공식 코드https://github.com/ApolloScapeAuto/dataset-api
공식 코드The ApolloScape Open Dataset for Autonomous Driving and its Application
https://github.com/AugmentedDesignLab/junction-art
공식 코드Procedural Generation of Complex Roundabouts for Autonomous Vehicle Testing
https://github.com/AutoCompSysLab/DCTNet
공식 코드https://github.com/Banconxuan/RTM3D
공식 코드RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
https://github.com/Banconxuan/RTS3D
공식 코드https://github.com/BichenWuUCB/squeezeDet
공식 코드https://github.com/BillChan226/AgentPoison
공식 코드AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases
https://github.com/BillOuyang/3D-OGFlow
공식 코드Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds
https://github.com/BlueCat-de/SDM
공식 코드https://github.com/Bob-cheng/DepthModelHardening
공식 코드Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
https://github.com/Bob-cheng/MDE_Attack
공식 코드Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches
https://github.com/CAIC-AD/YOLOPv2
공식 코드YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.