Researchers indicated that using a smartphone for text messaging while maneuvering a vehicle can impair driving performance and lead to road accidents [23]. Increasing the usage of a smartphone while driving leads to an increase the traffic accidents [24]. For example, the research found that distraction-related fatalities increased from 10.9% in 1999 to 15.8% in 2008 due to increased texting while driving [7]. The risk postured by text messaging while driving has attracted considerable research attention among legislators, automakers, safety researchers, and developers to come up with distracted-free solutions [23]. On one side, text messaging is a dangerous activity while driving, and on the other side, it lacks context-awareness that automatically detects the context and responds accordingly.
The term context-aware computing was first coined by Schilit et al. in 1964 [25]. Since then, it has been popular and used continuously by researchers. According to Zimmermann et al. [26], the term “context” can include the location, time, and temperature. An-other researcher [27] defines the context as the location of the user’s, who they are with, the time of the day, the season of the year, and the temperature. Schilit describes the con-text-sensitive systems that are aware of and adapt the location of use, detecting nearby objects and people according to time [28]. Similarly, Ryan et al. [29] define context as location, temperature, time, and user identity. Some researchers [28, 29] also described the context as the computing environment or the environment that the computers know about [30, 31]. In this regard, phone context was particularly used to allow users to define personal con-text rules (e.g., switching to meeting mode when the phone lies still or face down) [31].
Researchers have investigated several ways to sense contextual and emotional information in instant messaging applications [32]. Recently wearable and physiological sensors have been utilized to uplift the effect of context-awareness in text messaging. Researchers have used various smartphone, environmental, and on-body sensors to sense different contexts such as location, temperature, and activities [32–34]. Similarly, the researchers have used text analysis to sense the context and emotions in chatting applications. For example, [35–37], have used text analysis to surmise the mood in instant messaging applications. Some researchers [38–40] have used facial recognition in text messaging applications to communicate in-chat emotional states via avatars and images. Furthermore, for contexts (i.e., location, temperature, and activities) sensing and detecting, the researchers have used various smartphone sensors, environmental sensors, and a combination of on-body sensors. For example, ConChat [41], a context-sensitive chat application, captures and integrates information from the environment using embedded sensors, such as several people in the room, room temperature, and currently running application. Hong et al. [42] used four different sensors: accelerometer sensor, physiological sensors, GPS, and smartphone sensors to extract and analyzed emotions, location, stress, weather, movement, and time of the day using dynamic Bayesian network in the ConaMSN messenger. However, these solutions suffer from a lack of real-time contexts and adaptation to minimize the drivers physical, visual, and mental engagements. There is a dire need to make the interaction simplified and automatic using an adaptive user interface paradigm.
2.1. Adaptive User Interfaces
The adaptive user interfaces use a context-awareness approach and generate new interfaces according to changes in environment, user preferences, and device usage [43]. This approach will help drivers personalize their smartphone user interfaces irrespective of their visual, physical, and cognitive limitations. ICCS [44, 45], an in-car communication system intended to minimize driver distraction when drivers are engaging with their cell phones with the help of speech input and output. However, this system is not widely adopted because of not using the vehicle contextual information for generating automatic UI. The researchers from different domains have emphasized adaptive user interfaces and have designed easy-to-use, user-friendly, and accessible interfaces according to the HCI guidelines to solve real-world problems in the different domains [46]. For example, a system called “Supple System” [47] generates user interfaces for the users based on their tasks, preferences, and cognitive abilities. The findings have shown that novice users can complete a complex task in less than 20 minutes using the proposed user interface. A multipath user interface system is developed, using XML to generate user interfaces based on current contexts [48]. The Egoki system is a user interface generator system designed for people with disabilities [49]. The purpose of the system was to recommend appropriate user interfaces to select multimedia content to the users based on their needs. The MARIA system proposed a model-based user interface description language to automatically generate user interfaces and customize for the different devices in run time [50]. The ODESEeW system is a semantic web portal that automatically uses the WebODE platform and an ontology application to generate a knowledge portal of interests [51]. For example, it generates different menus based on the users’ interests and adjusts the visibility of contents according to the users’ needs.
A generic interface infrastructure has been presented in the MyUI system, aiming to increase accessibility through an adaptive user interface [52]. The MyUI provides a run-time adaptation to user preferences, device usages, and work conditions. An XML-based pervasive multimodal user interface framework is proposed, which helps the designer to design a wide range of platforms that support multi-languages [53]. The aim was mainly on how to change the mono-modal web-oriented environment of a simplified interface for a variety of platforms. The ViMoS system has proposed a context-aware framework to provide information about adapted embedded in the user devices according to the environment [54]. The system is composed of a set of available widgets to render different data patterns on various visualization techniques to adapt and customize visual layouts in the available area. In this research work, we have used the context-awareness and the adaptation approach to simplify the interaction between driver and smartphone.
2.2. Assistive Technologies
Different assistive technologies (as shown in Table 1) have been used to reduce distractions. The main aim of these technologies is to simplify smartphone functionalities [1, 20]. This approach is aimed to reduce visual interactions by simplifying driver interactions with smartphone applications. For example, a smartphone-based system, namely Safe Driving App: Drivemode, provides a simplified yet effective interface to smartphone usages and minimizes visual and motor demands by providing shortcuts to the apps and using voice commands for interactions. Following the idea, several smartphone systems are developed to support safe interaction between driver and mobile phones, such as Car Dashdroid, HereWeGo, Microsoft Cortana, Google Assistant, Siri, AutoMate, and Waze [67]. The voice commands-based solutions use voice metaphor to search contact numbers, dial numbers, read and send messages loudly by voice [68] such as Android Auto, CarPlay, Do Not Disturb While Driving1, DriveSafe.ly [69], etc. Chris, a digital driver assistant, is an external device linked with the smartphone via Bluetooth, providing the features of text messages, calls, and operate music without physical interaction by using voice commands or gestures. However, these solutions can result in excessive cognitive overload due to voice commands as discussed earlier, off-road visual engagement, and navigational complexity [68]. Furthermore, the latest study [20] found no empirical evidence regarding these applications in minimizing the risk of crashes. The existing user interfaces have numerous challenges due to heterogeneity issues. The heterogeneity can broadly be defined as the multiplicity of the driver, input/out capabilities, environmental conditions, contextual variability, interaction modalities, and computing platforms. The multiplicity of the drivers is based on their physical, visual, and cognitive limitations.
These solutions are vital by considering the leading cause of deaths due to smartphone usages. However, these solutions still have certain limitations which might not be excluded. For example, operating the smartphone using voice commands still requires more visual-manual demands, interior glance time, and higher mental demand than baseline drive [1]. Similarly, this approach may fail to reduce cognitive overload in case of excessive traffic [44, 45]. Privacy could also be an issue when using voice commands, especially when other passengers are in the vehicle. Moreover, installing external hands-free systems is often a barrier due to usability, cost, and lack of practicality [20].
Table 1
Assistive Technologies for Drivers
Available Solutions | Read Text Aloud | Respond text by Voice | Respond to Text Automatically | Respond to Calls Automatically | Operating Smartphone | Integration with Vehicle | Blocking Other Phone Functions |
Android Auto | YES | YES | YES | NO | NO | YES | NO |
AT&T DriveMode | NO | NO | YES | NO | YES | YES | YES |
BAZZ Text While Driving Safe | YES | YES | NO | NO | NO | YES | NO |
Car Mode | YES | YES | No | NO | NO | YES | NO |
Cellcontrol | NO | NO | NO | NO | YES | YES | YES |
cellMUTE + Drive Mode & More | NO | NO | NO | NO | NO | NO | NO |
Do Not Disturb While Driving | YES | YES | YES | NO | NO | YES | NO |
Down For The Count | NO | NO | YES | NO | NO | NO | NO |
Drive Now Text Later | NO | NO | YES | YES | NO | NO | NO |
Drive Safe | NO | NO | YES | YES | NO | NO | NO |
DriveAlertNow | NO | NO | YES | YES | YES | YES | YES |
DriveSafe.ly | YES | YES | YES | NO | NO | YES | NO |
eBrake | NO | NO | YES | YES | NO | YES | NO |
Fleetmode | NO | NO | YES | YES | YES | YES | YES |
Lifesaver | NO | NO | YES | NO | YES | NO | YES |
Live-n-Drive | NO | NO | YES | YES | NO | YES | NO |
MessageLOUD | YES | NO | NO | YES | YES | YES | YES |
Mojo:Reward for Safe Driving | NO | NO | YES | YES | NO | NO | NO |
No Texting While Driving App | NO | NO | YES | YES | NO | NO | NO |
One-Tap-Block Phone Alerts | NO | NO | YES | YES | YES | NO | YES |
ProtextMe Safe Driving | YES | NO | YES | NO | YES | YES | YES |
PULL OVER TO TEXT | NO | NO | YES | NO | NO | NO | NO |
Safe Driving + Auto SMS | YES | YES | YES | YES | NO | NO | NO |
Safe Driving App: DriveMode | NO | YES | YES | NO | YES | YES | YES |
Safely Go | NO | NO | NO | NO | YES | YES | YES |
Text Ninja | NO | NO | YES | YES | NO | YES | NO |
TextDrive | YES | YES | NO | NO | YES | NO | YES |
TextLimit | NO | NO | NO | YES | NO | NO | NO |
TrueMotion Family Safe Driving | NO | NO | YES | YES | NO | NO | NO |