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This paper elaborated the overall framework of commercial vehicle fatigue early warning system, and analyzed in details the research status of monitoring system, human machine interface and fatigue detection method. The paper pointed out that the future monitoring system should have the ability of high stability, short delay and massive data processing, and divided the warning into two levels, and defined the effective human-computer interaction respectively. The paper then analyzed 4 categories of fatigue detection methods, and indicated that the fatigue detection method based on multi-feature information fusion will be the main research direction in the future. The paper finally revealed the difficulties of the current research, and prospected research of the commercial vehicle fatigue warning system from 3 aspects, i.e. obtaining more driver information, extracting more fatigue features, and reducing the dependence on specific fatigue features.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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