SensingBay: an Affordable Roadside Sensing System for Student Vehicle Competitions

Numerous universities participate in student-led vehicle competitions, aiming to construct, examine, and race a student-built prototype vehicle, fostering learning and advancement in cutting-edge vehicle technologies. As electric vehicles and vehicle computing rise to prominence, modern vehicles have evolved into individualized computing hubs, equipped with sophisticated sensing, computing, and network capabilities. In response to these developments and future vehicle technology trends, there is a pressing need for roadside sensing systems in student vehicle competitions, facilitating student incorporation of emerging technologies to enhance vehicle safety and efficiency. However, acquisition of such systems poses financial hurdles. To solve this problem, this paper presents SensingBay, a ready-to-use and affordable roadside sensing system specifically designed for student vehicle competitions. SensingBay links sensor nodes via a WiFi-based mesh network to a central gateway node, which carries out more complex functions such as data analysis and user engagement. The system is constructed using Raspberry Pi, allowing the sensor nodes to be readily upgraded with a range of sensing capabilities. A prototype vehicle can interface with any sensor node to collect sensor data. The feasibility of SensingBay was validated by integrating laser distance sensors with the sensor node and employing the system in real-world conditions to collect real-time timing data for prototype vehicles. A performance evaluation of the system indicates that it has the potential to be a valuable resource in student vehicle competitions.