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Due to the variability of real traffic conditions for vehicle testing, real-world vehicle performance estimation using simulation method become vital. Especially for heavy duty vehicles (e.g. 40 t trucks), which are used for international freight transport, real-world tests are difficult, complex and expensive. Vehicle simulations use mathematical methods or commercial software, which take given driving cycles as inputs. However, the road situations in real driving are different from the driving cycles, whose speed profiles are obtained under specific conditions. In this paper, a real-world vehicle performance estimation method using simulation was proposed, also it took traffic and real road situations into consideration, which made it possible to investigate the performance of vehicles operating on any roads and traffic conditions. The proposed approach is applicable to all kind of road vehicles, e.g. trucks, buses, etc. In the method, the real-road network includes road elevation.

Motion cueing algorithms can improve the perceived realism of a driving simulator, however, data on the effects on driver performance and simulator sickness remain scarce. Two novel motion cueing algorithms varying in concept and complexity were developed for a limited maneuvering workspace, hexapod/Stuart type motion platform. The RideCue algorithm uses a simple swing motion concept while OverTilt Track algorithm uses optimal pre-positioning to account for maneuver characteristics for coordinating tilt adjustments. An experiment was conducted on the US Army Tank Automotive Research, Development and Engineering Center (TARDEC) Ride Motion Simulator (RMS) platform comparing the two novel motion cueing algorithms to a pre-existing algorithm and a no-motion condition.

Research is currently being undertaken to develop improved suspensions for Combat Support Vehicles (CSV's). Part of this work focuses on the feasibility of using intelligent suspensions to continuously optimise the vehicles performance as the operating environment changes. For an intelligent suspension to be effective in this case, it should enable increased vehicle speed from an improvement in ride performance whilst not detracting from vehicle safety or handling performance. This paper investigates the power consumption of a CSV vehicle with a slow-active suspension. From the power consumption it is possible to estimate the extra fuel consumption and reduction in vehicle top speed. The power consumption was evaluated for a set of representative terrain profiles and vehicle speeds, demonstrating the trade off between suspension power consumption and ride performance improvement.