Multi-Vehicle Simulation in Urban Automated Driving: Technical Implementation and Added Benefit

Feierle, Alexander; Rettenmaier, Michael; Zeitlmeir, Florian; Bengler, Klaus

doi:10.3390/info11050272

Cited by 12 publications

(2 citation statements)

References 38 publications

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“…References: [56][57][58][59]. Rationale: ORU perceive the signals of the CAV, rely on their accuracy, and perform actions accordingly.…”

Section: Principle A8: Adaptivity To (Traffic) Environmentmentioning

confidence: 99%

Principles for External Human–Machine Interfaces

Wilbrink,

Cieler,

Weiß

et al. 2023

Information

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Automated vehicles will soon be integrated into our current traffic system. This development will lead to a novel mixed-traffic environment where connected and automated vehicles (CAVs) will have to interact with other road users (ORU). To enable this interaction, external human–machine interfaces (eHMIs) have been shown to have major benefits regarding the trust and acceptance of CAVs in multiple studies. However, a harmonization of eHMI signals seems to be necessary since the developed signals are extremely varied and sometimes even contradict each other. Therefore, the present paper proposes guidelines for designing eHMI signals, taking into account important factors such as how and in which situations a CAV needs to communicate with ORU. The authors propose 17 heuristics, the so-called eHMI-principles, as requirements for the safe and efficient use of eHMIs in a systematic and application-oriented manner.

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“…References: [56][57][58][59]. Rationale: ORU perceive the signals of the CAV, rely on their accuracy, and perform actions accordingly.…”

Section: Principle A8: Adaptivity To (Traffic) Environmentmentioning

confidence: 99%

Principles for External Human–Machine Interfaces

Wilbrink,

Cieler,

Weiß

et al. 2023

Information

Self Cite

View full text Add to dashboard Cite

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“…The third paper "Multi-Vehicle Simulation in Urban Automated Driving: Technical Implementation and Added Benefit" by Feierle, Rettenmaier, Zeitlmeir and Bengler [3] investigates the simultaneous interaction between an automated vehicle (AV) and its passenger, and between the same AV and a human driver of another vehicle. For this purpose a multi-vehicle simulation consisting of two driving simulators, one for the AV and one for the manual vehicle was implemented.…”

Section: Assessing the Relationship Of Automated Vehicles And Surrounmentioning

confidence: 99%

Editorial for Special Issue: Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles

et al. 2020

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Today, OEMs and suppliers can rely on commonly agreed and standardized test and evaluation methods for in-vehicle human–machine interfaces (HMIs). These have traditionally focused on the context of manually driven vehicles and put the evaluation of minimizing distraction effects and enhancing usability at their core (e.g., AAM guidelines or NHTSA visual-manual distraction guidelines). However, advances in automated driving systems (ADS) have already begun to change the driver’s role from actively driving the vehicle to monitoring the driving situation and being ready to intervene in partially automated driving (SAE L2). Higher levels of vehicle automation will likely only require the driver to act as a fallback ready user in case of system limits and malfunctions (SAE L3) or could even act without any fallback within their operational design domain (SAE L4). During the same trip, different levels of automation might be available to the driver (e.g., L2 in urban environments, L3 on highways). These developments require new test and evaluation methods for ADS, as available test methods cannot be easily transferred and adapted. The shift towards higher levels of vehicle automation has also moved the discussion towards the interaction between automated and non-automated road users using exterior HMIs. This Special Issue includes theoretical papers a well as empirical studies that deal with these new challenges by proposing new and innovative test methods in the evaluation of ADS HMIs in different areas.

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Measuring and Describing Cooperation Between Road Users—Results from CoMove

Quante,

Stoll,

Baumann

et al. 2024

Cooperatively Interacting Vehicles

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Safe and efficient traffic requires that road users interact and cooperate with each other. Especially in situations which are not explicitly regulated, and the right of way is not clearly defined, it is of great importance that road users are able to communicate their own intentions and understand the communication and cooperation behaviour of the other involved road users. When automated vehicles enter the current traffic system, their ability to fit into the system, that is their ability to communicate and cooperate, will determine their success. Therefore, the development of cooperatively interacting, automated vehicles requires detailed knowledge about human cooperation behaviour in traffic, which can only be obtained using appropriate methods and measures. By focusing on road narrowings and lane changing, this chapter gives an overview on how to measure cooperation between road users, considering methods for data collection, subjective and objective measures of cooperation as well as behaviour modeling, to support the systematic research on cooperation in road traffic. This overview is extended by findings from studies conducted within CoInCar, including results on factors influencing human behaviour in cooperative situations, either in a manual or an automated setting, and initial findings from modeling the cognitive processes underlying cooperative driving behaviour.

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Multi-Vehicle Simulation in Urban Automated Driving: Technical Implementation and Added Benefit

Cited by 12 publications

References 38 publications

Principles for External Human–Machine Interfaces

Principles for External Human–Machine Interfaces

Editorial for Special Issue: Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles

Measuring and Describing Cooperation Between Road Users—Results from CoMove

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