Augmented Situational Awareness of Traffic Management with Connected Vehicles

Incorporating connected vehicles to intelligent transport systems promise improved safety, reduced emissions, and enhanced traffic flow. In this thesis, a functional implementation of a connected vehicle utilizing lidar-based object detection and C-V2X communication is presented. The research vehicle developed for this study was capable of detecting other vehicles, converting the detections into coordinates, and transmitting this data via a V2X on-board unit to a roadside unit. An edge computing unit connected to the roadside unit enabled real-time visualization and data logging for analysis. The C-V2X connection was assessed in three scenarios: a controlled environment in Otaniemi without traffic, and in the Jätkäsaari with and without traffic. The measurements indicated that environmental and development factors have, on average, a greater impact on signal strength variance than traffic conditions. However, when large vehicles obstructed the line-of-sight between the on-board unit and the roadside unit, the signal experienced high variance. Other evaluation metrics such as latency, bandwidth or range did not get impacted by traffic conditions. The study concludes that while the implemented system shows promise for enhancing traffic management by providing real-time data and improving situational awareness, further research is needed to address limitations such as time synchronization, ego vehicles localization accuracy, and the detection of smaller road users. Additionally, the research highlights the importance of optimal installation positions for RSUs and OBUs.