Support system for safe driving heavily depends on global navigation satellite system. Pseudoranges between satellites and vehicles are measured to compute vehicles’ positions and their relative positions. In urban areas, however, multipath errors (MPEs) in pseudoranges, caused by obstruction and reflection of roadside buildings, greatly degrade the precision of relative positions. On the other hand, simply removing all reflected signals might lead to a shortage of satellites in fixing positions. In our previous work, we suggested solving this dilemma by cooperative relative positioning (CoRelPos) which exploits spatial correlation of MPEs. In this paper, we collected the trace data of pseudoranges by driving cars in urban areas, analyzed the properties of MPEs (specifically, their dependency on signal strength, elevation angles of satellites, and receivers’ speeds), and highlighted their spatial correlation. On this basis, the CoRelPos scheme is refined by considering the dynamics of MPEs. Evaluation results under practical vehicular scenarios confirm that properties of MPEs can be exploited to improve the precision of relative positions. 1. Introduction The rapid and wide spread of motor vehicles after World War II has greatly changed human society. On one hand, it frees common people from the limitations of their geography and facilitates remote travel. On the other hand, it also brings some undesirable consequences such as vehicle accidents, which often lead to severe body injuries or even the loss of precious lives. To reduce vehicle accidents, many efforts have been devoted to support systems for safe driving, from two aspects. (i) Each vehicle independently detects the presence of nearby vehicles by using cameras or radars. But this fails to work when the line of sight (LOS) path between vehicles is obstructed. (ii) Vehicles cooperate to measure their distances, which relies on global navigation satellite system (GNSS) [1] and intervehicle communications (IVC) [2]. In this system, each vehicle receives positioning signals from satellites, measures the pseudoranges between itself and satellites, and on this basis computes its own position. Each vehicle further exchanges its own position and moving speed with nearby vehicles via IVC. Based on current positions and moving speeds of two vehicles, their distance (relative position) several seconds later is estimated, and the driver is warned in case a collision is predicted to happen. Such applications have been extensively evaluated in the ASV4 (advanced safety vehicle) experiments in Japan [3]. In
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