A Survey of the Inadequacies in Traffic Sign Recognition Systems for Autonomous Vehicles

Magnussen, Angelica F.; Le, Nathan; Hu, Linghuan; Wong, Wernsen

doi:10.23940/ijpe.20.10.p10.15881597

Cited by 19 publications

(13 citation statements)

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“…The challenge of effectively creating an accurate TSR system is complicated since the system must be trained to detect traffic signs that are partially obstructed by objects, in bad conditions resulting in insufficient maintenance, and barely visible in severe weather circumstances. There is a variety of factors that can contribute to anomalies, including the climate, environment, visibility, and location [67].…”

Section: Cyber-physical Attack On Av Perception Systemsmentioning

confidence: 99%

An Integrated Approach of Threat Analysis for Autonomous Vehicles Perception System

et al. 2023

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Automated vehicles are a revolutionary step in mobility, providing a safe and convenient riding experience while keeping the human-driving task minimal to none. Therefore, these intelligent vehicles are equipped with sophisticated perception sensors (e.g., cameras and radars), high-performance computers, artificial intelligence (AI)-driven algorithms, and connectivity with other internet-of-things (IoT) devices. This makes autonomous vehicles (AVs) a special kind of cyber-physical system (CPS) that is moving at speed in highly interactive and dynamic environments (e.g., public roads). Thus, AV is a potential target for cyber attackers to weaponize, compromising safety and mobility on the road. The first step in addressing this problem is to have a robust threat modeling framework that can address the evolving cyber-physical threats, especially to AV applications. In this regard, two areas are studied in this paper: the common practice of threat modeling in automotive and the ISO/SAE 21434 standard, and sensors and machine learning (ML) algorithms for AV perception systems and potential cyber-physical attacks. A comparative threat analysis for an AV perception system with the ISO/SAE 21434 standard and a system-theoretic process analysis for security (STPA-Sec) approach is also demonstrated in this paper. Based on the analysis, this paper proposes a robust threat analysis and risk assessment framework with mathematical modeling to identify cyber-physical threats to AV perception systems that are critical for the driving behaviors and complex interactions of AVs in their operational design domain.

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Section: Cyber-physical Attack On Av Perception Systemsmentioning

confidence: 99%

An Integrated Approach of Threat Analysis for Autonomous Vehicles Perception System

et al. 2023

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“…Nowadays, many ADAS are launched by automotive manufacturers to prevent possible accidents. Even today's vehicles already have a set of components such as traffic sign recognition [11] and lane departure warning systems [12] to detect traffic signs, lane keeping assist [13] to help drivers to keep their vehicles on track, and several other features that help alert the driver by audio or visual as well dhidik.prastiyanto@mail.unnes.ac.id [14]. For example, Adaptive Cruise Control (ACC) is quite good in increasing safety and comfort [15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cruise Control tuned by Genetic Algorithm for Safe Distance of Automated Vehicle

Prastiyanto,

Apriaskar,

Ibrahim

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The importance of safety in transportation has forced developments in automated vehicles. Many control methods have been introduced to ensure safe distance between automated vehicle and another car. This article presents an adaptive cruise control whose parameters are tuned by genetic algorithm for the case study of safe distance in automated vehicle. Two genetic algorithms with two different objective functions, i.e., integral square error (ISE) and integral absolute error (IAE), were evaluated. The results show that genetic algorithms could work well using both functions indicated by the obtained best fitness values through generations. The optimal controller parameter obtained by them also seems to be similar each other, including the distance, velocity and acceleration response of the automated vehicle. It is only the standard deviation that differs them. After 20 running tests, IAE produced 0.0288, 0.0439, and 0.0134 in standard deviation for verr_gain, xerr_gain, vx_gain, respectively, while ISE got 0.0169, 0.0755, and 0.0101. From the total values of standard deviation, we can conclude that GA with IAE performs slightly better since it has smaller value indicating that it has better repeatability.

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“…Traffic signs are an important aspect of road infrastructure, as they contain essential information about what is coming ahead, which ADAS systems like Mobileye use as supplementary information for scene understanding. Failure to robustly perceive and process the road scene has led to multiple fatal crashes involving commercially deployed AVs as well [3]. Sample frames from our Missing Traffic Signs Video Dataset (MTSVD), containing context cues but no signs are present in Fig.…”

Section: Introductionmentioning

confidence: 99%

CueCAn: Cue Driven Contextual Attention For Identifying Missing Traffic Signs on Unconstrained Roads

Gupta¹,

Subramanian²,

Jawahar³

et al. 2023

Preprint

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A Survey of the Inadequacies in Traffic Sign Recognition Systems for Autonomous Vehicles

Cited by 19 publications

References 0 publications

An Integrated Approach of Threat Analysis for Autonomous Vehicles Perception System

An Integrated Approach of Threat Analysis for Autonomous Vehicles Perception System

Adaptive Cruise Control tuned by Genetic Algorithm for Safe Distance of Automated Vehicle

CueCAn: Cue Driven Contextual Attention For Identifying Missing Traffic Signs on Unconstrained Roads

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