Real-time estimation of road slope based on multiple models and multiple data fusion
Real-time estimation of road slope based on multiple models and multiple data fusion
复制标题
基于多模型、多数据融合的道路坡度实时估计
DOI：
10.1016/j.measurement.2021.109609
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发表时间：
2021-05-29
期刊：
影响因子：
5.6
通讯作者：
You, Yong
中科院分区：
文献类型：
--
作者：
Feng, Jihao;Qin, Datong;You, Yong
虽然车辆的行驶条件是复杂的，但在传统的坡度估计方法中使用单个模型。这降低了在真实的时间内跟踪实际道路坡度变化的能力，导致低估计精度。此外，基于单一估计方法的斜率估计受到制动、换档和传感器故障的影响。这降低了斜率估计的鲁棒性和可靠性。针对这些问题，提出了一种基于多模型多数据融合的斜率估计算法。首先，对于每种估计方法（基于运动学和基于动力学），开发了两层交互多模型（TLIMM）斜率估计算法。第二，决策层估计融合是基于两种方法估计的坡度数据。实验结果表明，所提出的TLIMM算法和基于TLIMM的多数据估计融合方法能有效提高斜率估计的精度、鲁棒性和可靠性。
Although driving conditions of vehicles are complex, a single model is used in conventional slope estimation methods. This reduces the ability to track changes in actual road slopes in real time, resulting in low estimation accuracy. Furthermore, slope estimation based on a single estimation method are affected by braking, shifting, and sensor failure. This reduces the robustness and reliability of the slope estimation. To address these problems, we propose a slope estimation algorithm based on multi-model and multi-data fusion. First, for each estimation method (kinematics-based and dynamics-based), a two-layer interacting multiple model (TLIMM) slope estimation algorithm is developed. Second, the decision-level estimation fusion is based on the slope data estimated by the two methods. The experimental results show that the proposed TLIMM algorithm and multi-data estimation fusion method based on TLIMM can effectively improve the accuracy, robustness and reliability of the slope estimation.