Performance indices for motorsport drivers analysis

The present paper aims to propose performance indices able to characterize the driving abilities of a car driver in the motorsport ambit. These indices could be used both to improve drivers performances and to conduct comparative analyses between professional and non-professional drivers. The data used for the analysis come from a Formula 4 vehicle and have been acquired by means of a specific data logger. Some indices, suggested by the specific literature in the motorsport vehicles, have been analyzed and employed on the data acquired on track during races. The results were not so satisfactory especially to evaluate the performance of a non-professional driver. The proposed indicators defined as the product of the accelerations along one determined direction (longitudinal or lateral) for the corresponding velocities seem to be suitable to be used as performance indices for the pilot in all the three main phases of a curve. The analysis of the data shows that these indices are quite reliable even if, in some particular cases, they show little discrepancies. This happens because the indices must be interpreted differently in dependence of the various types of curve, which are diversely approached (e.g. a chicane or a hairpin). Further development will improve the indicators according to the type of curve, trying to give an overall performance indicator for each curve

Ambient intelligence in systems to support wellbeing of drivers

The possibilities of ambient intelligence in the healthcare sector are multifaceted, ranging from supporting physical to mental wellbeing in various ways. Ambient intelligence can play an important role in supporting emotional wellbeing and reducing discomfort. Real-time capability in systems to provide support during discomfort can be useful in scenarios which are traditionally neglected. Absence of concern about wellbeing among commercial vehicle drivers during stressful driving situations may lead to accidents and poor lifestyle. Ambient intelligence can play a role in determining such situations to support the drivers when it is required. The availability of low-cost Internet of Thing (IoT ) based components has opened up opportunities in areas where resources are constrained. In the current chapter, the focus is on improving the wellbeing of commercial vehicle drivers in a low-income setting. The chapter focuses on understanding the concepts of discomfort and wellbeing through a detailed qualitative study followed by a possible solution approach to address the ongoing challenges. A low-cost wearable IoT-enabled system along with a long-term analytic support is proposed to improve the wellbeing of drivers using ambient intelligence. The entire system is built up using a connectivity framework. The low-cost IoT device would enable support for discomfort for community who traditionally do not receive such support. Wellbeing of drivers is important for improved driving quality and better traffic management . A system in place to support drivers in real time, named Bap re Bap is presented here in the context of Bangladesh

Performance Comparison of Heterogeneity Measures for Count Data Models in Bayesian Perspective

Random effects model is one of the widely used statistical techniques in combining information from multiple independent studies and examine the heterogeneity. The present study has focussed on count data model which is comparatively uncommon in such research studies. Also the interest is to exploit the advantage of Bayesian modelling by incorporating plausible prior distributions on the parameter of interest. The study is illustrated with a data on rental bikes obtained from UC Irvine Machine Learning Repository. Results have indicated the impact of prior distributions and usage of heterogeneity estimators in count data models