Abstract:
Every year, a massive number of deaths happen because of traffic accidents. In order to increase the traffic victim’s survival rates, it is important to reduce the arrival time of trauma intervention teams to accidents’ sites. Automatic incident detection provides faster incident reporting, in which it decreases the delay of arrival time of first responders. In this thesis, we propose a system where traffic incidents can be detected, verified and reported using multiple detection mechanisms and communication technologies to provide faster response and allow for first responders to arrive as quickly as possible. Our proposed system contains a Roadside Unit (RSU), which is responsible for inci dent detection using two automatic incident detection algorithms depending on traffic parameters collected by Inductive Loops sensors and communication technology called VANET. The proposed RSU, also, listens and receives two incident reporting signals sent by eCall and WreckWatch solutions. This thesis provides the internal architecture of the proposed RSU using Model-Based Engineering concept, where the RSU is mod eled in Architecture Analysis and Design Language (AADL). We provide an AADL model that captures the internal structure of the RSU, its components and their inter actions. We provide experiments on the model’s tasks execution using gem5 simulator depending on different configurations. We used gem5 simulation results for scheduling properties of the AADL model in order to present scheduling tests and latency analy sis. In addition, we present scheduling simulations using AADL Inspector. Test results show that our RSU model is schedulable with low processor utilization factors, and provides incident detection and reporting in under three minutes.
