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To balance lane departure issues under various driving styles and visibility conditions, this paper proposes an adaptive lane departure warning strategy. Driving behavior data is collected using driving simulator, and parameters such as lateral position deviation, time-to-lane crossing, and deviation speed are selected. Based on the fuzzy clustering algorithm, drivers are classified according to their driving styles. Subsequently, a Radial Basis Function Neural Network (RBFNN) model is introduced to achieve driving style recognition. Different warning thresholds are designed to construct an adaptive lane departure warning model. Finally, a driver-in-the-loop experiment is conducted. The results indicate that the model achieves an overall accuracy of 94.7%, it can effectively output dynamic thresholds for different drivers and visibility, thereby reducing false alarms and enhancing the applicability of the lane departure warning system.
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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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