scholarly journals Improving Driver Emotions with Affective Strategies

2019 ◽  
Vol 3 (1) ◽  
pp. 21 ◽  
Author(s):  
Michael Braun ◽  
Jonas Schubert ◽  
Bastian Pfleging ◽  
Florian Alt
Keyword(s):  
User Interfaces ◽  
Road Safety ◽  
Affective Computing ◽  
Qualitative Data ◽  
Emotional States ◽  
Ambient Light ◽  
Road Users ◽  
Recent Advances ◽  
Simulator Study ◽  
Automotive User Interfaces

Drivers in negative emotional states, such as anger or sadness, are prone to perform bad at driving, decreasing overall road safety for all road users. Recent advances in affective computing, however, allow for the detection of such states and give us tools to tackle the connected problems within automotive user interfaces. We see potential in building a system which reacts upon possibly dangerous driver states and influences the driver in order to drive more safely. We compare different interaction approaches for an affective automotive interface, namely Ambient Light, Visual Notification, a Voice Assistant, and an Empathic Assistant. Results of a simulator study with 60 participants (30 each with induced sadness/anger) indicate that an emotional voice assistant with the ability to empathize with the user is the most promising approach as it improves negative states best and is rated most positively. Qualitative data also shows that users prefer an empathic assistant but also resent potential paternalism. This leads us to suggest that digital assistants are a valuable platform to improve driver emotions in automotive environments and thereby enable safer driving.

scholarly journals Touchless Selection Schemes for Intelligent Automotive User Interfaces With Predictive Mid-Air Touch

2019 ◽  
Vol 11 (3) ◽  
pp. 18-39
Author(s):  
Bashar I. Ahmad ◽  
Chrisminder Hare ◽  
Harpreet Singh ◽  
Arber Shabani ◽  
Briana Lindsay ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
User Interfaces ◽  
The Road ◽  
Promising Strategy ◽  
Pointing Movement ◽  
Simulator Study ◽  
And Performance ◽  
Bayesian Inference Framework ◽  
Automotive User Interfaces ◽  
Expert Workshop

Predictive touch technology aims to improve the usability and performance of in-vehicle displays under the influence of perturbations due to the road and driving conditions. It fundamentally relies on predicting and early in the freehand pointing movement, the interface item the user intends to select, using a novel Bayesian inference framework. This article focusses on evaluating facilitation schemes for selecting the predicted interface component whilst driving, and without physically touching the display, thus touchless. Initially, several viable schemes were identified in a brainstorming session followed by an expert workshop with 12 participants. A simulator study with 24 participants using a prototype predictive touch system was then conducted. A number of collected quantitative and qualitative measures show that immediate mid-air selection, where the system autonomously auto-selects the predicted interface component, may be the most promising strategy for predictive touch.

scholarly journals Affective Automotive User Interfaces–Reviewing the State of Driver Affect Research and Emotion Regulation in the Car

2022 ◽  
Vol 54 (7) ◽  
pp. 1-26
Author(s):  
Michael Braun ◽  
Florian Weber ◽  
Florian Alt
Keyword(s):  
Emotion Regulation ◽  
User Interfaces ◽  
Road Safety ◽  
Driver Behavior ◽  
The State ◽  
Cultural Aspects ◽  
Methodological Considerations ◽  
The Impact ◽  
Affective Interaction ◽  
Automotive User Interfaces

Affective technology offers exciting opportunities to improve road safety by catering to human emotions. Modern car interiors enable the contactless detection of user states, paving the way for a systematic promotion of safe driver behavior through emotion regulation. We review the current literature regarding the impact of emotions on driver behavior and analyze the state of emotion regulation approaches in the car. We summarize challenges for affective interaction in the form of technological hurdles and methodological considerations, as well as opportunities to improve road safety by reinstating drivers into an emotionally balanced state. The purpose of this review is to outline the community’s combined knowledge for interested researchers, to provide a focussed introduction for practitioners, raise awareness for cultural aspects, and to identify future directions for affective interaction in the car.

scholarly journals P5.007 Road safety active education methods in the opinion of young road users

2021 ◽  
Author(s):  
Dagmara Jankowska-Karpa ◽  
Justyna Wacowska-Slezak ◽  
Aneta Wnuk
Keyword(s):  
Road Safety ◽  
Road Users

scholarly journals Gender Effects in Young Road Users on Road Safety Attitudes, Behaviors and Risk Perception

2016 ◽  
Vol 7 ◽  
Author(s):  
Pierluigi Cordellieri ◽  
Francesca Baralla ◽  
Fabio Ferlazzo ◽  
Roberto Sgalla ◽  
Laura Piccardi ◽  
...  
Keyword(s):  
Risk Perception ◽  
Road Safety ◽  
Gender Effects ◽  
Safety Attitudes ◽  
Road Users

scholarly journals Representativeness of Czech In-Depth Accident Data

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 40
Author(s):  
Robert Zůvala ◽  
Kateřina Bucsuházy ◽  
Veronika Valentová ◽  
Jindřich Frič
Keyword(s):  
Road Safety ◽  
National Level ◽  
Road Accident ◽  
General Applicability ◽  
Weighting Method ◽  
The Czech Republic ◽  
Depth Data ◽  
Road Users ◽  
Depth Study ◽  
Accident Data

Road accident occurrence is often the result of driving system malfunctions, and road safety improvements need to focus on all basic driving components—the vehicle, road infrastructure, and road users. Only focusing on one type of improvement does not necessarily lead to increased road safety. Instead, improved road safety requires comprehensive measures that consider all factors using in-depth accident analysis. The proposed measures, based on the findings from in-depth data that have general applicability, are necessary to determine whether data gained from in-depth studies adequately represent national statistics. This article aims to verify the representativeness of the Czech In-Depth Accident Study at a national level. The main contribution of this article lies in the use of a weighting method (specifically, a raking procedure) to generalise research results and render them applicable to a whole population. The obtained results could be beneficial at the national level, in the Czech Republic, and also on the supranational level. The applicability of this method on accident data is verified; thus, the method can be applied also in other countries or can be used to verify the applicability of conclusions from the Czech in-depth study also on a European or worldwide level.

scholarly journals An IoT Architecture for Assessing Road Safety in Smart Cities

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Abd-Elhamid M. Taha
Keyword(s):  
Road Safety ◽  
Markov Models ◽  
Smart Cities ◽  
Dynamic Assessment ◽  
Route Planning ◽  
Cost Effective ◽  
World Health ◽  
Road Users ◽  
Health Organization ◽  
The Impact

The Safe System (SS) approach to road safety emphasizes safety-by-design through ensuring safe vehicles, road networks, and road users. With a strong motivation from the World Health Organization (WHO), this approach is increasingly adopted worldwide. Considerations in SS, however, are made for the medium-to-long term. Our interest in this work is to complement the approach with a short-to-medium term dynamic assessment of road safety. Toward this end, we introduce a novel, cost-effective Internet of Things (IoT) architecture that facilitates the realization of a robust and dynamic computational core in assessing the safety of a road network and its elements. In doing so, we introduce a new, meaningful, and scalable metric for assessing road safety. We also showcase the use of machine learning in the design of the metric computation core through a novel application of Hidden Markov Models (HMMs). Finally, the impact of the proposed architecture is demonstrated through an application to safety-based route planning.

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