Purpose of On-Board Diagnostics on Vehicles (Editing Sample)

CHAPTER 2: LITERATURE REVIEW
2.1 Automotive Diagnostic Apps
There are thousands of apps available on both iOS and Android smartphone platforms; there is an app for it whatever the need. Although Automotive Diagnostic Apps are not yet as abundant, the Automotive Diagnostic Apps ecosystem will continue to grow, enabling rich user experiences and allowing users to perform all kinds of tasks whenever they want (Zhang, 2017). Today, a variety of automotive diagnostic apps exists which are increasingly feature-rich. Some of these systems are densely packed with information and more complicated in terms of human-machine interactions. In contrast, others are simple and less-featured systems that do not meet most of the user expectations. With the improvements of smartphones and tablets, automotive diagnostic applications' popularity is growing; however, most applications confine their content to a limited part of vehicle control functions and information.
For instance, most phone projection systems, Apple CarPlay, provide in-vehicle consumption of mobile devices-based media but do not offer improvement for the rest of connected vehicle features. CarPlay and other similar apps have limited compatibility with different devices and only connect to devices made by Apple. Other phone projection systems, such as MirrorLink, are smaller players in phone projection, but they are not operating-system-specific. These systems operate on a set of well-established technologies such as Wi-Fi, USB, and Bluetooth to create a two-way link between the car and a mobile device, allowing control over essential phone functions using the car's controls in a limited fashion (Lee et al., 2019).
Different developers have different ideas of where vehicle diagnostics information should go or how it should operate. Today, users own multiple connected devices, and they use them concurrently. As a result, interactive design has trended toward multi-system design (Mandal et al., 2019). Designing for different systems requires careful content planning for devices and systems in their proper use contexts. The result is not duplicated but continuous and complementary content to achieve the best overall experience.
Most vehicle diagnostics information systems have been traditionally designed as standalone systems, whereby their content and features lead to overwhelming and unsatisfactory user experiences (Mandal et al., 2019). Driven by the need to identify opportunities for simplification, the proposed vehicle diagnostics information systems will apply a multi-device content strategy to the car context to ensure the best experience. The user requirements analysis shows that the content of a vehicle diagnostics information system can be made simpler by offloading complex interactions or tasks to off-board devices.
2.2 Purpose of On-Board Diagnostics on Vehicles:
Today, sophisticated electronics in modern vehicles are way beyond what was implemented earlier in simple cars. Vehicles have now advanced and evolved, hence running more computerized processes than in the past. This has greatly been influenced by technological innovation in the vehicle industry, as stated by ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"xa0bsxYN","properties":{"formattedCitation":"(W. Li et al., 2021)","plainCitation":"(W. Li et al., 2021)","noteIndex":0},"citationItems":[{"id":15,"uris":["http://zotero.org/users/local/0PDMvOdP/items/UUZURMGS"],"uri":["http://zotero.org/users/local/0PDMvOdP/items/UUZURMGS"],"itemData":{"id":15,"type":"article-journal","abstract":"It is well known that innovation-driven emerging industries have gradually become the main driving force of global economic recovery and growth. Technological innovation decision-making is a complex and dynamic system, which is affected by various factors inside and outside an enterprise. In this dynamic system, how to make the optimal technological innovation investment decisions is a key concern for enterprises and governments. As an investment activity, technological innovation largely depends on the amount of external financing obtained by enterprises. However, financial constraints have increasingly become an obstacle to enterprises’ technological innovation. At the same time, technological innovation is also affected by the external political and economic environment, such as changes in economic policy, government subsidy policies, and institutional environmental policies. Can these external environments reduce the negative impact of financing constraints on technological innovation? In this study, based on the data of listed companies in China’s strategic emerging industries, we adopt a panel negative binomial regression model to investigate the complexity of technological innovation decision-making from the perspective of financing constraints. Our main findings include the following. First, financing constraints significantly inhibit the input and output of technological innovation in emerging industries. Second, the inhibition effect on the output of substantive innovations is more pronounced than that on the output of strategic innovations. Third, based on the analysis of enterprise heterogeneity in different dimensions, we show that this inhibition has a selective effect among different industries. Finally, we show that economic policy and marketization can help alleviate the inhibition effect of financing constraints on technological innovation.","container-title":"Complexity","DOI":"10.1155/2021/3611921","ISSN":"1099-0526, 1076-2787","journalAbbreviation":"Complexity","language":"en","page":"1-14","source":"DOI.org (Crossref)","title":"The Complexity of Technological Innovation Decision-Making in Emerging Industries","volume":"2021","author":[{"family":"Li","given":"Wenjing"},{"family":"Guo","given":"Xue"},{"family":"Cao","given":"Dan"}],"editor":[{"family":"Xin","given":"Baogui"}],"issued":{"date-parts":[["2021",7,30]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (W. Li et al., 2021).
Vehicles are now manufactured and have installed the onboard diagnostic system (OBD) which its primary purpose is to diagnose every possible issue that can occur in the vehicle. A better understanding of what these onboard diagnostics can diagnose ranges from checking whether the car radiator temperature is very high to if the vehicle is running on low oil.
The onboard diagnostics updates notify the user of the problems and malfunctions that the vehicle may be encountering. The near-universal codes can clearly be illustrated by ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"uwv6FKaF","properties":{"formattedCitation":"(Ameen et al., 2021)","plainCitation":"(Ameen et al., 2021)","noteIndex":0},"citationItems":[{"id":17,"uris":["http://zotero.org/users/local/0PDMvOdP/items/GA3JAHV5"],"uri":["http://zotero.org/users/local/0PDMvOdP/items/GA3JAHV5"],"itemData":{"id":17,"type":"article-journal","abstract":"Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios.","container-title":"Information","DOI":"10.3390/info12050194","ISSN":"2078-2489","issue":"5","journalAbbreviation":"Information","language":"en","page":"194","source":"DOI.org (Crossref)","title":"Identification of Driving Safety Profiles in Vehicle to Vehicle Communication System Based on Vehicle OBD Information","volume":"12","author":[{"family":"Ameen","given":"Hussein Ali"},{"family":"Mahamad","given":"Abd Kadir"},{"family":"Saon","given":"Sharifah"},{"family":"Malik","given":"Rami Qays"},{"family":"Kareem","given":"Zahraa Hashim"},{"family":"Bin Ahmadon","given":"Mohd Anuaruddin"},{"family":"Yamaguchi","given":"Shingo"}],"issued":{"date-parts":[["2021",4,29]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}
fully addressing any issue that may occur in a vehicle(Ameen et al., 2021). There needs to be a complete understanding of the difference between onboard diagnostics (OBD) versus onboard diagnostics II (OBD II) and the different available code readers.
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