Analysis of truck crash data shows that the majority of truck occupant fatalities occur as a result of rollover or frontal collisions. A large proportion results from single-vehicle crashes and about one third of fatal crashes involve ejection of the truck driver from the cab. Stronger cab structures to provide adequate occupant survival space, the use of stronger doors and side inflatable tubular structures to prevent ejection, more forgiving interior surfaces, air bags, and seat belts are all possible means of reducing occupant injury. This paper provides a status report on a current effort to mitigate crash injury to large truck occupants. It presents a detailed survey of the current state-of-the-art in occupant protection countermeasures and their effectiveness, an analysis of U.S. truck crash data with an overview of occupant injury modes, and concludes with a description of a current effort intended to quantitatively estimate the benefits of implementing these countermeasures for the U.S. road system. For the covering abstract see ITRD E825082.

A method of determining the model order of MIMO nonlinear dynamic systems was proposed using the concept of the coherence function. It was demonstrated that the proposed scheme is extremely successful even in the presence of noise, clearly outperforms the Lipschitz criterion scheme in determining the model order accurately and unambiguously and identifies the order correctly even in the case when the Lipschitz scheme produces an erroneous result. The effectiveness of the order determination scheme in conjunction with the NARMAX identification technique, for identifying a severely nonlinear system such as an automobile exhaust gas oxygen (EGO) sensor was also studied. It was shown that the proposed scheme is suitable for practical implementation using simulation results

Modeling of automotive subsystems, such as the engine, vehicle dynamics etc., is an area that has received much attention for a number of years. It is well recognized that most of these systems are inherently complex and highly non-linear, a number of different approaches have been take to the construction of automotive engine models including (i) first principle or physical understanding based modeling and (ii) input-output system identification. The former method often produces models that quite accurately represent engine behavior but are very detailed and difficult to use either in the design stage for construction of control and monitoring algorithms or in the implementation of algorithms. System identification on the other hand results in simpler models, but due to the relative paucity of nonlinear techniques the identified models often do not adequately model engine behavior. This paper presents the application of a nonlinear system identification technique, called the NARMAX scheme, to the modeling of the dynamics of an internal combustion engine. The models are designed to model engine behavior over a driving schedule, referred to as the IM240 cycle, mandated by the US EPA for emissions testing. The local stability of the constructed models is verified for the operating conditions of the cycle. Further, the conditions for a NARMAX model to be invertible are presented and the IC engine models are analyzed for invertibility. It is also demonstrated that the inverse models can be directly identified from the input-output data.