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Road loads tire models are used in the automotive industry in full vehicle simulations to compute the loading from the road into the chassis encountered in proving ground durability events. Such events typically include Belgian Block events, bump events, potholes and others. Correctly capturing tire enveloping forces in such events has historically been challenging - several different approaches exist each with its own limitations. In this paper a model is presented which captures the first order tire dynamics (frequencies lower than 80 Hz) and associated enveloping loading without the need of an effective road profile. The theory behind this tire model is briefly introduced. Importantly, a comprehensive study of the validation of the tire model is given which shows correlation for full vehicle dynamic proving ground events. A Virtual Tire Lab (VTL) pre-processing tool is also presented which is used to compute tire model input parameters from a validated non-linear FEA tire model.

Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.