Full
Outline
To enhance the accuracy and adaptability of electric heavy-duty truck speed prediction under various operating conditions, a speed prediction method based on information fusion from vehicles-road-traffic is proposed. A real-road traffic simulation model is established using SUMO to acquire vehicle and road traffic data. Principal components analysis is applied for data dimensionality reduction, with input features for the speed prediction model selected based on contribution rates. A speed prediction algorithm integrating Radial Basis Function-Linear Regression is designed, utilizing a sliding time window to update the prediction model. Joint simulation results using MATLAB and SUMO demonstrate that, compared to using vehicle speed information alone, the proposed method reduces the mean root-mean-square error and mean absolute error in speed prediction by 39.86% and 40.48%, respectively; compared to backpropagation neural networks, the average reduction is 40.30% and 41.84%; compared to generalized regression neural networks, the average reduction is 31.78% and 35.28%. Through Hardware-in-the-Loop simulation, the deployability and real-time capability of the vehicle speed prediction method are further validated.
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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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