BSM-Data Reuse Model Based on In-Vehicular Computing

Abstract

Basic Safety Messages that are frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. Otherwise, when considering the way Basic Safety Messages (BSMs) are treated, multiple deficiencies prevent the latter to be capable of constituting a precious data source. As we know, data become more useful the more widely are used, which is the exact opposite of what happens with the BSMs that exist only temporarily, used locally, considered disposable, and are never stored. In this paper, we introduce a data reuse model that retains collected BSMs, stores, and processes them inside the vehicle constituting a continuous data source holding retained snapshots along the roadway. Our model provided a primary data source available on a large scale, considered to be a worthy dataset for machine learning tasks, capable of visualizing different traffic-related indicators to enhance analytics and support decisions-making. In the study case, we set up an in-vehicle data platform, where we achieved an 80% of BSMs size reduction and provided a rich set of APIs to serve applications. We also adopted the Artificial Neural Networks (ANN) as an information processing paradigm for performing traffic volume prediction, where the obtained results have reached over 99% of accuracy.

Keywords

• BSM
• data reuse
• in-vehicule computing
• connected vehicle
• VANETs
• machine learning