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There are many situations where physical testing of a vehicle or vehicle controller is necessary, yet use of a full-size vehicle is not practical. Some situations include implementation testing of novel actuation strategies, analysing the behaviour of chassis feedback control under system faults, or nearunstable situations such as limit handling under driver-assist feedback control. Historically, many have advocated the use of scale vehicles as surrogates for larger vehicles. This article presents analysis and experimental testing that examines the fidelity of using scaled vehicles for vehicle chassis dynamics and control studies. In support of this effort, this work introduces an experimental system called the Pennsylvania State University Rolling Roadway Simulator (the PURRS). In the PURRS, a custom-built scale-sized vehicle is freely driven on a moving roadway surface. While others have used scalevehicle rolling roadway simulators in the past, this work is the first to attempt to directly match the planar dynamic performance of the scale-sized vehicle to a specific full-sized vehicle by careful design of the scale vehicle. This article explains details of this effort including vehicle dynamic modelling, detailed measurement of model parameters, conditions for dynamic similitude, validation of the resulting experimental vehicle in the time, frequency, and dimensionless domains. The results of the dynamic comparisons between scale-and full-sized vehicles clearly illustrate operational regimes where agreement is quite good, and other regimes where agreement is quite poor. Both are useful to understand the applicability of scale-vehicle results to full-size vehicle analysis.

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Rollover accidents are one of the leading causes of death in highway accidents due to their very high fatality rate. A key challenge in preventing rollover via chassis control is that the prediction of the onset of rollover can be quite difficult, especially in the presence of terrain features typical of roadway environments. These road features include superelevation of the road (e.g road bank), the median slope, and the shoulder down-slope. This work develops a vehicle rollover prediction algorithm that is based on a kinematic analysis of vehicle motion, a method that allows explicit inclusion of terrain effects. The solution approach utilizes the concept of zero-moment point (ZMP) that is typically applied to walking robot dynamics. This concept is introduced in terms of a lower-order model of vehicle roll dynamics to measure the vehicle rollover propensity, and the resulting ZMP prediction allows a direct measure of a vehicle rollover threat index. Simulation results using a complex multi-body vehicle simulation show the effectiveness of the proposed algorithm during different road geometry scenarios and driver excitations.

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