@inproceedings{e7df1216b27b4f17bacbfcc8f1f6de41,
title = "Intelligent Decision-Making in Lane Detection Systems Featuring Dynamic Framework for Autonomous Vehicles",
abstract = "As Advanced Driver-Assistance Systems (ADAS) pave the way for autonomous vehicles, they also improve safety by decreasing the risk of hazardous events. Essential for ADAS, lane detection ensures that vehicles stay on their intended path and navigate safely. Despite their significance, the variability of lane detection algorithms can diminish performance, underscoring the need for robust and reliable solutions. We propose a novel approach to enhance decision-making in autonomous lane detection systems by introducing trust indicator metrics which help determine the usefulness of each algorithm in particular scenarios. Our dynamic framework combines a conventional algorithm with a deep learning model. This complementing combination adapts seamlessly, leveraging each other{\textquoteright}s strengths across operational scenarios. We aim to balance the interpretability of conventional methods with the effectiveness of AI, exploring techniques like trust indicators or lane annotations. We validate the proposed framework using a self-built model vehicle demonstrator, providing insights into the real-time performance of our solution and demonstrating the potential for practical deployment.",
keywords = "ADAS, autonomous driving, hybrid approach, lane detection systems, safety, trust indicator, vehicle demonstrator",
author = "Romana Blazevic and Maa{\ss}, {Fynn Luca} and Omar Veledar and Georg Macher",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 19th Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems, DECSoS 2024, 11th International Workshop on Next Generation of System Assurance Approaches for Critical Systems, SASSUR 2024, Towards A Safer Systems architecture Through Security, TOASTS 2024 and 7th International Workshop on Artificial Intelligence Safety Engineering, WAISE 2024 held in conjunction with the 43rd International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2024 ; Conference date: 17-09-2024 Through 17-09-2024",
year = "2024",
month = sep,
day = "9",
doi = "10.1007/978-3-031-68738-9_2",
language = "English",
isbn = "9783031687372",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "21--33",
editor = "Andrea Ceccarelli and Andrea Bondavalli and Mario Trapp and Erwin Schoitsch and Barbara Gallina and Friedemann Bitsch",
booktitle = "Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops - DECSoS, SASSUR, TOASTS, and WAISE, Proceedings",
address = "Germany",
}