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Connectivity has become an essential need for daily device users. With the car projected to be the “ultimate mobile device”, connectivity modules will eventually be mainstream in every car. Network providers are expanding their infrastructure and technology to accommodate the connected cars. Besides making voice and emergency calls the connected car will be sharing data with telematics service providers, back end systems and other vehicles. This trend will increase vehicle modules, complexity, entry points and vulnerabilities. This paper will present the current connected car architectures. The paper will present current architectural issues of the connected car and its vulnerabilities. The paper will present a new proposed architecture for the future connected car that enhances efficiency and security.

The substantial increase of electronic systems and processors in vehicles is increasing the already remarkable amount of software code, generating thousands of software-related recalls according to the National Highway Traffic Safety Administration (NHTSA), leading to frequent customer updates. Current software updating methods are inconvenient for customers and dealers alike, requiring a significant amount of time and expensive hardware to implement. With Wi-Fi technology and embedded modems entering vehicles, several OEMs have already taken an innovative approach with Over-The-Air (OTA) technology. OTA updating has shown to be a proven method in the telecom industry with tens of millions of phones equipped with OTA capabilities and millions of successful OTA updates performed each year, contributing to a reliable and efficient method of updating. This paper analyzes the different ways OTA is currently being used to successfully achieve in-vehicle software updates.

This paper presents an assessment of competing algorithms for normalizing volume levels between tracks and/or sources in an automotive infotainment system. Portable media players such as smartphones and iPod® devices are extremely popular for listening to music collections or streaming content from the Internet. The lack of normalization is a source of dissatisfaction if the user experiences significant changes in audio level between tracks. Several commercially available algorithms exist to solve this problem. This research includes a double-blind listening test comparing an audio sample processed with the different leveling algorithms to an unprocessed reference. The listener preference rating is recorded and results indicate which algorithm is preferred.