Graphic Interfaces in ADAS

Masola, Alessio; Gabbi, Cristian; Castellano, Andrea; Capodieci, Nicola; Burgio, Paolo

doi:10.1145/3411170.3411259

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…This is again motivated by the requirements of the bus operator, which wants to have a system that can be easily installed in the existing fleet of electric buses at a reasonable cost. In such a case, the design of the graphical user interface of the HMI [44] becomes particularly important, as the visual cues are the only means to communicate the ADAS decisions to the driver. Therefore, we describe the HMI in detail and draw conclusions about its influence on the correctness of the docking manoeuvres in our tests.…”

Section: Related Workmentioning

confidence: 99%

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

Esfandiyar,

Ćwian,

Nowicki

et al. 2023

Remote Sensing

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Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find it difficult to execute docking manoeuvres on the charger. A practical solution to this problem requires a suitable advanced driver-assistance system (ADAS), which is a system used to automatise and make safer some of the tasks involved in driving a vehicle. In the considered case, ADAS supports docking to the electric charging station, and thus, it must solve two issues: precise positioning of the bus relative to the charger and motion planning in a constrained space. This paper addresses these issues by employing GNSS-based positioning and optimisation-based planning, resulting in an affordable solution to the ADAS for the docking of electric buses while recharging. We focus on the practical side of the system, showing how the necessary features were attained at a limited hardware and installation cost, also demonstrating an extensive evaluation of the fielded ADAS for an operator of public transportation in the city of Poznań in Poland.

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Section: Related Workmentioning

confidence: 99%

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

Esfandiyar,

Ćwian,

Nowicki

et al. 2023

Remote Sensing

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“…The HMI design, instead of focusing on critical situations only, also concentrates on more common circumstances of cooperation, to evaluate the ease of understanding and the level of trust in the system. More detailed descriptions and results are reported in [41,42].…”

Section: Visual Hmimentioning

confidence: 99%

From the Concept of Being “the Boss” to the Idea of Being “a Team”: The Adaptive Co-Pilot as the Enabler for a New Cooperative Framework

et al. 2021

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The “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task.

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“…Since the complexity of driving is significantly increasing, the role of information systems is rapidly evolving as well. Traditional in-vehicle information systems are designed to inform the driver about dangerous situations without overloading him/her with an excessive amount of information [74]. For this reason, the most relevant issues related to evaluation of the HMI from a human factors perspective are related to inattention and workload topics [75].…”

Section: Human-machine Interface (Hmi)mentioning

confidence: 99%

On Driver Behavior Recognition for Increased Safety: A Roadmap

Davoli

Martalò

Cilfone

et al. 2020

Safety

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Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

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Graphic Interfaces in ADAS

Cited by 7 publications

References 13 publications

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing

From the Concept of Being “the Boss” to the Idea of Being “a Team”: The Adaptive Co-Pilot as the Enabler for a New Cooperative Framework

On Driver Behavior Recognition for Increased Safety: A Roadmap

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