Abstract
The Internet of Vehicles (IoV) is an important artificial intelligence research field for intelligent transportation applications. Complex event interactions are important methods for data flow processing in a Vehicle to Everything (V2X) environment. Unlike the classic Internet of Things (IoT) systems, data streams in V2X include both temporal information and spatial information. Thus, effectively expressing and addressing spatiotemporal data interactions in the IoV is an urgent problem. To solve this problem, we propose a spatiotemporal event interaction model (STEIM). STEIM uses a time period and a raster map for its temporal model and spatial model, respectively. In this paper, first, we provide a spatiotemporal operator and a complete STEIM grammar that effectively expresses the spatiotemporal information of the spatiotemporal event flow in the V2X environment. Second, we describe the design of the operational semantics of the STEIM from the formal semantics. In addition, we provide a spatiotemporal event-stream processing algorithm that is based on the Petri net model. The STEIM establishes a mechanism for V2X event-stream temporal and spatial processing. Finally, the effectiveness of the STEIM-based system is demonstrated experimentally.
Highlights
Introduction eVehicle-to-Everything (V2X), which is the information exchange between vehicular systems and the outside world of information exchange, is a critical technology for future intelligent transportation systems
On the data integration and analysis platform, the data analysis is performed on the industrial personalCcomputer (IPC) and data transmission is conducted via the LTE network
Based on the operational semantics of the spatiotemporal event interaction model (STEIM) model and the spatiotemporal event flow processing (Algorithm 1), which are based on a Petri net, the following experiment is performed
Summary
Many studies have discussed complex event handling in V2X. e approach proposed in Ashok and Vaidehi [16] primarily relies on spatial abstractions of each object and mining frequent temporal patterns in a sequence of frames to form a regular temporal pattern. is process is repeated for each window of sequences, and support for a temporal sequence is obtained to discover the regular patterns of normal events. The identification methods of road sections with multiple traffic accidents mainly include the following categories: (1) applying traffic conflict analysis technology [27]. It judges the possible traffic conflict points by predicting the running track of vehicles and judges the more serious traffic conflicts as the road section with frequent accidents. (2) Identification method based on historical traffic accident data It first segmented the studied road manually. For the visualization of accident-prone road sections, Fan et al [30] proposed context-aware big data analysis and spatiotemporal visualization methods for traffic data such as road networks, vehicles, drivers, and weather. E result is the establishment of a mechanism that can be formulated in V2X
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