Driving Context Detection and Validation using Knowledge-based Reasoning

Abderraouf Khezaz, Abderraouf Khezaz, Manolo Hina, Hongyu Guan, Amar Ramdane-Cherif


The intensive research on artificial intelligence and internet of things is speeding up the rise of smart cities and autonomous vehicles. In order to ensure the safety of the drivers and pedestrians, the transportation network needs to be connected to its surroundings and consider every valuable piece of information it can gather. Knowledge bases have proven themselves to be efficient in the storage and processing of structured data, making them interesting solutions for the management of transportation networks. This study focuses on the building of a driving simulator allowing the gathering of practical data that can be processed by an ontology and a set of rules, and can quickly and continuously infer a result to suggest the driver on an optimal choice to make. The accuracy results are encouraging, yet giving us extra room for improvement.


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