In-vehicle Infotainment Research Articles

Evaluation of different interface designs for human-machine interaction in vehicles

In this paper we report on a user study in which we compared three different interaction designs in an in-vehicle infotainment system. Touchpad, free-hand and ordinary button interface designs were compared for usability, user experience and how they affect driving safety in different driving conditions. The study was performed in a high-fidelity driving simulator. Thirty participants were divided into two groups, one with less demanding and one with more demanding driving conditions, with the purpose of evaluating the interaction designs also in different driving environments. Each participant completed a set of tasks on each interface design and evaluated it with the User Experience Questionnaire. The conclusion from this study is that using a buttons-based input design on the steering wheel is the most efficient type of interaction compared to touchpad and free-hand input designs. On the other hand, we found that although newer input designs could be very attractive, their implementation into the vehicles infotainment system should be done wisely and carefully.

Security Analysis of Android Automotive

<div class="section abstract"><div class="htmlview paragraph">In-vehicle infotainment (IVI) platforms are getting increasingly connected. Besides OEM apps and services, the next generation of IVI platforms are expected to offer integration of third-party apps. Under this anticipated business model, vehicular sensor and event data can be collected and shared with selected third-party apps. To accommodate this trend, Google has been pushing towards standardization among proprietary IVI operating systems with their <i>Android Automotive</i> platform which runs natively on the vehicle’s IVI platform. Unlike <i>Android Auto</i>’s limited functionality of display-projecting certain smartphone apps to the IVI screen, Android Automotive will have access to the in-vehicle network (IVN), and will be able to read and share various vehicular sensor data with third-party apps. This increased connectivity opens new business opportunities for both the car manufacturer as well as third-party businesses, but also introduces a new attack surface on the vehicle. Therefore, Android Automotive must have a secure system architecture to prevent any potential attacks that might compromise the security and privacy of the vehicle and the driver. In particular, malicious third-party entities could remotely compromise a vehicle’s functionalities and impact the vehicle safety, causing financial and operational damage to the vehicle, as well as compromise the driver’s privacy and safety.</div><div class="htmlview paragraph">This paper presents an Android Automotive system architecture and provides guidelines for conducting a high-level security analysis. It also describes what countermeasures have already been taken by Google to prevent potential attacks, and discusses what still needs to be done in order to offer a secure and privacy-preserving Android experience for next-generation IVI platforms.</div></div>

Conceptual and Ontological Modeling of In-Vehicle Life-logging Software Systems

Abstract In-vehicle software systems that contain interconnected lifelogging components such as in-vehicle infotainment systems, smartphones, On Board Diagnostics (OBD) devices or wearable devices, operate with very large volumes of data. To manage the complexity and big volume of data, it is necessary to analyze the data and build models. Our goal, in this paper, is to build suitable models for in-vehicle lifelogging data, in the context of vehicle information standardization. To this aim, we build conceptual and ontological models for the data that is transmitted between the lifelogging components of an in-vehicle system, by applying conceptualization and abstraction mechanisms. Conceptual models can be built considering three viewpoints: functional, architectural, and behavioral. In this paper, we constructed a facet of the architectural view that contains the Unified Modeling Language (UML) classes and the relationships between them. Next, we designed and implemented the class diagram and generated each type of message in JavaScript Object Notation (JSON) format. In order to model the data and their semantics, we built an application ontology in Web Ontology Language (OWL). The consistency of this ontology was verified in Protege. The ontology was further generated in the JavaScript Object Notation for Linked Data (JSON-LD) format, which provides us with a formal format of the messages transmitted between the components of the in-vehicle system. The resulting ontology is used in the persistence layer of the in-vehicle system we are developing.

车载信息娱乐系统项目管理研究和分析

车载信息娱乐系统项目管理研究和分析

Design and Development of Integrated management module in In-Vehicle Infotainment System

Design and Development of Integrated management module in In-Vehicle Infotainment System

A Cascaded Multimodal Natural User Interface to Reduce Driver Distraction

Natural user interfaces (NUI) have been used to reduce driver distraction while using in-vehicle infotainment systems (IVIS), and multimodal interfaces have been applied to compensate for the shortcomings of a single modality in NUIs. These multimodal NUIs have variable effects on different types of driver distraction and on different stages of drivers' secondary tasks. However, current studies provide a limited understanding of NUIs. The design of multimodal NUIs is typically based on evaluation of the strengths of a single modality. Furthermore, studies of multimodal NUIs are not based on equivalent comparison conditions. To address this gap, we compared five single modalities commonly used for NUIs (touch, mid-air gesture, speech, gaze, and physical buttons located in a steering wheel) during a lane change task (LCT) to provide a more holistic view of driver distraction. Our findings suggest that the best approach is a combined cascaded multimodal interface that accounts for the characteristics of a single modality. We compared several combinations of cascaded multimodalities by considering the characteristics of each modality in the sequential phase of the command input process. Our results show that the combinations speech + button, speech + touch, and gaze + button represent the best cascaded multimodal interfaces to reduce driver distraction for IVIS.

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.

Applying a User-Centered Method to Develop 3D Gestural Inputs for In-Vehicle Tasks

Gestures are a natural input method for human communication and may be effective for drivers to interact with in-vehicle infotainment systems (IVIS). Most of the existing work on gesture-based human-computer interaction (HCI) in and outside of the vehicle focus on the distinguishability of computer systems. The purpose of this study was to identify gesture sets that are used for IVIS tasks and to compare task times across the different functions for gesturing and touchscreens. Task times for user-defined gestures were quicker than for a novel touchscreen. There were several functions that resulted in relatively intuitive gesture mappings (e.g., zooming in and zooming out on a map) and others that did not have strong mappings across participants (e.g., decreasing volume and playing the next song). The findings of this study suggest that user-centric gestures can be utilized to interact with IVIS systems instead of touchscreens, and future work should evaluate how to account for variability in intuitive gestures. Understanding the gesture variability among the end users can support the development of an in-vehicle gestural input system that is intuitive for all users.

Visual and Cognitive Demands of CarPlay, Android Auto, and Five Native Infotainment Systems.

The present research compared and contrasted the workload associated with using in-vehicle information systems commonly available in five different automotive original equipment manufacturers (OEMs) with that of CarPlay and Android Auto when used in the same vehicles. A growing trend is to provide access to portable smartphone-based systems (e.g., CarPlay and Android Auto) that support an expansion of various in-vehicle infotainment system features and functions. The study involved on-road testing of 24 participants in each configuration of five vehicles crossed with the three different infotainment systems: the embedded portion of the native OEM systems, CarPlay, and Android Auto. Our analysis found that workload was significantly greater for the embedded portion of the native OEM systems than for CarPlay and Android Auto. The strengths and weaknesses of each CarPlay and Android Auto traded off in such a way that the overall demand associated with using the two systems did not differ. CarPlay and Android Auto provided more functionality and resulted in lower levels of workload than the embedded portion of the native OEM infotainment systems. Potential applications of this research include refinements to CarPlay and Android Auto to address variations in workload as a function of task type, the modality of interaction, and OEM implementation of the system.

Tomorrow’s Infotainment

The in-vehicle infotainment experience is set to change drastically over the next few years.

차량용 인포테인먼트 시스템의 딥 러닝 기반 UI 테스팅 자동화 기술

차량용 인포테인먼트 시스템의 딥 러닝 기반 UI 테스팅 자동화 기술

User preferences and willingness to pay for in-vehicle assistance

As consumers’ demand for interconnectivity and infotainment grows continuously, car manufacturers face the challenge of developing more sophisticated, user appealing and economically viable in-vehicle infotainment assistants while staying within the boundaries of their limited resources. Based on the results extracted from an empirical study with 278 participants from Germany, this contribution supports car manufacturers to tackle this challenge by providing concrete guidance on optimal feature design, pricing, as well as initial market segmentation. Regarding the optimal feature design, we note that delivering continuously available and flawless systems with a speech input interface should be the top priority when developing such vehicular assistance. Further, we suggest that the in-vehicle infotainment assistants should be either reactive (i.e., react only to driver’s instruction) or independently proactive (i.e., exert full control without engaging the driver in decisions), but not semi-automatic (i.e., assistant issues recommendations and then follows the driver’s instructions).

IoT-Based Resource Control for In-Vehicle Infotainment Services: Design and Experimentation.

A variety of in-vehicle infotainment (IVI) devices and services have been developed by many vehicle vendors and software companies, which include navigation systems, cameras, speakers, headrest displays, and heating seat. However, there has not been enough research on how to effectively control and manage numerous IVI resources (devices and contents), so as to provide users with more enhanced services. This paper proposes a framework of resource control for IVI services so as to efficiently manage the IVI resources within an automobile. Differently from conventional IVI systems, in the proposed scheme, the IVI-Master is newly introduced for overall control of IVI resources, and IVI users are divided into owner and users. In addition, the IVI resources are classified as personal resources and shared resources, which are managed by the IVI-Master using the Lightweight Machine-to-Machine (LWM2M) standard. The proposed IoT-based IVI resource control scheme was implemented and tested. The experimental results showed that the proposed scheme can be used to effectively manage IVI resources for users. Additionally, the proposed resource control scheme shows lower bandwidth usage than the existing scheme.

Integration of Ensemble and Evolutionary Machine Learning Algorithms for Monitoring Diver Behavior Using Physiological Signals

The level of consciousness and the concentration of drivers while driving play a vital role for reducing the number of accidents. In recent decade, in-vehicle infotainment (IVI) [or in-car entertainment (ICE)] is one of the main reasons that lead to degradation of drivers performance and losing awareness. However, the impacts of some other reasons, such as drowsiness and driving fatigue, are entirely important as well. Hence, early detection of such performance degradation using different methods is a very hot research domain. To this end, the data set is collected using two different simulated driving scenarios: normal and loaded drive (17 elderly and 51 young/35 male and 33 female). This paper, therefore, concentrates on driving performance analysis using various machine learning techniques. The optimization part of the proposed methodology has two main steps. In the first step, the performances of the K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB) algorithms are improved using bagging, boosting, and voting ensemble learning techniques. Afterward, four well-known evolutionary optimization algorithms [the ant lion optimizer (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and grey wolf optimizer (GWO)] are applied to the system for optimizing the parameters and as a result enhance the performance of whole system. The GWO-voting approach has the best performance compared to other hybrid methods with the accuracy of 97.50%. The obtained outcomes showed that the proposed system can remarkably raise the performance of the classical algorithms used.

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergency-related announcements is approximately 7 s.

Design and Implementation of a Drowsiness-Fatigue-Detection System Based on Wearable Smart Glasses to Increase Road Safety

This paper proposes a drowsiness-fatigue-detection system based on wearable smart glasses to increase road safety. The proposed system is composed of a pair of wearable smart glasses, an in-vehicle infotainment telematics platform, an on-board diagnostics-II-based automotive diagnostic bridge, an active vehicle rear light alert mechanism, and a cloud-based management platform. A dedicated miniature bandpass infrared (IR) light sensor is also proposed and implemented for the low-cost, lightweight, wearable smart glasses, which can provide a higher signal-to-noise ratio than a general commercial IR light sensor, minimize the ambient environmental light image, and efficiently increase the accuracy of detection. The proposed system can detect the status of the vehicle driver with respect to drowsiness or fatigue conditions in real time. When drowsiness or fatigue is detected, the active vehicle real light alert mechanism will automatically be flickered to alert following vehicles. The related information will also be concurrently transmitted to a cloud-based management platform. As a result, the proposed system can lead to increased road safety.

In-Vehicle Infotainment 시스템에서 Configuration Protocol의 설계 및 구현

In-Vehicle Infotainment 시스템에서 Configuration Protocol의 설계 및 구현

Software Platform for Heterogeneous In-Vehicle Environments

Modern technologies lead to more sophisticated hardware, while software is becoming more complex. These trends are widely present in consumer electronics and do not bypass automotive electronics either. There is an evident recent growth in in-vehicle infotainment, telematics, advanced driver assistance systems (ADASs) and cluster development. The number of electronic control units (ECUs) in vehicle constantly grows. Since typical vehicle ECU is providing one function per vehicle, it becomes harder for manufacturers to manage these ECUs due to diverse nature of the system, hence a rising demand for ECU consolidation exists. With the availability of sophisticated hardware, powerful system-on-chips (SoCs) can be used for multiple functions inside a vehicle. The transition toward less ECUs is an ongoing process, in which software needs to be aligned first and then transferred to the same SoC. This paper presents the software platform for heterogeneous immersive in-vehicle environments, providing a step in software consolidation, by allowing same abstractions for diverse applications executing on various hardware platforms. It proposes a framework for the scalable development of ADAS from consumer level to different automotive safety levels, provides unified access toward algorithm building blocks, multi-sensor real-time environment and easy integration of algorithms, thus enabling shorter development time.

Connected Vehicles' Security from the Perspective of the In-Vehicle Network

Connected vehicles are generally equipped with many (dozens of, or even hundreds of) electronic and intelligent devices so that drivers can gain a more comfortable driving experience. Despite their numerous benefits, these technological developments have also created serious safety/security concerns. This article recapitulates the diversified attack surfaces of connected vehicles related to the technological developments from the perspective of the in-vehicle network. For each of the attacks, we discuss the rationale and the concrete methods presented in the literature. In particular, we illustrate how to launch successful attacks through the controlled area network (CAN) bus, electronic control units (ECUs), and in-vehicle infotainment system. The article also suggests some feasible solutions to the attacks demonstrated by the community. Considering the fact that vehicles are safety- critical, more practical and effective steps should be taken within the connected vehicle network toward securing connected vehicles and protecting drivers and passengers.

The impact of drowsiness on in-vehicle human-machine interaction with head-up and head-down displays

Various studies show that drowsiness reduces driver alertness and can significantly affect driver performance. In this paper, we investigate the effect of drowsiness on the interaction with the in-vehicle infotainment system (IVIS) while driving. The motivation was to investigate whether a specific type of user interface can provide better performance and lower distraction when the driver is drowsy. The users were asked to navigate a vehicle in a driving simulator and simultaneously perform a set of tasks of varying complexity first when they were rested and alert and then when they were drowsy, after 7 h without sleep. A hierarchical, list-based menu was presented using a stereoscopic head-up display (HUD) and a head-down display (HDD). Based on the results, no general and statistically significant connection was found between drowsiness and driving performance. Surprisingly, the secondary task performance was even better when participants were drowsy, which was evident from the faster task completion times. This could be attributed to extra efforts invested in executing tasks as a result of the participants being aware of their drowsiness. However, when comparing the participants’ performance using HUD and HDD displays, the results showed that using HUD introduces less mental fatigue than using HDD but only in rested and alert condition. No significant difference between the displays was found in the drowsy state.