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The explosion of mobile phone growth has been significant over the past couple of decades but it has been clear that impact on this form of mobile communication having on driving. In today's world, number of accidents is due to distracted driving especially on usage of mobile phone while driving which has been determined as the major cause of autom...
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... This set-up facilitates more trustworthy discrimination of driver use of mobile phone. The output of RF amplifier stage is given to PIC16F917 microcontroller which executes the voltage analysis algorithm. The microcontroller is programmed in such a way that, once the voltage level obtained from the RF amplifier stage is greater than voltage value stored in EPROM of microcontroller, it will activate the jammer which will prevent cellular phones from receiving signals from base stations. MAX232 which is an integrated circuit that converts signals from an RS-232 serial port to signals suitable for use in TTL compatible digital logic circuits. In this experiment, a call was made to the rear seat passenger and to the driver when the vehicle was moving. A call was maintained for few seconds, an antenna which was placed above the driver’s seat captures more energy from a mobile phone over various open-air distances when compare to rear seat passenger as shown in Fig 3(a),3(b). When the mobile phone is not in use, the energy captured is minimal. When a call is made, this energy is inversely proportional to the distance between the telephone and the energy-capturing antenna. Here, we have set threshold value as 100mv, once the signal received by the antenna exceeds threshold value a jammer will gets triggered which will prevent cellular phones from receiving signals from base stations. In the above circuit we have used low filter to suppress the false signal. Fig 4 shows the vehicle’s internal structure with energy capturing circui t along with mobile jammer When a caller initiates a call by dialling a number in his mobile it directly send a request to the BTS which he comes under. BTS there by sends the request to the BSC to which it is connected and from the BSC, the request is made to the MSC. Subsequently MSC sends a request to the HLR to check the information about the caller like account balance (if pre paid), area of the caller etc. After checking all the details the HLR sends a acknowledgement message to the MSC that the caller is O.K. to make a call or not. Once the message received by MSC it establishes an air link between the both parties and the call gets connected. When the phone started ringing it activate a jamming device which transmits on the same radio frequencies as the cell phone, which disrupt the communication between the phone and the cell- phone base station in the tower as shown in Fig 5. Since the voltage captured by the energy capturing circuit exceeds threshold value it’s a called a denial - of-service-attack. Once the driver dials the number and press call button the mobile device will start transmitting more voltage. The energy capturing circuit captures voltage above the threshold value which results in activation of mobile jamming device which squeeze the RF signal as shown in Fig 6. Which forces the driver not to use mobile phone while drive. Normally the mobile jammer will take approx.20-30 sec. to start blocking the communication. Even during this time frame there is high possibility of driver getting distracted. In order to avoid the driver from taking the phone during these time frames we use ATMAL AT89C52 microcontroller which will get activated when voice communication is carried out. The microcontroller contains information about vehicle number plate which will be transmitted through ND R433 transmitter. The transmitted information will be received by RXD1 receiver which is placed on the signal post. This received information will be displayed in the LCD which will be monitored by traffic police as shown in Fig 7. Once the traffic police taken the legislative action against the driver they can remove the alert from the LCD which is password protected. The information received in the LCD will also get transferred to the neighboring traffic signal LCD display so that all traffic police in the corresponding area aware of that vehicle. Once the action is taken against the driver, police officer can remove the alert in any one of the LCD display which will get reflected in all surrounding LCD ...
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Citations
... Figure 2 shows that amongst them are representatives of all three databases used. Two of the 16 unique papers can be found in two of the databases (Shabeer and Wahidabanu, 2012 [62], are in the result lists of Scopus and WoS, and Paruchuri and Kumar [63] are in the result lists of WoS and IEEE), while one (Song et al., 2016 [64]) is present in all three database result lists. ...
... Shabeer and Wahidabanu [62] detect incoming or outgoing phone calls while driving using an antenna located on the top of the driver's seat for detecting when the driver uses their mobile phone. Thereby, a GSM signal connection between the smartphone and other entities of the GSM Network Architecture (e.g., mobile switching centres and base stations with associated base transceivers) is detected. ...
Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained.
... Implementing deep learning techniques, they achieved 87.02% accuracy in detecting distracted driving. The smartphone camera has also been used in prior work (Wesley et al., 2010;Shabeer & Wahidabanu, 2012). Nambi et al. (2018) used both smartphone cameras in order to monitor distracted driving; the front camera for driver monitoring and the back camera for driver behavior monitoring. ...
While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.
... Various other applications and operating systems have been introduced to block, or mute external communications and send automated responses whilst driving [21,22]. The isolation of the driver from external influences might enhance temporarily the driver's attention yet on multiple occasions this could upsurge the driver's anxiety, feeling disconnected and deprived of potentially crucial information [9,23]. ...
Multiple infotainment sources can significantly overload the driver’s cognitive load and increase the collision probabilities. Current solutions provided have attempted to alleviate this issue with the centralization of infotainment devices to single touchscreen devices in the dashboard area. Yet this solution still requires the driver to take the eyes from the road and concentrate on operating the secondary tasks unrelated to the driving process. The paper presents a prototype Augmented Reality Head-Up Display (AR HUD) system that superimposes a selected number of infotainment data on the vehicle’s windshield only when this is safe for the driver. The selection of infotainment data and projection timing is calculated by a prototype Artificial Intelligence (AI) Co-Driver that aims to reduce the driver’s cognitive load. The proposed system was evaluated by 50 users against a typical touchscreen dashboard system. This work presents and discusses the subjective feedback related to the cognitive load that the users perceived during the trials. The paper concludes with a future plan for improving both the AI and AR HUD elements to perform in an urban environment.
... Shabeer & Wahidabanu [44] detect incoming or outgoing phone calls while driving, using an antenna located on the top of driver seat for detecting when the driver uses its mobile phone. Thereby, a GSM signal connection between the smartphone and other entities of the GSM Network Architecture (e.g. ...
Distracted driving is known to be one of the leading causes of vehicle accidents. With the increase in the number of sensors available within vehicles, there exists an abundance of data for monitoring driver behaviour, which, however, have so far only been comparable across vehicle manufacturers to a limited extent due to proprietary solutions. A special role in distraction is played by the smartphone, which is repeatedly a source of distraction for drivers through calls and messages. However, the smartphone can be used for driver behaviour monitoring (like driver distraction detection) too, as current developments show. As vehicle manufacturer-independent device, which is usually equipped with adequate sensor technology, smartphones can provide significant advantages, however, an overview of such approaches is missing so far. Thus, this work carries out an author-centric literature review of 16 research papers to illustrate the opportunities in using smartphones to detect driver distraction.
... Researchers have shown that the reaction time of drivers increases by 0.5 to 1.5 seconds when they are talking on handheld phones, and drivers have difficulty maintaining the correct positions in their lanes, maintaining appropriate speeds and judging and accepting safe gaps in traffic. 4 Despite the high risks associated with distracted driving; there is little knowledge about its extent and nature in India. While distracted driving includes any activities that take eyes or attention away from driving, there has been particular and intense interest on texting and other Smartphoneassociated distraction as smart phones have become widely available over the past 10 years. ...
WHO categorizes driver distraction like mobile usage as an important risk factor for road crash injuries, asdriver’s reaction time increases causing difficulty in: maintaining correct positions in their lanes, maintainingappropriate speeds and judging safe gaps in traffic.Descriptive study with objective of assessing knowledge, attitude and practices regarding mobile usagewhile driving amongst medical and engineering students aged 18-21 years was carried out using proformaafter taking consent.During driving, most of medical (61%, 79%) & engineering (60%, 69%) students called & receivedcalls; 55% & 60% knew it is distracted driving & only 43% & 46% always considered it as punishablerespectively. 42% & 30% read and 34% & 18% texted message; 53% & 26% used social media (WhatsApp,Facebook etc); most viewed maps/directions on their phone and felt that pedestrians using mobile are pronefor accidents. Although 87% & 88% would advice others, only 52% & 55% would restrain mobile usageduring driving respectively.This study suggest for improving on-ground situation through formal education to bring about moralresponsibility amongst youth and propose automatic electronic system in vehicles for early detection of thisglobally identified risk factor
... One of the more serious consequences of uncontrolled smartphone use is the occurrence of accidents. Unintentional injuries, such as road traffic injuries, pedestrian collisions, and falls have increased with the incidence of smartphone users talking, texting, or listening to music while on the road (Kim, Min, Kim, & Min, 2017;Kong, Xiong, Zhu, Zheng, & Long, 2015;Shabeer & Wahidabanu, 2012;Walsh, White, Hyde, & Watson, 2008). One in three adolescents aged 16-17 reported that they texted while driving (Madden & Lenhart, 2009). ...
... Smartphones compel people to keep checking them and this leads to fear of missing out (FOMO) and "nomophobia" (no mobile phone phobia), which are situations that will cause distress or anxiety in young people (De-Sola Gutiérrez et al., 2016;Thomée, 2018). The problematic use of smartphone leads to psychosocial complications in which some of them would even text or watch video while they are walking, driving, or even talking to others (Sansone & Sansone, 2013;Shabeer & Wahidabanu, 2012;Walsh et al., 2008). A physical loss of phone is even worse, with many being very concerned about losing personal information, disconnecting from others, their mobile payment options being misused, the leakage of inappropriate photos or videos, and missing the newest updates of self or others (De-Sola Gutiérrez et al., 2016). ...
Almost all adolescents between the ages of 12 and 19 own a smartphone and these offer quick access to the internet and social media, which facilitates message transmissions or communication, plus a wide range of amusements such as games, multimedia players, photo albums and electronic books, and essential tools including camera. Owing to the convenience and multiple functions of smartphones, users tend to become overattached and preoccupied with their devices, and excessive or problematic smartphone use is twice as prevalent among teenagers as among adults. Greater consensus among researchers is needed in relation to the development of guidelines relating to the management of problematic smartphone use. Psychosocial treatments remain the dominant approach. A comprehensive management plan should be tailored to the person’s risk factors and needs, taking into account the external support available from parents (family), schools, and may even consider advanced ICT to achieve the best outcomes.
... C. Bo et al [24], proposed a technique to detect texting while driving by assessing the timing differences between keypad entries on the phone. More sophisticated techniques like a) using external antennas [25]; b) using acoustic ranging techniques between a phone and car speakers [26], and c) computing centripetal acceleration from smart-phones sensors to assess vehicular dynamics [27] are also attempts to detect phone use while driving. But, driver discomfort levels or basic driver ergonomics were not been detected by any of these techniques. ...
As per the studies involving automobile accidents, every year, an enormous number of fatalities occur on the roads of India because of road traffic crashes on account of driver’s slip-up. Distraction caused to the vehicle driver is a well-noticed reason for this error, though less reported. The prime source for such distractions can be an outcome of general ergonomics, vehicle design, distractions from in and out of the vehicle or other psychological factors. With the fast development of in-vehicle systems and infotainment gadgets, the burden associated with distracted driving is expected to increase. The objective of this paper is to review current researches in the field of driver behavior and other quirks that contribute driver distractions in Indian road conditions. The sources, influence and other contributing factors of driver distractions were analyzed and suggestions for engineering and administrative control strategies were discussed in the paper. This review also underlines the need for constant driver-vehicle interactions to avoid accidents. Further, the study justifies the focus on training strategies and technological involvements that would address the issue of driver distractions.
... In [14], a technique is proposed to only detect texting while driving by assessing the timing differences between key pad entries on the phone. More sophisticated techniques like a) using external antennas [15]; b) using acoustic ranging techniques between a phone and car speakers [16]; and c) computing centripetal acceleration from smart-phones sensors to assess vehicular dynamics [17] are also attempts to detect phone use while driving. However, none of these techniques aim to detect the type of distracted driving activity among texting, calling and reading on the phone, which we accomplish in this paper. ...
In this paper, we explore the feasibility of leveraging the accelerometer and gyroscope sensors in modern smartphones to detect instances of distracted driving activities (e.g., calling, texting, and reading while driving). To do so, we conducted an experiment with 16 subjects on a realistic driving simulator. As discussed later, the simulator is equipped with a realistic steering wheel, acceleration/braking pedals, and a wide screen to visualize background vehicular traffic. It is also programmed to simulate multiple environmental conditions like daytime, nighttime, fog, and rain/snow. The subjects were instructed to drive the simulator while performing a randomized sequence of activities that included texting, calling, and reading from a phone while they were driving, during which the accelerometer and gyroscope in the phone were logging sensory data. By extracting features from this sensory data, we then implemented a machine learning technique based on random forests to detect distracted driving. Our technique achieves very good precision, recall, and F-measure across all environmental conditions we tested. We believe that our contributions in this paper can have a significant impact on enhancing road safety.
... Many mitigation strategies were recommended or proved to be successful for this hazardous behavior. In addition to law enforcement efforts (McCartt et al., 2003;McCartt and Geary, 2004), some studies recommended focusing on the design of new technologies related to vehicles and cell phones in order to provide warnings to drivers and help them to focus more on the road (Zhou et al., 2012) or technologies to prevent drivers from receiving a signal while driving (Shabeer and Wahidabanu, 2012). Other studies suggested increasing the awareness of people by educating them through the media and other measures such as showing them scenes of accidents (Zhou et al., 2012). ...
There is no doubt that cell phone use while driving can lead to a higher probability of driver error, which increases the likelihood of more crashes. In Qatar, the high rate of cell phone use while driving among young drivers is a major traffic safety concern. The objectives of this study are to identify the factors affecting this hazardous behavior and to suggest practical solutions to deter this specific category of drivers from driving while distracted. The study combined stated and revealed preference questions to design a detailed survey questionnaire. Data were collected from a sample of 403 young drivers. The structural equation modeling results showed that, for the revealed preference, conducting public campaigns may provide a suitable solution to reduce cell phone usage while driving. On the other hand, increasing enforcement did not seem to have a significant effect on reducing this type of behavior. For the stated preference, young drivers who had a crash history resulting from cell phone usage tend to use their cell phones less than those who did not have a cell phone related crash. Furthermore, the driving experience and safe duration of distraction had a significant effect on the cell phone usage. Based on the results, it is recommended to provide road safety campaigns to educate young drivers on the risk associated with such behavior. This information is valuable to legislators and traffic safety experts dealing with this problem in Qatar and other countries in the region.
... In [9], a technique is proposed to only detect texting while driving by assessing the timing differences between keypad entries on the phone. More sophisticated techniques like a) using external antennas to discover, and then jam phone use by drivers [10]; b) using acoustic ranging techniques between a phone and car speakers [11]; and c) computing centripetal acceleration due to vehicular dynamics [12] are also attempts to detect phone use while driving. However, none of these techniques aim to detect the type of distracted driving activity among texting, calling and reading on the phone, using wrist-worn wearables which we accomplish in this paper. ...
