基于航向角变化的趋势集合轨迹特征划分算法

在GPS或网络信号受到干扰或遮蔽时,导航精度和自主性会大大降低,组合导航技术能弥补这一缺陷。针对组合导航匹配实时、快速和准确的应用需求,本文提出一种具有实时性、高准确性的基于航向角变化的趋势集合特征划分算法。该算法通过计算轨迹点的航向角变化量,对趋势确定集合和趋势待定集合的趋势状态进行赋值,能有效地划分出轨迹上各段的特征点集合。算法还对由外界因素（如超车、紧急避让和驾驶员不同驾驶习惯等）引起的无效特征情况,进行趋势集合状态的分析和判断,以保证特征划分的准确性。本文以北京西五环地区的车辆轨迹数据为例,进行了实地跑车实验表明,基于航向角的趋势集合特征划分算法,在实时性和提取效果方面,具有明显的优势,算法简单可行、高效、可操作性强。

An Extraction Algorithm of Track Features Based on Trend Set of Heading Angle Variable

When the GPS or a network signal is disturbed or obscured, the precision and autonomy of navigation will be greatly reduced. The integrated navigation technology can compensate for this defect. In view of the application requirements of real-time, quickness and accuracy of trail data and road data during the integrated navigation process, this paper puts forward a real-time, high accuracy algorithm which is entitled as an extraction algorithm of track features based on trend set of heading angle variable. The algorithm picks up the calculation and analysis of heading angle in trail data points for navigation, assigns values to the confirmed trend set and the pending trend set innovatively, which effectively improve the real-time aspect and the accuracy of this algorithm in the navigation trajectory data extraction, ensuring the efficiency and precision of the integrated navigation. This algorithm also provides analysis and judgment on trend set status for the invalid features which is caused by external factors (e.g. the overtaking, the emergency avoidance and the drivers’ driving habits). The invalid features will be filtered and eliminated using the buffer of the pending trend set, ensuring the accuracy of the feature extraction. The vehicle trajectory data in Beijing is used for conducting experiments. The results show that the extraction algorithm of track features based on trend set of heading angle variable has obvious advantages in real-time application and extracting effect, and the buffer judging with the pending trend set is able to rule out some invalid features and interferences caused by external factors. The proposed algorithm is not only simple and feasible, but also has high efficiency and strong maneuverability.