Effectiveness Analysis on Human-Machine Information Interaction of Intelligent Highway. (11th January 2022)

• Record Type: Journal Article
• Authors: Zhou, Yicheng; Sun, Tuo; Wen, Shunzhi; Zhong, Hao; Cui, Youkai; Xie, Jiemin; Wu, Wei; Ma, Wanjing
• Other Names: Du Muqing Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2022(2022)
• Publication Date: 2022-01-11
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2022/2728984
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : Different human-machine collaboration modes and driving simulation tests with the orthogonal method considered are designed for a series of typical intelligent highway landscapes. The feedback of drivers under different interaction modes is evaluated through NASA-LTX questionnaire, driving simulator, eye tracker, and electroencephalograph (EEG). This optimal interaction mode (including voice form, broadcasting timing, and frequency) of each driving assistance scene in CVI (Cooperative Vehicle Infrastructure) environment under the conditions of high and low traffic is determined from subjective and objective perspectives. In accordance with feedback of these subjects on each set scene, the voice information structure of each assistance mode plays the most important role on drivers followed by the broadcasting timing and frequency. These broadcasts which provide good effects include scenarios such as various assistance scenes at curves and an early warning timing at a long-distance trip as well as a high early warning frequency; in addition, as for an exit-tip assistance scenario, a voice mode assistance is preferred; and for various speed assistance scenes, the beep mode is better. Furthermore, it is found that, at a higher traffic level but a short-distance trip, an early warning timing is favored generally for various scenes while under a low traffic level, a long-distance early warning timing is better.