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Keywords = autonomous road vehicles (ARV)

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19 pages, 490 KiB  
Article
The Safety Risks of AI-Driven Solutions in Autonomous Road Vehicles
by Farshad Mirzarazi, Sebelan Danishvar and Alireza Mousavi
World Electr. Veh. J. 2024, 15(10), 438; https://doi.org/10.3390/wevj15100438 - 26 Sep 2024
Cited by 5 | Viewed by 6683
Abstract
At present Deep Neural Networks (DNN) have a dominant role in the AI-driven Autonomous driving approaches. This paper focuses on the potential safety risks of deploying DNN classifiers in Advanced Driver Assistance System (ADAS) systems. In our experience, many theoretically sound AI-driven solutions [...] Read more.
At present Deep Neural Networks (DNN) have a dominant role in the AI-driven Autonomous driving approaches. This paper focuses on the potential safety risks of deploying DNN classifiers in Advanced Driver Assistance System (ADAS) systems. In our experience, many theoretically sound AI-driven solutions tested and deployed in ADAS have shown serious safety flaws in practice. A brief review of practice and theory of automotive safety standards and related body of knowledge is presented. It is followed by a comparative analysis between DNN classifiers and safety standards developed in the automotive industry. The output of the study provides advice and recommendations for filling the current gaps within the complex and interrelated factors pertaining to the safety of Autonomous Road Vehicles (ARV). This study may assist ARV’s safety, system, and technology providers during the design, development, and implementation life cycle. The contribution of this work is to highlight and link the learning rules enforced by risk factors when DNN classifiers are expected to provide a near real-time safer Vehicle Navigation Solution (VNS). Full article
(This article belongs to the Special Issue Design Theory, Method and Control of Intelligent and Safe Vehicles)
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17 pages, 6144 KiB  
Article
Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles
by Man-Wen Tian, Shu-Rong Yan, Ardashir Mohammadzadeh, Jafar Tavoosi, Saleh Mobayen, Rabia Safdar, Wudhichai Assawinchaichote, Mai The Vu and Anton Zhilenkov
Mathematics 2021, 9(21), 2742; https://doi.org/10.3390/math9212742 - 28 Oct 2021
Cited by 42 | Viewed by 2902
Abstract
Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and [...] Read more.
Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and cornering with variable speed. In this study, a new controller on the basis of interval type-3 (T3) fuzzy logic system (FLSs) is designed. The main novelties and advantages are as follows. (1) The uncertainty is a main challenge in the path-following problem of ARVs. However, in the fuzzy-based approaches, the bounds of uncertainty are assumed to be known. However, in the our suggested approach, the bounds of uncertainties are also fuzzy sets and type-3 FLSs with online adaptation rules are suggested to handle the uncertainties. (2) The approximation errors (AEs) and perturbations are investigated and tackled by the compensators. (3) The bounds of estimation errors are also uncertain and are estimated by the suggested adaptation laws. (4) The stability is ensured under unknown dynamics, perturbations and critical maneuvers. (5) Comparison with the benchmarking techniques and conventional fuzzy approaches verifies that the suggested path-following scheme results in better maneuver performance. Full article
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