针对车辆群组动画仿真的路网语义模型

A Semantic Model of Road Networks for Traffic Animations

针对现有路网模型在处理交叉路口处冲突线路的自动分离以及用于车辆模拟时坐标定位的高效反馈等方面存在的问题,提出一种面向车辆群组动画仿真的新颖的路网模型——路网数据层次化语义模型.通过扩充一般意义上车道的概念,给出一种采用离散压缩技术生成并满足给定约束的广义车道,并将其作为语义模型的原子层数据;然后通过关系定义构建层次语义数据来描述车道之间的拓扑连接、邻接以及冲突关系;根据这些语义信息可以自动获得交叉路口处的相位分配信息,从而实现冲突线路的分离.实验结果表明,对于某城市中心城区约5km半径范围内的路网,只需要输入车道线矢量数据,文中模型所有语义数据以及路口处的交通相位数据就可以在约200s内全自动生成,将这些数据用于动画仿真时可以提高车辆获取坐标的效率约1倍多;并且可以很好地改善车辆运动轨迹的平滑性.

Existing road network models fail to group conflicting lanes at iunctions and calculate vehicles＇ locations efficiently in traffic animations. This paper presents a novel model--hierarchical semantic model of road networks to address these problems. It extends traditional descriptions of lanes and gives a broad road lane--Lane, which is subjected to certain constraints. We obtain Lanes according discretization and compression and then treat them as the atomic layer of the semantic model. We then build hierarchical semantic concepts to describe connection/adjacency/conflict relations among Lanes. Traffic phases on conflicting areas could then be generated efficiently to separate Lanes with different directions according to these semantic data. Experimental results show that： for the road networks within 5 km from the downtown of a city, all of the semantic data and traffic phase information could be obtained automatically in 200 s. The adoption of such data can significantly improve the efficiency of location calculations in traffic animations and give a smooth trajectory presentation.