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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.

A simple and robust path follower has been developed for use in road loads simulations as required for virtual evaluation of vehicle durability and strength. The path follower is described as simple because it has a single tunable parameter and minimal control logic. It is deemed robust because the same gain setting can be used over a wide range of terrains, paths, and speeds. Path following performance is successfully demonstrated with a multibody vehicle model of a generic heavy truck for a variety of terrains, paths, and speeds. The convergence to the desired path during parameter tuning is observed to be monotonic. A physical interpretation of the gain as a maximum centripetal acceleration further enables vehicles in the same or similar classes of mobility characteristics (all off-road trucks for example) to utilize the same gain value.