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For the energy management of new energy vehicles, it is difficult to predict the vehicle speed in a long-term and accurate way, this paper proposed a model-based parametric prediction method to predict the vehicle speed trajectory using the forward-looking data provided by sensors and GPS. Firstly, the speed prediction algorithm based on Intelligent Driver Model (IDM) was established in term of vehicle dynamics and vehicle stop-turn trend; secondly, data was selected from the NGSIM public data set for parameter calibration and simulation; then the algorithm parameters were calibrated using Genetic Algorithm (GA). The results show that the optimized speed prediction algorithm has high accuracy for long-term speed prediction in both unobstructed and congested traffic environments, the error can be controlled in the range of 8%~13%.
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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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