Buchumschlag

Enhanced Drift-Aware Computer Vision Architecture for Autonomous Driving

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Hossain, Md Shahi Amran, Ahammed, Abu Shad, Mukherjee, Sayeri, Obermaisser, Roman
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Computer Vision and Pattern Recognition Logic
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

A Computer Vision Approach for Autonomous Cars to Drive Safe at Construction Zone
von: Ahammed, Abu Shad, et al.
Veröffentlicht: (2024)

Impact Analysis of Data Drift Towards The Development of Safety-Critical Automotive System
von: Hossain, Md Shahi Amran, et al.
Veröffentlicht: (2024)

Integration of Computer Vision with Adaptive Control for Autonomous Driving Using ADORE
von: Ahammed, Abu Shad, et al.
Veröffentlicht: (2025)

Smart Health Software to Support Rescue Personnel in Emergency Situations
von: Ahammed, Abu Shad, et al.
Veröffentlicht: (2024)

Diagnosing Psychiatric Patients: Can Large Language and Machine Learning Models Perform Effectively in Emergency Cases?
von: Ahammed, Abu Shad, et al.
Veröffentlicht: (2025)

Enhancing Vision Language Models with Logic Reasoning for Situational Awareness
von: Pradeep, Pavana, et al.
Veröffentlicht: (2026)

Enhancing Computer Vision with Knowledge: a Rummikub Case Study
von: Vandevelde, Simon, et al.
Veröffentlicht: (2024)

Real-Time Object Detection and Classification using YOLO for Edge FPGAs
von: Amin, Rashed Al, et al.
Veröffentlicht: (2025)

Creating a Formally Verified Neural Network for Autonomous Navigation: An Experience Report
von: Bukhari, Syed Ali Asadullah, et al.
Veröffentlicht: (2024)

Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction
von: Rizvi, Syed Tahir Hussain, et al.
Veröffentlicht: (2025)

Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verification
von: Singh, Vikash, et al.
Veröffentlicht: (2026)

FLARE: Learning Future-Aware Latent Representations from Vision-Language Models for Autonomous Driving
von: Xie, Chengen, et al.
Veröffentlicht: (2026)

VLP: Vision Language Planning for Autonomous Driving
von: Pan, Chenbin, et al.
Veröffentlicht: (2024)

Advancing Autonomous Driving Perception: Analysis of Sensor Fusion and Computer Vision Techniques
von: Bharti, Urvishkumar, et al.
Veröffentlicht: (2024)

DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
von: Tian, Xiaoyu, et al.
Veröffentlicht: (2024)

SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving
von: Li, Peizheng, et al.
Veröffentlicht: (2025)

DriveFuture: Future-Aware Latent World Models for Autonomous Driving
von: Hong, Yufeng, et al.
Veröffentlicht: (2026)

Spatial-aware Vision Language Model for Autonomous Driving
von: Wei, Weijie, et al.
Veröffentlicht: (2025)

DynRsl-VLM: Enhancing Autonomous Driving Perception with Dynamic Resolution Vision-Language Models
von: Zhou, Xirui, et al.
Veröffentlicht: (2025)

A Computer Vision Based Approach for Stalking Detection Using a CNN-LSTM-MLP Hybrid Fusion Model
von: Hasan, Murad, et al.
Veröffentlicht: (2024)

Formal Reasoning About Confidence and Automated Verification of Neural Networks
von: Afzal, Mohammad, et al.
Veröffentlicht: (2025)

LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision
von: Huang, Jiani, et al.
Veröffentlicht: (2023)

Using Formal Models, Safety Shields and Certified Control to Validate AI-Based Train Systems
von: Gruteser, Jan, et al.
Veröffentlicht: (2024)

Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing
von: Elboher, Yizhak, et al.
Veröffentlicht: (2024)

Network Inversion and Its Applications
von: Suhail, Pirzada, et al.
Veröffentlicht: (2024)

Are VLMs Ready for Lane Topology Awareness in Autonomous Driving?
von: Chen, Xin, et al.
Veröffentlicht: (2025)

ROAD-Waymo: Action Awareness at Scale for Autonomous Driving
von: Khan, Salman, et al.
Veröffentlicht: (2024)

Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding
von: Elhenawy, Mohammed, et al.
Veröffentlicht: (2025)

SonoVision: A Computer Vision Approach for Helping Visually Challenged Individuals Locate Objects with the Help of Sound Cues
von: Zishan, Md Abu Obaida, et al.
Veröffentlicht: (2025)

OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
von: Wang, Shihao, et al.
Veröffentlicht: (2025)

DriveRX: A Vision-Language Reasoning Model for Cross-Task Autonomous Driving
von: Diao, Muxi, et al.
Veröffentlicht: (2025)

Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology
von: Zhang, Jingyu, et al.
Veröffentlicht: (2024)

Judge, Then Drive: A Critic-Centric Vision Language Action Framework for Autonomous Driving
von: Yang, Lijin, et al.
Veröffentlicht: (2026)

OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
von: Wang, Shihao, et al.
Veröffentlicht: (2024)

Concept-based Analysis of Neural Networks via Vision-Language Models
von: Mangal, Ravi, et al.
Veröffentlicht: (2024)

An Advanced Deep Learning Based Three-Stream Hybrid Model for Dynamic Hand Gesture Recognition
von: Rahim, Md Abdur, et al.
Veröffentlicht: (2024)

Comparative Analysis of Patch Attack on VLM-Based Autonomous Driving Architectures
von: Fernandez, David, et al.
Veröffentlicht: (2026)

Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving
von: Keser, Mert, et al.
Veröffentlicht: (2025)

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving
von: Zhang, Tianyuan, et al.
Veröffentlicht: (2024)

Foundations and Models in Modern Computer Vision: Key Building Blocks in Landmark Architectures
von: Bourceanu, Radu-Andrei, et al.
Veröffentlicht: (2025)