An integrated method for Adaptive automotive Human Machine Interfaces

Heigemeyr, Andreas; Harrer, Andreas

doi:10.1109/itsc.2013.6728290

Cited by 3 publications

(8 citation statements)

References 16 publications

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“…This paper presented concepts for context-aware information management which have been embedded in the integrated method for adaptive automotive HMIs [11]. The information management system was implemented and evaluated with the help of a simulator study.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Both sub-models are integrated as they are derived from the same parent class BlackboardObject. We described in [11] that situational information represents the current context and is modeled as a taxonomy. Occurring messages are represented equally.…”

Section: Data Modelmentioning

confidence: 99%

“…It continuously executes a control cycle which is depicted in figure 3. The control cycle is defined as follows: situation analysis modules continuously analyse the current driving situation in accordance with [11]. They place relevant situational information on the blackboard and indicate if a driving situation has been successfully classified.…”

Section: Information Managermentioning

confidence: 99%

“…Accordingly, relevant situational information was read and stored in the situation model. In accordance with [11], the information was interpreted and analysed in order to classify the current driving situation. Message sources were simulated by a script-like mechanism which allowed the emission of instances of the message taxonomy at defined trigger marks.…”

Section: Softwarementioning

confidence: 99%

“…In [11] we have presented an integrated method for adaptive HMIs. It comprises concepts for the perception, modeling and analysis of driving situations.…”

Section: Introductionmentioning

confidence: 99%

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Information Management for Adaptive Automotive Human Machine Interfaces

Heigemeyr

Harrer

2014

Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Adaptive Human Machine Interfaces (HMI) provide a substantial contribution to avoid information overload of the driver. This paper presents novel concepts for context-aware information management in the sector of adaptive automotive HMI. In particular, we describe an architectural design which supports an easy integration of multiple strategies to manipulate and to adapt the in-vehicle information flow depending on the current driving context. The presented architecture follows the blackboard design pattern and enables opportunistic reasoning in the automotive domain. We further present a novel fusion strategy for messages based on a taxonomic message model. The fusion strategy allows to combine two or more low-level messages and to replace them with a higher-level message in order to reduce the information load for the driver. The described concepts have been evaluated with the help of a driving simulator study with N = 41 test persons. The results show a significant reduction of the subjective workload if the information manager is applied.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Data Modelmentioning

confidence: 99%

Section: Information Managermentioning

confidence: 99%

Section: Softwarementioning

confidence: 99%

“…In [11] we have presented an integrated method for adaptive HMIs. It comprises concepts for the perception, modeling and analysis of driving situations.…”

Section: Introductionmentioning

confidence: 99%

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Information Management for Adaptive Automotive Human Machine Interfaces

Heigemeyr

Harrer

2014

Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Towards Implicit Interaction in Highly Automated Vehicles - A Systematic Literature Review

Stampf

Colley

Rukzio

2022

Proc. ACM Hum.-Comput. Interact.

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The inclusion of in-vehicle sensors and increased intention and state recognition capabilities enable implicit in-vehicle interaction. Starting from a systematic literature review (SLR) on implicit in-vehicle interaction, which resulted in 82 publications, we investigated state and intention recognition methods based on (1) their used modalities, (2) their underlying level of automation, and (3) their considered interaction focus. Our SLR revealed a research gap addressing implicit interaction in highly automated vehicles (HAVs). Therefore, we discussed how the requirements for implicit state and intention recognition methods and interaction based on them are changing in HAVs. With this, open questions and opportunities for further research in this area were identified.

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An Optimization Framework for Information Management in Adaptive Automotive Human–Machine Interfaces

Tufano,

Bahadure,

Tufo

et al. 2023

Applied Sciences

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In recent years, advancements in Intelligent and Connected Vehicles (ICVs) have led to a significant increase in the amount of information to the driver through Human–Machine Interfaces (HMIs). To prevent driver cognitive overload, the development of Adaptive HMIs (A-HMIs) has emerged. Indeed, A-HMIs regulate information flows by dynamically adapting the presentation to suit the contextual driving conditions. This paper presents a novel methodology, based on multi-objective optimization, that offers a more generalized design approach for adaptive strategies in A-HMIs. The proposed methodology is specifically tailored for designing an A-HMI that, by continuously monitoring the Driver–Vehicle–Environment (DVE) system, schedules actions requested by applications and selects appropriate presentation modalities to suit the current state of the DVE. The problem to derive these adaptive strategies is formulated as an optimization task where the objective is to find a set of rules to manage information flow between vehicle and driver that minimizes both the driver’s workload and the queuing of actions. To achieve these goals, the methodology evaluates through two indexes how applications’ requests impact the driver’s cognitive load and the waiting queue for actions. The optimization procedure has been solved offline to define adaptive strategies for scheduling five application requests, i.e., forward collision warning, system interaction, turn indicators, infotainment volume increase, and phone calls. A theoretical analysis has demonstrated the effectiveness of the proposed framework in optimizing the prioritization strategy for actions requested by applications. By adopting this approach, the design of rules for the scheduling process of the A-HMI architecture is significantly streamlined while gaining adaptive capabilities to prevent driver cognitive overload.

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An integrated method for Adaptive automotive Human Machine Interfaces

Cited by 3 publications

References 16 publications

Information Management for Adaptive Automotive Human Machine Interfaces

Information Management for Adaptive Automotive Human Machine Interfaces

Towards Implicit Interaction in Highly Automated Vehicles - A Systematic Literature Review

An Optimization Framework for Information Management in Adaptive Automotive Human–Machine Interfaces

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