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首页> 外文会议>COTA international conference of transportation professionals >Unscented Kalman Filtering-Supported Accident Prevention System Based on Prediction of Vehicle Tire Forces Guided by Using Digital Map
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Unscented Kalman Filtering-Supported Accident Prevention System Based on Prediction of Vehicle Tire Forces Guided by Using Digital Map

机译：基于数字地图指导的车辆轮胎力预测的无味卡尔曼滤波辅助事故预防系统

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摘要

Improvements of traffic safety is the ultimate objective of intelligent vehicle systems. This paper presents a novel approach for preventing traffic accidents by predicting vehicle's tire forces in the upcoming roads. The biggest advantage of this approach is to warn the drivers about an upcoming dangerous situation before the accidents. It provides more time for the drivers to make correct decisions to handle the situations. The main contributions of this paper include two aspects: the algorithm of using digital maps to retrieve the road information ahead of vehicle and the algorithm of estimating and evaluating vehicle's dynamics status. A new map data structure is defined to facilitate map information retrievals. The sensor fusion methods like Kalman filter and unscented Kalman filter are employed to minimize the estimation error of the observations. Experimental data validated the proposed algorithm as an efficient method to prevent traffic accidents.
机译：改善交通安全是智能车辆系统的最终目标。本文提出了一种通过预测即将到来的道路上的车辆轮胎力来预防交通事故的新颖方法。这种方法的最大优点是在事故发生前警告驾驶员即将发生的危险情况。它为驾驶员提供了更多的时间来做出正确的决定来处理这些情况。本文的主要贡献包括两个方面：使用数字地图检索车辆前方道路信息的算法以及估计和评估车辆动力学状态的算法。定义了新的地图数据结构，以方便地图信息的检索。采用传感器融合方法，如卡尔曼滤波器和无味卡尔曼滤波器，可最大程度地减少观测值的估计误差。实验数据验证了该算法是预防交通事故的有效方法。

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