https://github.com/ucla-mobility/v2v4real
공식 코드V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
Papers with Code 아카이브
Autonomous Vehicles
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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/uddd821/hv-bev
공식 코드HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object Detection
https://github.com/uestc-nnlab/lgu-slam
공식 코드https://github.com/uiuc-arc/cgt
공식 코드https://github.com/ukcheolshin/bridgemultispectraldepth
공식 코드Bridging Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive Learning
https://github.com/uliege-driving/dade
공식 코드Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments
https://github.com/uliege-driving/msc-tta
공식 코드Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments
https://github.com/umautobots/driving-in-the-matrix
공식 코드Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?
https://github.com/umautobots/pedestrian_intent_action_detection
공식 코드Coupling Intent and Action for Pedestrian Crossing Behavior Prediction
https://github.com/umich-bipedlab/optimal_shape_generation
공식 코드https://github.com/umich-bipedlab/optimal_shape_global_pose_estimation
공식 코드https://github.com/unibwtas/collisionpro
공식 코드Collision Probability Distribution Estimation via Temporal Difference Learning
https://github.com/unmannedlab/RELLIS-3D
공식 코드https://github.com/unstable-zeros/learning-rocbfs
공식 코드Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations
https://github.com/uoe-agents/cema
공식 코드Causal Explanations for Sequential Decision-Making in Multi-Agent Systems
https://github.com/uoe-agents/ogrit
공식 코드Verifiable Goal Recognition for Autonomous Driving with Occlusions
https://github.com/urancon/stereospike
공식 코드https://github.com/urbste/MultiCol-SLAM
공식 코드MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
https://github.com/url-kaist/Struct-MDC
공식 코드https://github.com/usail-hkust/CoLLMLight
공식 코드CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control
https://github.com/usail-hkust/llmtscs
공식 코드LLMLight: Large Language Models as Traffic Signal Control Agents
https://github.com/usaywook/gin
공식 코드GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving
https://github.com/usc-gvl/agent-driver
공식 코드https://github.com/usc-sail/mica-subtitle-aligned-movie-sounds
공식 코드https://github.com/usslab/tpatch
공식 코드https://github.com/ut-amrl/coda-devkit
공식 코드Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
https://github.com/ut-amrl/coda-models
공식 코드Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
https://github.com/ut21/wsad-driving
공식 코드What Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised Horizon
https://github.com/utiasASRL/mm_masking
공식 코드Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights
https://github.com/uwsbel/polar-digital
공식 코드POLAR-Sim: Augmenting NASA's POLAR Dataset for Data-Driven Lunar Perception and Rover Simulation
https://github.com/uwsbel/polar-sim
공식 코드POLAR-Sim: Augmenting NASA's POLAR Dataset for Data-Driven Lunar Perception and Rover Simulation
https://github.com/uzh-rpg/DSEC
공식 코드https://github.com/val-iisc/flss
공식 코드Boosting Adversarial Robustness using Feature Level Stochastic Smoothing
https://github.com/valencebond/Rethinking_of_PAR
공식 코드https://github.com/valeoai/ConfidNet
공식 코드https://github.com/valeoai/LaRa
공식 코드LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.