Literature DB >> 31987518

Evaluating advanced driver-assistance system trainings using driver performance, attention allocation, and neural efficiency measures.

Maryam Zahabi1, Ashiq Mohammed Abdul Razak2, Ashley E Shortz3, Ranjana K Mehta2, Michael Manser4.   

Abstract

There are about 44 million licensed older drivers in the U.S. Older adults have higher crash rates and fatalities as compared to middle-aged and young drivers, which might be associated with degradations in sensory, cognitive, and physical capabilities. Advanced driver-assistance systems (ADAS) have the potential to substantially improve safety by removing some of driver vehicle control responsibilities. However, a critical aspect of providing ADAS is educating drivers on their operational characteristics and continued use. Twenty older adults participated in a driving simulation study assessing the effectiveness of video-based and demonstration-based training protocols in learning ADAS considering gender differences. The findings revealed video-based training to be more effective than demonstration-based training in improving driver performance and reducing off-road visual attention allocation and mental workload. In addition, female drivers required lower investment of mental effort (higher neural efficiency) to maintain the performance relative to males and they were less distracted by ADAS. However, male drivers were faster in activating ADAS as compared to females since they were monitoring the status of ADAS features more frequently while driving. The findings of this study provided an empirical support for using video-based approach for learning ADAS in older adults to improve driver safety and supported previous findings on older adults' learning that as age increases there is a tendency to prefer more passive and observational learning methods.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  ADAS; Older adults; fNIRS

Year:  2020        PMID: 31987518     DOI: 10.1016/j.apergo.2019.103036

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  2 in total

1.  Characterization of Indicators for Adaptive Human-Swarm Teaming.

Authors:  Aya Hussein; Leo Ghignone; Tung Nguyen; Nima Salimi; Hung Nguyen; Min Wang; Hussein A Abbass
Journal:  Front Robot AI       Date:  2022-02-17

2.  Smart Steering Sleeve (S3): A Non-Intrusive and Integrative Sensing Platform for Driver Physiological Monitoring.

Authors:  Chuwei Ye; Wen Li; Zhaojian Li; Gopi Maguluri; John Grimble; Joshua Bonatt; Jacob Miske; Nicusor Iftimia; Shaoting Lin; Michele Grimm
Journal:  Sensors (Basel)       Date:  2022-09-26       Impact factor: 3.847

  2 in total

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