Modeling communication and estimation processes of automated crash avoidance systems

Abstract

We present a novel approach to modeling the combined estimation and networking processes of automated crash/collision avoidance systems (ACAS). The estimation and networking processes are two necessary components of the real-time situation awareness component of the system. The existing models for these two components are mostly based on stochastic modeling methods, describing each component separately and in abstract probabilistic terms. Such modeling methods lead to the loss of useful details. In our recent work we presented extended stochastic models using discrete-time Markov chains for the networking component and empirical statistical models for the estimation process. Although these models led to significantly improved designs for the situation awareness component of ACAS, it was observed that the extent of the improvement was limited. The limitation is due the fact that stochastic models are limited in describing the system which inherently has many deterministic features. In this paper we attempt to advance the approach to modeling the ACAS systems (and other similar systems) through developing a method to model the communication component based on Probabilistic Timed automata and also a Hybrid automata to combine and model the entire system (both estimation and communication/networking processes). This paper presents the new model and verifies it using simulations.