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Autonomous Driving
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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>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Aligned Delta-Triplane Transformers as Occupancy World Models
GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving
AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework
World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving
LaViPlan : Language-Guided Visual Path Planning with RLVR
Channel-wise Motion Features for Efficient Motion Segmentation
Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models
Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers
Safeguarding Federated Learning-based Road Condition Classification
A Survey on Interpretability in Visual Recognition
Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving
LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learning
Scene-Aware Conversational ADAS with Generative AI for Real-Time Driver Assistance
$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
Review of Feed-forward 3D Reconstruction: From DUSt3R to VGGT
Objectomaly: Objectness-Aware Refinement for OoD Segmentation with Structural Consistency and Boundary Precision
ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture
3DGS_LSR:Large_Scale Relocation for Autonomous Driving Based on 3D Gaussian Splatting
LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
Towards Solar Altitude Guided Scene Illumination
TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems
MCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
FMOcc: TPV-Driven Flow Matching for 3D Occupancy Prediction with Selective State Space Model
LLM-based Realistic Safety-Critical Driving Video Generation
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation
Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence
Out-of-distribution detection in 3D applications: a review
World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model
A Survey on Vision-Language-Action Models for Autonomous Driving
Epona: Autoregressive Diffusion World Model for Autonomous Driving
Where, What, Why: Towards Explainable Driver Attention Prediction
Point Cloud Compression and Objective Quality Assessment: A Survey
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.