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With the advancement of intelligent driving technology, high-precision vehicle status information has become important urgent. Road gradient is a crucial parameter for vehicle operation, having a significant impact on the vehicle’s dynamics control. High-precision and low-latency road gradient estimation is a prerequisite for precise control, which can effectively enhance the intelligence level of the vehicle. Adaptive Extended Kalman Filter (AEKF) is widely used for road gradient estimation, but exhibits limitations in complex operating conditions with different noise levels. This paper proposes an improved adaptive Kalman filter algorithm that enhances the estimation accuracy by introducing dynamic noise scaling factors. The effectiveness of the proposed method is validated through simulation tests under double lane change conditions and steady-state circular motion conditions. The results show that the proposed method achieving a road gradient estimation accuracy with a Root Mean Square Error (RMSE) of less than 2°.
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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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