ABSTRACT
Owing to stringent vehicle emission regulations and the shifting automotive landscape towards clean-energy vehicles, the emission of non-exhaust tire-wear particles and its implications for microplastic contamination have garnered substantial attention, emerging as a focal point of research interest. Unlike traditional source apportionment methods involving direct environmental sampling, this study focuses on the physical and chemical attributes of tire treads, the tread temperature changes, and the tire-wear particle emissions of three light-duty vehicles manufactured between 2011 and 2021. This study advances the understanding of the effects of tire properties on particle emissions, which provides preliminary information on low-wear tires. The results show that tire-wear particle emissions, mainly composed of ultrafine particles in terms of number, heavily depend on the elevated tread temperatures. The change in tread temperature is influenced not only by the initial tread temperature but also by tread pyrolysis characteristics. Ca, Mg, and Zn are abundantly contained in the tire tread and tire-wear particles.
ABSTRACT
This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.
