Safety Diagnostics and Degraded Operational Modes

Abstract

Unmanned ground combat vehicles (UGCV) promise numerous ultimate military, civilian, and space applications. The focus of this research mainly deals with how a non-geometric hazard scenario may potentially lead to a mission ending situation and a non geometric hazard can be considered as any terrain feature or object that is adjudged to be non-traversable by virtue of its physical properties. The novelty of this research lies in how a UGCV in a military applications can overcome the challenges of traversing through ever changing natural obstacles when compared to the reinforcing obstacles which are encountered by autonomous vehicle in a conventional structured scenario (see Figure 1).Figure 1. Traversal across an off-road terrain facing uneven terrain and obstacles.An operational design domain (ODD) can help specify potential unsafe situations and restrict the vehicle’s operation within them. A complete ODD will guarantee that the safety arguments can be dealt in a streamlined manner and in scenarios of restricting the ODD, the overall availability of the system gets reduced. With a semantic ODD structure for the UGVC, the standard development of the safety process for an automotive system is achieved and with the already created safety protocols, the overall situation space is reduced during critical situations.DISTRIBUTION A. Approved for public release; distribution unlimited. OPSEC5045The process of identifying reduced operational domain (ROD) starts with the quantificationand analysis of maneuvers and further leads to the identification of critical situations. Criticalareas are conditions of the environment that cause unintentional behaviour which eventuallyleads to an accident. A state machine constituting nominal behaviour will form the basis for acomponent fault tree (CFT) which is used to identify conditions that cause critical situations.Subsequently, the corresponding risk of an identified critical situation is assessed as a part ofthis process.The safety diagnostics mainly depends on a probabilistic model-based controller examining adynamic environment in which the stochastic evolution depends on the input of observationsand the current behaviour of the UGCV. In order to model the real-life performance of safetycritical systems realistically and accurately, Markov chain and Bayesian filters are highlyuseful. The finite state essence of the discrete controller may possibly lead to incorrectbehaviour of the complete system if an unforeseen situation occurs and for which there is alack of any predefined contingency. For this purpose, it becomes important to have a sense ofa complete set of admissible scenarios and also to develop a structured decision-making processfor each of the previously mentioned scenarios. State machine and failure propagation treescan help in determining the failure probabilities which keep updating based on changes incircumstances and this would help define if a mission should continue. As a part of thisresearch, Markov decision process which forms the basis for decision making process isemployed to identify and compare a set of state sequences and this in turn would help inrealizing better maneuverability of the vehicle.The assignment of ROD can be accomplished with the complete risk assessment of the criticalsituations. By assigning the ROD, the aim of increasing the overall availability of the systemwhich degraded from the nominal driving behaviour is fulfilled and this would allow for thesafe operation of UGCV.