In-car Infotainment Research Articles

Understanding the EV Semiconductor Chip Sustainable Supply Chain Chip Shortage

Semiconductor chips play a pivotal role in modern technology, with their significance heightened by the growing electric vehicle (EV) industry. As vital components powering electric powertrains, advanced driver-assistance systems (ADAS), and in-car infotainment, semiconductor chips have become the linchpin of the automotive industry's transformation. The escalating global demand for EVs has, however, triggered a semiconductor chip shortage, leading to systemic disruptions in the supply chain. This study adopts a comprehensive approach to understanding the repercussions of the chip shortage within the automotive sector. Our method involves examining news articles about the semiconductor shortage, which worsened due to the COVID-19 pandemic. From this examination, a framework is created that explains the reasons for the shortage and its consequences. Through these analytical frameworks, the paper draws essential lessons and implications regarding the initiation, propagation, and enduring nature of systemic disruptions. The findings provide valuable insights into the complexities of supply chain dynamics, offering an in-depth understanding of how disruptions manifest and persist over time. As semiconductor shortages continue to affect various industries, including the automotive sector, these insights contribute to the ongoing discourse on the sustainable management of semiconductor chip supply chains. This paper aims to delve into the intricacies of the EV semiconductor chip sustainable supply chain, shedding light on the factors contributing to the chip shortage and exploring potential solutions for a resilient and sustainable future.

The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention

A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behaviors and activities. While these algorithms work well in a controlled environment, naturalistic driving conditions add new challenges such as illumination variations, occlusions and extreme head poses. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks to effectively monitor driver actions and behaviors. Toward building the required infrastructure, this paper presents the multimodal driver monitoring (MDM) dataset, which was collected with 59 subjects that were recorded performing various tasks. We use the Fi- Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions. We ask the driver to look at predetermined gaze locations to obtain accurate correlation between the driver's facial image and visual attention. We also collect data when the driver performs common secondary activities such as navigation using a smart phone and operating the in-car infotainment system. All of the driver's activities are recorded with high definition RGB cameras and time-of-flight depth camera. We also record the controller area network-bus (CAN-Bus), extracting important information. These high quality recordings serve as the ideal resource to train various efficient algorithms for monitoring the driver, providing further advancements in the field of in-vehicle safety systems.

Power Control Identification: A Novel Sybil Attack Detection Scheme in VANETs Using RSSI

Vehicular <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> networks (VANETs) have far-reaching application potentials in the intelligent transportation system (ITS) such as traffic management, accident avoidance and in-car infotainment. However, security has always been a challenge to VANETs, which may cause severe harm to the ITS. Sybil attack is considered as a serious security threat to VANETs since the adversary can disseminate false messages with multiple forged identities to attack various applications in the ITS. RSSI-based Sybil nodes detection is an efficient scheme against Sybil attacks, which adopts position estimation, distribution verification or similarity comparison to identify Sybil nodes. However, when Sybil nodes conduct power control to deliberately change transmission powers, the received RSSI values would change correspondingly, which leads to inaccurate localization or different RSSI time series of these Sybil nodes. Thus, it is very difficult to differentiate Sybil nodes from normal nodes via conventional RSSI-based methods. This paper first discusses potential power control models (PCMs) for launching Sybil attacks in VANETs, then presents two simple Sybil attack models and three sophisticated Sybil attack ones with or without power control in detail, finally proposes a power control identification Sybil attack detection (PCISAD) scheme to find anomalous variations in RSSI time series, which are then used to identify Sybil nodes via a linear SVM classifier. Extensive simulations and real-world experiments prove that the proposed scheme can effectively deal with Sybil attacks with power control.

Automatic music genre recognition for in-car infotainment

Abstract Automatic music genre recognition is fundamental tool for music retrieval, recommendation and personalisation in smart infotainment systems and music streaming services. Such systems may be helpful especially for in-car audio, because driver’s interaction with such infotainment systems could become a major subject of his/her distraction. There are two important tasks to be considered for better genre classification, which present classifier and audio features extraction. In the proposed system, timbral textural and pitch content features were used for genre classification. Timbral texture includes the Mel-Frequency Cepstral Coefficients (MFCC) along with other spectral characteristics. For the pitch content the features extracted from Chroma are selected. The aim of this work is to explore possibilities of music genres classification from audio signal and to create a system for automatic recognition of music genres in the MATLAB programming environment. A functional system for recognition of music genres was developed on the GTZAN data set with ten different musical genres such as rock, pop, classical etc. We examined several classification methods including GMM, SVM, and k-NN. The experimental results show that by using both types of features, the classification accuracy of 69.7% is achieved for the k-NN classifier.

Gesture Recognition Using mm-Wave Sensor for Human-Car Interface

This article details the development of a gesture recognition technique using a mm-wave radar sensor for in-car infotainment control. Gesture recognition is becoming a more prominent form of human-computer interaction and can be used in the automotive industry to provide a safe and intuitive control interface that will limit driver distraction. We use a 60 GHz mm-wave radar sensor to detect precise features of fine motion. Specific gesture features are extracted and used to build a machine learning engine that can perform real-time gesture recognition. This article discusses the user requirements and in-car environmental constraints that influenced design decisions. Accuracy results of the technique are presented, and recommendations for further research and improvements are made.

Domain-specific language models training methodology for the in-car infotainment

The proposed paper focuses on the methodology for training small domain-specific language models. The methodology has been applied for creating a language model for the demonstration version of the in-vehicle infotainment system speech interface. The proposed methodology is based on constructing an initial n-gram language model only from the vocabulary. Such “random” model can be easily adapted to the user’s speaking style. The methodology is well suitable for situations when constructing the accurate deterministic grammar is not a trivial task or the higher flexibility is required. In the case of in-vehicle infotainment, the key idea is to decrease the cognitive load of the driver during controlling such a system and also to make designing process less time consuming. Evaluation of the proposed approach was done on the INCARSCOM corpus (Slovak In-car Command Database). Obtained results favour designed method instead of constructing the appropriate deterministic grammar.

On the performance of WLAN and Bluetooth for in-car infotainment systems

The connected car is ushering in a new era of automotive design. Driven by increasing customer demand for connectivity and advances in electronics, connected cars are now equipped with advanced infotainment systems with a variety of applications. Seamless integration of consumer electronic (CE) devices into car infotainment systems is crucial for mimicking home and office user experience. Because wireless communication is more user-friendly than wired communication, it has become the preferred method for connecting CE devices to car infotainment systems. WLAN1and Bluetooth2are the most promising technologies for this purpose. Both systems operate in the spectrum-scarce 2.4 GHz unlicensed industrial, scientific and medical (ISM) radio band. The coexistence between WLAN and Bluetooth has garnered a significant amount of attention from both academic and industry researchers. However, the unique features of vehicle mobility and the high density of devices in a limited roadway area necessitate further investigation in the automotive domain.This paper focuses on the coexistence between WLAN and Bluetooth systems among vehicle infotainment applications, and on WLAN co-channel interference. Performance is evaluated using experimental measurements in real-world scenarios. The mobility effect is studied in detail. Results show that an onboard WLAN network is strongly affected by the surrounding networks. Coexistence duration decreases exponentially with relative speed between automobile networks. WLAN effect on Bluetooth is extremely high when WLAN's non-overlapped channels 1, 6, and 11 are simultaneously occupied. WLAN interference leads to a significant number of clippings in Bluetooth audio signals, especially in high WLAN traffic load situations. An exponential decease in the number of clipping events as a function of speed is observed.

Semantic distance as a critical factor in icon design for in-car infotainment systems

In-car infotainment systems require icons that enable fluent cognitive information processing and safe interaction while driving. An important issue is how to find an optimised set of icons for different functions in terms of semantic distance. In an optimised icon set, every icon needs to be semantically as close as possible to the function it visually represents and semantically as far as possible from the other functions represented concurrently. In three experiments (N = 21 each), semantic distances of 19 icons to four menu functions were studied with preference rankings, verbal protocols, and the primed product comparisons method. The results show that the primed product comparisons method can be efficiently utilised for finding an optimised set of icons for time-critical applications out of a larger set of icons. The findings indicate the benefits of the novel methodological perspective into the icon design for safety-critical contexts in general.

Self-Sustaining Caching Stations: Toward Cost-Effective 5G-Enabled Vehicular Networks

In this article, we investigate the cost-effective 5G-enabled vehicular networks to support emerging vehicular applications, such as autonomous driving, in-car infotainment and location-based road services. To this end, self-sustaining caching stations (SCSs) are introduced to liberate on-road base stations from the constraints of power lines and wired backhauls. Specifically, the cache-enabled SCSs are powered by renewable energy and connected to core networks through wireless backhauls, which can realize "drop-and-play" deployment, green operation, and low-latency services. With SCSs integrated, a 5G-enabled heterogeneous vehicular networking architecture is further proposed, where SCSs are deployed along roadside for traffic offloading while conventional macro base stations (MBSs) provide ubiquitous coverage to vehicles. In addition, a hierarchical network management framework is designed to deal with high dynamics in vehicular traffic and renewable energy, where content caching, energy management and traffic steering are jointly investigated to optimize the service capability of SCSs with balanced power demand and supply in different time scales. Case studies are provided to illustrate SCS deployment and operation designs, and some open research issues are also discussed.

Modeling context-aware and intention-aware in-car infotainment systems

It is fundamental to understand users' intentions to support them when operating a computer system with a dynamically varying set of functions, e.g., within an in-car infotainment system. The system needs to have sufficient information about its own and the user's context to predict those intentions. Although the development of current in-car infotainment systems is already model-based, explicitly gathering and modeling contextual information and user intentions is currently not supported. However, manually creating software that understands the current context and predicts user intentions is complex, error-prone and expensive. Model-based development can help in overcoming these issues. In this paper, we present an approach for modeling a user's intention based on Bayesian networks. We support developers of in-car infotainment systems by providing means to model possible user intentions according to the current context. We further allow modeling of user preferences and show how the modeled intentions may change during run-time as a result of the user's behavior. We demonstrate feasibility of our approach using an industrial case study of an intention-aware in-car infotainment system. Finally, we show how modeling of contextual information and modeling user intentions can be combined by using model transformation.

“You Can Do It Baby”: Non-Task Talk with an In-Car Speech Enabled System

Natural language speech enabled systems are an attractive option for in-car infotainment. Differences in cultural expectations in communication, however, can pose difficulties for interface developers and cause frustration for users. We adopt the perspective of cultural discourse theory to analyze 26 drivers interacting with an in-car speech interface. We focus here on directive sequences and the phenomenon of participants using non-task talk. The analysis of these sequences reveals a norm that one ought to engage in non-task talk with the system. We suggest that this norm is grounded in a user premise that the system’s interactional status involves the ability to speak. We find that this norm lacks crystallization among participants, and we formulate a competing norm that helps to account for this. The second norm reveals an underlying belief that the system’s status as a machine is the basis for how it should be treated.

Sonification of in-vehicle interface reduces gaze movements under dual-task condition

In-car infotainment systems (ICIS) often degrade driving performances since they divert the driver's gaze from the driving scene. Sonification of hierarchical menus (such as those found in most ICIS) is examined in this paper as one possible solution to reduce gaze movements towards the visual display. In a dual-task experiment in the laboratory, 46 participants were requested to prioritize a primary task (a continuous target detection task) and to simultaneously navigate in a realistic mock-up of an ICIS, either sonified or not. Results indicated that sonification significantly increased the time spent looking at the primary task, and significantly decreased the number and the duration of gaze saccades towards the ICIS. In other words, the sonified ICIS could be used nearly exclusively by ear. On the other hand, the reaction times in the primary task were increased in both silent and sonified conditions. This study suggests that sonification of secondary tasks while driving could improve the driver's visual attention of the driving scene.

Designing for Frustration and Disputes in the Family Car

Families spend an increasing amount of time in the car carrying out a number of activities including driving to work, caring for children and co-ordinating drop-offs and pick ups. While families travelling in cars may face stress from difficult road conditions, they are also likely to be frustrated by coordinating a number of activities and resolving disputes within the confined space of car. A rising number of in-car infotainment and driver-assistance systems aim to help reduce the stress from outside the vehicle and improve the experience of driving but may fail to address sources of stress from within the car. From ethnographic studies of family car journeys, the authors examine the work of parents in managing multiple stresses while driving, along with the challenges of distractions from media use and disputes in the car. Keeping these family extracts as a focus for analysis, we draw out some design considerations that help build on the observations from our empirical work.

Glance behaviours when using an in-vehicle smart driving aid: A real-world, on-road driving study

In-vehicle information systems (IVIS) are commonplace in modern vehicles, from the initial satellite navigation and in-car infotainment systems, to the more recent driving related Smartphone applications. Investigating how drivers interact with such systems when driving is key to understanding what factors need to be considered in order to minimise distraction and workload issues while maintaining the benefits they provide. This study investigates the glance behaviours of drivers, assessed from video data, when using a smart driving Smartphone application (providing both eco-driving and safety feedback in real-time) in an on-road study over an extended period of time. Findings presented in this paper show that using the in-vehicle smart driving aid during real-world driving resulted in the drivers spending an average of 4.3% of their time looking at the system, at an average of 0.43s per glance, with no glances of greater than 2s, and accounting for 11.3% of the total glances made. This allocation of visual resource could be considered to be taken from ‘spare’ glances, defined by this study as to the road, but off-centre. Importantly glances to the mirrors, driving equipment and to the centre of the road did not reduce with the introduction of the IVIS in comparison to a control condition. In conclusion an ergonomically designed in-vehicle smart driving system providing feedback to the driver via an integrated and adaptive interface does not lead to visual distraction, with the task being integrated into normal driving.

Spectrum-aware Beaconless Geographical Routing Protocol for Cognitive Radio Enabled Vehicular Networks

The FCC and ETSI have allocated spectrum in the 5.9 GHz band for intelligent transportation systems. However, this spectrum supports short-range transmissions (up to 1000 m) and limited bandwidth (up to 75 MHz), which are not enough to meet the increasing demand for in-car infotainment services. In this paper, we propose a distributed routing protocol for vehicular ad hoc networks, where cognitive radio enabled vehicles (CRVs) dynamically share the TV-band channels. In the proposed protocol, CRVs jointly select relay nodes, channels, transmission powers, and transmission rates so that their total transmission rates are maximized while meeting their rate demands and power constraints. This selection process is carefully executed so that ongoing communications between primary radios (PRs) and between other CRVs are not disrupted. Once the relay nodes are selected, they continue to relay more messages as long as they stay in a predefined forwarding area. By doing so, the overhead for selecting relay nodes can be substantially reduced. Channels, powers, and rates are changed on a per-packet and per-hop basis so that the proposed protocol can efficiently adapt to spectrum dynamics. Simulation results show that our protocol increases the end-to-end network throughput by up to 250 % and decreases the end-to-end delay by up to 400 % compared with other geographical routing protocols.

AFFECTIVE-SOUND EFFECTS ON DRIVING BEHAVIOUR

Recent technological developments in active advanced driver assistance systems and in-car infotainment devices have contributed to reducing the number and severity of road accidents as well as improving and simplifying driver experience. However, these systems may impact driving performance in undesired ways, especially when emotionally-charged stimuli are used as warning signals. Emotional distraction can be a serious danger, causing delays in information processing, and reducing driving safety below minimal acceptable levels. Here we study the effect of emotionally-laden auditory signals on the speed of concurrent driving decisions. We distinguished two categories of behavioural responses: ‘urgent’ vs ‘evaluative’. In the experiments reported here participants were quicker to evaluate whether a traffic scene was risky or not after hearing an emotionally-charged auditory stimulus than after a neutral one. However, urgent (braking) responses to the same scenes were not affected by the emotional quality of the auditory signal. Based on these results, we give preliminary advice on the design of guidelines for in-car interfaces particularly in the field of affective in-car computing.

Evaluating User Response to In-Car Haptic Feedback Touchscreens Using the Lane Change Test

Touchscreen interfaces are widely used in modern technology, from mobile devices to in-car infotainment systems. However, touchscreens impose significant visual workload demands on the user which have safety implications for use in cars. Previous studies indicate that the application of haptic feedback can improve both performance of and affective response to user interfaces. This paper reports on and extends the findings of a 2009 study conducted to evaluate the effects of different combinations of touchscreen visual, audible, and haptic feedback on driving and task performance, affective response, and subjective workload; the initial findings of which were originally published in (M. J. Pitts et al., 2009). A total of 48 non-expert users completed the study. A dual-task approach was applied, using the Lane Change Test as the driving task and realistic automotive use case touchscreen tasks. Results indicated that, while feedback type had no effect on driving or task performance, preference was expressed for multimodal feedback over visual alone. Issues relating to workload and cross-modal interaction were also identified.

There goes the recovery

Analysis of the economic implications of the Japanese earthquake has focused on supply side economics and with good reason. There are plenty of concerns about shortages of electronic components and raw materials used by high-tech industries. Automotive OEMs worldwide are closing because they cannot source chips used to monitor airflow and drive In-car Infotainment systems. Japan's Importance in producing waters for semiconductor manufacturing the shuttered Shin-Etsu plant in Nishigo Village, northern Japan, normally supplies 20 per cent of the entire industry's requirements and materials for printed circuit boards may affect all products that integrate electronics. However, as time passes, attention is also encompassing demand. Shortly after disaster struck, Apple put the Japanese launch of the IPad 2 on indefinite hold. Coupling this to concerns about securing parts for the tablet, financial markets disregarded Its glowing reviews and marked down Apple's shares by more than 5 per cent.

A scalable architecture for multilingual speech recognition on embedded devices

In-car infotainment and navigation devices are typical examples where speech based interfaces are successfully applied. While classical applications are monolingual, such as voice commands or monolingual destination input, the trend goes towards multilingual applications. Examples are music player control or multilingual destination input. As soon as more languages are considered the training and decoding complexity of the speech recognizer increases. For large multilingual systems, some kind of parameter tying is needed to keep the decoding task feasible on embedded systems with limited resources. A traditional technique for this is to use a semi-continuous Hidden Markov Model as the acoustic model. The monolingual codebook on which such a system relies is not appropriate for multilingual recognition. We introduce Multilingual Weighted Codebooks that give good results with low decoding complexity. These codebooks depend on the actual language combination and increase the training complexity. Therefore an algorithm is needed that can reduce the training complexity. Our first proposal are mathematically motivated projections between Hidden Markov Models defined in Gaussian spaces. Although theoretically optimal, these projections were difficult to employ directly in speech decoders. We found approximated projections to be most effective for practical application, giving good performance without requiring major modifications to the common speech recognizer architecture. With a combination of the Multilingual Weighted Codebooks and Gaussian Mixture Model projections we create an efficient and scalable architecture for non-native speech recognition. Our new architecture offers a solution to the combinatoric problems of training and decoding for multiple languages. It builds new multilingual systems in only 0.002% of the time of a traditional HMM training, and achieves comparable performance on foreign languages.

SWICOM: An SDR-Based Wireless Communication Gateway for Vehicles

A wide range of emerging and promising wireless communication protocols are rapidly being introduced into vehicles. They are commonly used for in-car infotainment, telematics, and safety applications. However, adopting new wireless communications into vehicles requires them to be equipped with the corresponding hardware devices. This hardware dependency incurs extra costs to customers to deploy and maintain wireless services in vehicles. To alleviate this problem, this paper proposes a novel wireless communication gateway for vehicles that is called the software-defined radio (SDR)-based wireless communication gateway (SWICOM). It exploits the SDR technology that uses software running on a generic hardware platform to perform signal processing instead of dedicated hardware. The SWICOM can thus integrate multiple wireless hardware devices into a single generic wireless gateway device, which improves flexibility, adaptability, and connectivity of wireless communications. We built its prototype implementation and performed measurements to quantify its run-time performance. The worst-case execution time (WCET) analysis is also given using synchronous data flow (SDF) graphs. All entire results clearly show the viability of the SWICOM.