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/rdlin118/hilots

공식 코드

HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving

https://github.com/realcrane/SMART

공식 코드

Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack

https://github.com/refkxh/DUSA

공식 코드

DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception

https://github.com/rerv/vdt

공식 코드

VDT: General-purpose Video Diffusion Transformers via Mask Modeling

https://github.com/retis-ai/carla-gear

공식 코드

CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models

https://github.com/retis-ai/semsegadvpatch

공식 코드

On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving

https://github.com/retsuh-bqw/fmp

공식 코드

Adversarial Feature Map Pruning for Backdoor

https://github.com/revisitq/Pseudo-Stereo-3D

공식 코드

Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving

https://github.com/rheinzler/PointCloudDeNoising

공식 코드

CNN-based Lidar Point Cloud De-Noising in Adverse Weather

https://github.com/richardmarcus/synth-it-like-kitti

공식 코드

Synth It Like KITTI: Synthetic Data Generation for Object Detection in Driving Scenarios

https://github.com/ridgerchu/sad

공식 코드

Autonomous Driving with Spiking Neural Networks

https://github.com/risal-shefin/xsrl

공식 코드

xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of Explainability

https://github.com/rishidarkdevil/daam-i2i

공식 코드

Mapping the Mind of an Instruction-based Image Editing using SMILE

https://github.com/rishikakade/vulnerability-analysis-of-time-synchronization-in-automotive-ethernet

공식 코드

Vulnerability Analysis of Time Synchronization in Automotive Ethernet

https://github.com/risqiutama/ti-slam

공식 코드

Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks

https://github.com/ristea/arim-v2

공식 코드

Estimating the Magnitude and Phase of Automotive Radar Signals under Multiple Interference Sources with Fully Convolutional Networks

https://github.com/rl-ina/lemgorl

공식 코드

Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control

https://github.com/rll-research/sim2seg

공식 코드

Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data

https://github.com/rmsalinas/LDSO

공식 코드

UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers

https://github.com/robertwyq/panoocc

공식 코드

PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation

https://github.com/robin-karlsson0/dslp

공식 코드

Learning to Predict Navigational Patterns from Partial Observations

https://github.com/roboak/nav-q

공식 코드

Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars

https://github.com/robosafe-lab/sim4ad

공식 코드

HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation

https://github.com/robot-learning-freiburg/LCDNet

공식 코드

LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM

https://github.com/robot-learning-freiburg/PASTEL

공식 코드

A Good Foundation is Worth Many Labels: Label-Efficient Panoptic Segmentation

https://github.com/robot-motion/mpb

공식 코드

Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile Robots

https://github.com/robotvisionmu/densemonoslam

공식 코드

A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving

https://github.com/rochelleni/dat

공식 코드

Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation

https://github.com/roeiherz/STAG-Nets

공식 코드

Spatio-Temporal Action Graph Networks

https://github.com/rogerferrod/crodino-kd

공식 코드

Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation

https://github.com/roggirg/AutoBots

공식 코드

Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction

https://github.com/rohban-lab/cobra

공식 코드

Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection

https://github.com/rohollahhs/bad

공식 코드

BAD: Bidirectional Auto-regressive Diffusion for Text-to-Motion Generation

https://github.com/rohtkumar/adversarial_attacks_on_segmentation

공식 코드

Evaluating the Robustness of Off-Road Autonomous Driving Segmentation against Adversarial Attacks: A Dataset-Centric analysis

https://github.com/romainducrocq/dqn-itscwpd

공식 코드

Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection

https://github.com/romainloiseau/Helix4D

공식 코드

Online Segmentation of LiDAR Sequences: Dataset and Algorithm

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