Vehicle Infotainment System Research Articles

A data privacy survey on personal identifiable information (PII) left on rental vehicle infotainment systems

In the current automotive ecosystem, the trend of pairing mobile devices to connected vehicles is gaining momentum, providing a vast number of benefits such as hands-free driving and remote vehicle control. However, along with these conveniences arises the issue of data accumulation, ranging from vehicle diagnostics to personal identifiable information (PII). The problem emerges when a consumer rents a vehicle, pairs their mobile device to the infotainment system, and neglects to remove their device prior to returning the vehicle. This oversight can potentially expose vulnerabilities with the current renter’s PII for subsequent renters to exploit. Research indicates that renters often overlook the deletion process prior to returning the rental vehicle and are unaware of whose responsibility it is to perform this task. In this survey, we investigated the experiences and perceptions of a group of consumers who have previously rented vehicles. We wanted to know if the participants were aware they were responsible for deleting their mobile device from the rental vehicle before returning it, the renters’ importance and tolerance for risk if they overlooked the deletion process leaving their shared data on the infotainment system for subsequent users, and if they were aware who was responsible for deleting their shared data. Lastly, we explored if the participants supported an automated solution to perform this manual deletion process. The goal of this paper is to demonstrate the significance of safeguarding PII in connected vehicles and advocate for the adoption of an automated solution to mitigate this privacy risk.

Investigating effects of font faces and line spacing in vehicle Infotainment system on driver performance including driving distractions

Investigating effects of font faces and line spacing in vehicle Infotainment system on driver performance including driving distractions

Design of Cost effective QoL Infotainment System

In vehicle infotainment (IVI) systems are becoming increasingly popular in vehicles. It is less a modern luxury and more a utility as the quality of life (QoL) of the average individual improves. The aim of this project is to attempt a more efficient design with low cost hardware that improves the Quality of Life (QoL) of the driver and passengers of a vehicle. The method of design is to use commonly available resources and appropriately interfacing them to create an infotainment system that collects, displays and stores information. The collection of information is local, the display is both local and over a cloud, and the storage is over a cloud. The data collected by the sensors could also be processed according to a manufacturer at either the microcontroller, or the phone application, and relevant information can be displayed on an attached screen and on the cloud for analysis by a manufacturer

A convolutional neural network with feature fusion for real-time hand posture recognition

Gesture based human–computer interaction is both intuitive and versatile, with diverse applications such as in smart houses, operating theaters and vehicle infotainment systems. This paper presents a novel architecture, combining a convolutional neural network (CNN) and traditional feature extractors, capable of accurate and real-time hand posture recognition. The proposed architecture is evaluated on three distinct benchmark datasets and compared with the state-of-the art convolutional neural networks. Extensive experimentation is conducted using binary, grayscale and depth data, as well as two different validation techniques. The proposed feature fusion-based convolutional neural network (FFCNN) is shown to perform better across combinations of validation techniques and image representation. The recognition rate of FFCNN on binary images is equivalent to grayscale and depth when the aspect ratio of gestures is preserved. A real-time recognition system is presented with a demonstration video.

Analysis of Vehicle Infotainment System by Security Engineering

Analysis of Vehicle Infotainment System by Security Engineering

Multi-objective optimization for hand posture recognition

Hand gestures are an intuitive way for humans to interact with computers. They are becoming increasingly popular in several applications, such as smart houses, games, vehicle infotainment systems, kitchens and operating theaters. An effective human–computer interaction system should aim at both good recognition accuracy and speed. This paper proposes a new approach for static hand gesture recognition. A benchmark database with 36 gestures is used, containing variations in scale, illumination and rotation. Several common image descriptors, such as Fourier, Zernike moments, pseudo-Zernike moments, Hu moments, complex moments and Gabor features are comprehensively compared in terms of their respective accuracy and speed. Gesture recognition is undertaken by a multilayer perceptron which has a flexible structure and fast recognition. In order to achieve improved accuracy and minimize computational cost, both the feature vector and the neural network are tuned by a multi-objective evolutionary algorithm based on the Nondominated Sorting Genetic Algorithm II (NSGA-II). The proposed method is compared with state-of-the-art methods. A real-time gesture recognition system based on the proposed descriptor is constructed and evaluated. Experimental results show a good recognition rate, using a descriptor with low computational cost and reduced size.

THE MAKING OF DECISION MAKING

Quantum Electronics was a Brazilian startup in the 1990's that was acquired by an American equity fund in 2012. They are currently the largest manufacturer of vehicle tracking and infotainment systems. The company was founded by three college friends, who are currently executives at the company: Camilo Santos, Pedro Barbosa and Luana Correa. Edward Hutter was sent by the equity fund to take over the company’s finances, but is having trouble making organizational decisions with his colleagues. As a consultant, I was called to help them improve their decision making process and project prioritization. I adapted and deployed our firm's methodology, but, in the end, its adequacy is shown to be very much in question. The author of this case study intends to explore how actual organizational decisions rely on different decision models and their assumptions, .as well as demonstrate that a decision model is neither absolutely good nor bad as its quality is context dependent.

Addressing Challenges in Automotive Connectivity: Mobile Devices, Technologies, and the Connected Car

With the dramatic mismatch between handheld consumer devices and automobiles, both in terms of product lifespan and the speed at which new features (or versions) are released, vehicle OEMs are faced with a perplexing dilemma. If the connected car is to succeed there has to be a secure and accessible method to update the software in a vehicle's infotainment system - as well as a real or perceived way to graft in new software content. The challenge has become even more evident as the industry transitions from simple analog audio systems which have traditionally served up broadcast content to a new world in which configurable and interactive Internetbased content rules the day.

Development of an automated testing system for vehicle infotainment system

A current premium vehicle is implemented with a variety of information, entertainment, and communication functions, which are generally referred as an infotainment system. During vehicle development, testing of the infotainment system at an overall level is conventionally carried out manually by an expert who can observe at a customer level. This approach has significant limitations with regard to test coverage and effectiveness due to the complexity of the system functions and human’s capability. Hence, it is highly demanded by car manufacturers for an automated infotainment testing system, which replicates a human expert encompassing relevant sensory modalities relating to control (i.e., touch) and observation (i.e., sight and sound) of the system under test. This paper describes the design, development, and evaluation of such a system that consists of simulation of vehicle network, vision-based inspection, automated navigation of features, random cranking waveform generation, sound detection, and test automation. The system developed is able to: stimulate a vehicle system across a wide variety of initialisation conditions, exercise each function, check for system responses, and record failure situations for post-testing analysis.

An intelligent infotainment dissemination scheme for heterogeneous vehicular networks

In a vehicular network, applications are expected to be aware of network conditions and be adaptable to the varying environment in order to achieve the acceptable and predictable performance. Moreover, the establishment of vehicle infotainment system in a heterogeneous vehicular network needs to pay special attention to the distinctive network characteristic in wired/wireless networks, such as dynamic topology and less available bandwidth, and the exploitation of the underutilized peers’ resources to support large-scale services. This work thus proposes a service-oriented information dissemination scheme for vehicle infotainment system to ensure the possibility of heterogeneous vehicular communication and a framework for delivering real-time services over an IP-based network is provided to assure interoperability, roaming, and end-to-end session management. The main focus of this work is to implement an adaptive vehicle infotainment system, in which cache relay nodes ( (Xiao, Zhuang, & Liu, 2005)) can be intelligently determined. Fuzzy logic inference system is employed to determine appropriate cache relay nodes to cache infotainment data streams and distribute them to different peers through service-oriented architecture. The experimental results demonstrate that effectiveness and practicability of the proposed work.

Probability based vehicle fault diagnosis: Bayesian network method

Fault diagnostics are increasingly important for ensuring vehicle safety and reliability. One of the issues in vehicle fault diagnosis is the difficulty of successful interpretation of failure symptoms to correctly diagnose the real root cause. This paper presents an innovative Bayesian Network based method for guiding off-line vehicle fault diagnosis. By using a vehicle infotainment system as a case study, a number of Bayesian diagnostic models have been established for fault cases with single and multiple symptoms. Particular considerations are given to the design of the Bayesian model structure, determination of prior probabilities of root causes, and diagnostic procedure. In order to unburden the computation, an object oriented model structure has been adopted to prevent the model from overly large. It is shown that the proposed method is capable of guiding vehicle diagnostics in a probabilistic manner. Furthermore, the method features a multiple-symptoms-orientated troubleshooting strategy, and is capable of diagnosing multiple symptoms optimally and simultaneously.