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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Probabilistic and Holistic Prediction of Vehicle States Using Sensor Fusion for Application to Integrated Vehicle Safety Systems
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Probabilistic and Holistic Prediction of Vehicle States Using Sensor Fusion for Application to Integrated Vehicle Safety Systems

机译：使用传感器融合技术的车辆状态概率和整体预测在集成车辆安全系统中的应用

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摘要

This paper presents a probabilistic and holistic prediction algorithm for vehicle states using multisensor fushion. Three concerns are mainly considered in this paper, i.e., reliable and reasonable information fusion, extension of predicted states, and real-time evaluation of prediction uncertainties. The main idea of this paper is that a state-prediction problem can be solved as a multistage optimal estimation problem based on the current vehicle motion, a road geometry description in the current body-fixed frame, a path-following behavior model, and the error covariance of each. The prediction algorithm consists of two sequential parts. The first part is estimation, which contains a vehicle filter that estimates the current vehicle states, and a road geometry filter, which approximates the road geometry. The second part is prediction, which consists of a path-following model that generates the future desired yaw rate, which acts as a virtual measurement, and a vehicle predictor, which predicts the future vehicle states by a maximum-likelihood filtering method. The prediction performance of the proposed method has been investigated via vehicle tests. Moreover, its applicability to integrated vehicle safety system (IVSS) has been validated via computer simulation studies. It is shown that the state-prediction performance can be significantly enhanced by the proposed prediction algorithm compared with conventional methods. The enhancement of the prediction performance allows for the improvement of driver assistance functions of an IVSS by providing accurate predictions about the future driving environment.
机译：本文提出了一种基于多传感器融合的车辆状态概率和整体预测算法。本文主要考虑三个方面，即可靠合理的信息融合，预测状态的扩展以及预测不确定性的实时评估。本文的主要思想是，状态预测问题可以解决为基于当前车辆运动，当前车身固定框架中的道路几何描述，路径遵循行为模型以及模型的多阶段最优估计问题。每个的误差协方差。预测算法由两个顺序部分组成。第一部分是估算，其中包含估算当前车辆状态的车辆过滤器和近似于道路几何形状的道路几何形状过滤器。第二部分是预测，它由一个路径跟踪模型和一个车辆预测器组成，该路径跟踪模型生成用作虚拟测量的未来期望偏航率，该预测器通过最大似然滤波方法预测未来的车辆状态。通过车辆测试研究了该方法的预测性能。此外，它已通过计算机仿真研究验证了其对综合车辆安全系统（IVSS）的适用性。结果表明，与传统方法相比，所提出的预测算法可以显着提高状态预测性能。预测性能的增强通过提供有关未来驾驶环境的准确预测，可以改善IVSS的驾驶员辅助功能。

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