Investigating Wi-Fi, Bluetooth, and Bluetooth Low-Energy Signal Characteristics for Integration in Vehicle–Pedestrian Collision Warning Systems

The purpose of the study is to investigate the comparative field performance of Wi-Fi, Bluetooth Classic (Bluetooth) and Bluetooth Low Energy (BLE) signal modes for integration in vehicle–pedestrian collision warning systems. The study compares these wireless signal modes to find out which one is most appropriate to be utilized in these systems and provides better results in terms of accuracy and functionality. Five factors including received signal strength indicator (RSSI)-distance relationship, rainfall effects on the signals, motion effects, non-line of sight effects and signal transmission rates were selected for evaluation. These factors were selected considering the requirements of vehicle–pedestrian collision warning systems and compared with each other based on experimental outcomes. The results of the experiments indicated the overall superiority of BLE mode over Wi-Fi and Bluetooth modes to be utilized in these systems. Application of this mode may provide the possibility of fast collision warnings thanks to low signal transmission intervals and high probability of simultaneous signal detections by multiple signals scanners. Moreover, the capability of this mode to accurately estimate distance and position is higher than Wi-Fi mode and not significantly different from Bluetooth mode.

Evolution of VANETS to IoV

Advancement in wireless communication technology along with the evolution of low power computational devices, have given rise to the Internet of things paradigm. This paradigm is transforming conventional VANETs into Internet-of- vehicles. This transition has led to a substantial commercial interest; as a result, there has been a significant boost in the field of the Internet of vehicles during the past few years. IoV promises a wide range of applications of commercial interest as well as public entertainment and convenience (collision warning systems, on-demand in-car entertainment, smart parking, traffic information). Applications related to vehicular and passenger safety are particularly of great commercial as well as a research interest as such IoV is going to be a core component in implementing the smart city concept. This paper gives an overview of the transition of conventional VANETs to IoV and highlights the potential applications and challenges faced by the Internet of Vehicles (IoV) paradigm.

MOTORCYCLE EMERGENCY BRAKING WARNING SYSTEM – TRAFFIC SAFETY INNOVATION USING OFFTHE- SHELF TECHNOLOGY

In this paper we present research findings on collision warning systems and their influence on traffic safety, and present MEBWS – Motorcycle Emergency Braking Warning System, a patented innovation developed at the Faculty of Information Studies in Novo mesto. MEBWS analyses motorcycle movement in real-time using an accelerometer and GPS speed measurement and monitors the following vehicles using a LIDAR. In case a dangerous situation is detected, the MEBWS alerts vehicles behind the motorcycle with an autonomous flashing LED. Furthermore, we are developing a simulation model that will allow us to gauge the influence of MEBWS on traffic safety in large traffic systems and its contribution to the European Union’s goal “Vision Zero” – to reduce road deaths to almost zero by 2050.

Sectional Information-Based Collision Warning System Using Roadside Unit Aggregated Connected-Vehicle Information for a Cooperative Intelligent Transport System

Vehicular collision and hazard warning is an active field of research that seeks to improve road safety by providing an earlier warning to drivers to help them avoid potential collision danger. In this study, we propose a new type of a collision warning system based on aggregated sectional information, describing vehicle movement processed by a roadside unit (RSU). The proposed sectional information-based collision warning system (SCWS) overcomes the limitations of existing collision warning systems such as the high installation costs, the need for high market penetration rates, and the lack of consideration of traffic dynamics. The proposed SCWS gathers vehicle operation data through on-board units (OBUs) and shares this aggregated information through an RSU. All the data for each road section are locally processed by the RSU using edge computing, allowing the SCWS to effectively estimate the information describing the vehicles surrounding the subject vehicle in each road section. The performance of the SCWS was evaluated through comparison with other collision warning systems such as the vehicle-to-vehicle communication-based collision warning system (VCWS), which solely uses in-vehicle sensors; the hybrid collision warning system (HCWS), which uses information from both infrastructure and in-vehicle sensors; and the infrastructure-based collision warning system (ICWS), which only uses data from infrastructure. In this study, the VCWS with a 100% market penetration rate was considered to provide the most theoretically similar result to the actual collision risk. The comparison results show that in both aggregation and disaggregation level analyses, the proposed SCWS exhibits a similar collision risk trend to the VCWS. Furthermore, the SCWS shows a high potential for practical application because it provides acceptable performance even with a low market penetration rate (30%) at the relatively low cost of OBU installation, compared to the VCWS requirement of a high market penetration rate at a high installation cost.