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An objective evaluation of ground vehicle performance is a challenging task. This is further exacerbated by the increasing level of autonomy, dynamically changing the roles and capabilities of these vehicles. In the context of decision making involving these vehicles, as the capabilities of the vehicles improve, there is a concurrent change in the preferences of the decision makers operating the vehicles that must be accounted for. Decision based methods are a natural choice when multiple conflicting attributes are present, however, most of the literature focuses on static preferences. In this paper, we provide a sequential Bayesian framework to accommodate time varying preferences. The utility function is considered a stochastic function with the shape parameters themselves being random variables. In the proposed approach, initially the shape parameters model either uncertain preferences or variation in the preferences because of the presence of multiple decision makers.

Accurate terrain models provide the chassis designer with a powerful tool to make informed design decisions early in the design process. It is beneficial to characterize the terrain as a stochastic process, allowing limitless amounts of synthetic terrain to be created from a small number of parameters. A continuous-state Markov chain is proposed as an alternative to the traditional discrete-state chain currently used in terrain modeling practice. For discrete-state chains, the profile transitions are quantized then characterized by a transition matrix (with many values). In contrast, the transition function of a continuous-state chain represents the probability density of transitioning between any two states in the continuum of terrain heights. The transition function developed in this work uses a location-scale distribution with polynomials modeling the parameters as functions of the current state.

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.