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/guiliang/cirl-benchmarks-public

공식 코드

Benchmarking Constraint Inference in Inverse Reinforcement Learning

https://github.com/guiliang/icrl-benchmarks-public

공식 코드

Benchmarking Constraint Inference in Inverse Reinforcement Learning

https://github.com/guimindong/gnn4iot

공식 코드

Graph Neural Networks in IoT: A Survey

https://github.com/gujiaqivadin/HomographyLoss

공식 코드

Homography Loss for Monocular 3D Object Detection

https://github.com/guojyjy/cotv

공식 코드

CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning

https://github.com/guotengg/sspnet

공식 코드

SSPNet: Scale and Spatial Priors Guided Generalizable and Interpretable Pedestrian Attribute Recognition

https://github.com/guoyangzhao/fisheyedepth

공식 코드

FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera

https://github.com/guoyangzhao/tsclip

공식 코드

TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign Recognition

https://github.com/gurkirt/3D-RetinaNet

공식 코드

ROAD: The ROad event Awareness Dataset for Autonomous Driving

https://github.com/gurkirt/AMTNet

공식 코드

Two-Stream AMTnet for Action Detection

https://github.com/gurkirt/road-dataset

공식 코드

ROAD: The ROad event Awareness Dataset for Autonomous Driving

https://github.com/gustavokcouto/hgail

공식 코드

Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments

https://github.com/guytenn/terminator

공식 코드

Reinforcement Learning with a Terminator

https://github.com/gyyang23/rdrnet

공식 코드

Reparameterizable Dual-Resolution Network for Real-time Semantic Segmentation

https://github.com/habib-Boloorchi/CIVO-Visual-Odometry-

공식 코드

Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach

https://github.com/hai-chao-zhang/oostraj

공식 코드

OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising

https://github.com/haibao-yu/ffnet-vic3d

공식 코드

Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction

https://github.com/haiyang-w/unitr

공식 코드

UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation

https://github.com/hal-lucination/segfuse

공식 코드

360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network

https://github.com/halajun/multimot_track

공식 코드

Robust Ego and Object 6-DoF Motion Estimation and Tracking

https://github.com/hama-dl-dev/sesame

공식 코드

SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics

https://github.com/hamanhbui/reliable_ssl_baselines

공식 코드

Benchmark for Uncertainty & Robustness in Self-Supervised Learning

https://github.com/hamedhaghighi/cls2r

공식 코드

A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems

https://github.com/hamedhaghighi/coligen

공식 코드

A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems

https://github.com/hamidthri/navbot_ppo

공식 코드

Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation

https://github.com/hanjianghu/pixel-wise-smoothing

공식 코드

Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations

https://github.com/hannahhaensen/dynamon

공식 코드

DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields

https://github.com/hanyangzhong/fenet

공식 코드

FENet: Focusing Enhanced Network for Lane Detection

https://github.com/hanymragab/navinst19

공식 코드

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS

https://github.com/haohao11/MCENET

공식 코드

MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic

https://github.com/haomo-ai/cam4docc

공식 코드

Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications

https://github.com/haomo-ai/motionseg3d

공식 코드

Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation

https://github.com/haomo-ai/superfusion

공식 코드

SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation

https://github.com/haoranzhuexplorer/ad-l-jepa-release

공식 코드

AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR Data

https://github.com/haozhougt/signal_control_paper

공식 코드

Congested Urban Networks Tend to Be Insensitive to Signal Settings: Implications for Learning-Based Control

https://github.com/happinesslz/fbmnet

공식 코드

Multi-Modal 3D Object Detection by Box Matching

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