Integration of an adaptive infotainment system in a vehicle and validation in real driving scenarios

More services, functionalities, and interfaces are increasingly being incorporated into current vehicles and may overload the driver capacity to perform primary driving tasks adequately. For this reason, a strategy for easing driver interaction with the infotainment system must be defined, and a good balance between road safety and driver experience must also be achieved. An adaptive Human Machine Interface (HMI) that manages the presentation of information and restricts drivers’ interaction in accordance with the driving complexity was designed and evaluated. For this purpose, the driving complexity value employed as a reference was computed by a predictive model, and the adaptive interface was designed following a set of proposed HMI principles. The system was validated performing acceptance and usability tests in real driving scenarios. Results showed the system performs well in real driving scenarios. Also, positive feedbacks were received from participants endorsing the benefits of integrating this kind of system as regards driving experience and road safety.Postprint (published version

Next-Generation Smart Cars: Towards a More Intelligent Interactive Infotainment System

abstract: Today, in a world of automation, the impact of Artificial Intelligence can be seen in every aspect of our lives. Starting from smart homes to self-driving cars everything is run using intelligent, adaptive technologies. In this thesis, an attempt is made to analyze the correlation between driving quality and its impact on the use of car infotainment system and vice versa and hence the driver distraction. Various internal and external driving factors have been identified to understand the dependency and seriousness of driver distraction caused due to the car infotainment system. We have seen a number UI/UX changes, speech recognition advancements in cars to reduce distraction. But reducing the number of casualties on road is still a persisting problem in hand as the cognitive load on the driver is considered to be one of the primary reasons for distractions leading to casualties. In this research, a pathway has been provided to move towards building an artificially intelligent, adaptive and interactive infotainment which is trained to behave differently by analyzing the driving quality without the intervention of the driver. The aim is to not only shift focus of the driver from screen to street view, but to also change the inherent behavior of the infotainment system based on the driving statistics at that point in time without the need for driver intervention.Dissertation/ThesisMasters Thesis Software Engineering 201

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars