Browse Topic: Telematics
This paper presents the design of a cost-effective fuel injector driver designed for accelerated testing of injectors. The driver simulates injection patterns across a wide range of vehicle operating conditions and can be programmed with injection maps for different engines, test cycles based on drawing specifications, pre-defined engine running profiles, and manual control, where the user defines PWM frequency and duty cycle. It also enables remote operation through a Wi Fi access point. An injector driver-based test setup was developed to study wear and evaluate leakage tendency in an injector design. To simulate extended field usage in a short timeframe, an accelerated operating cycle was derived using telematics data. Injector samples were tested with periodic leak rate measurements. Conducting such tests at vehicle level or on engine test bench would involve significant time and cost. This setup is an effective tool for rapid comparative analysis across supplier design, enabling data driven product selection. It can also be used for quick evaluation of design improvement features introduced in injectors. The flexible architecture and remote operability make it a valuable tool for future injector development and validation.
Automotive industries focus on driver safety leading to raising improvements and advancements in Advanced Driver Assistance Systems (ADAS) to avoid collisions and provide safety and comfort to the drivers. This paper proposes a novel approach toward Driver health and fatigue monitoring systems that uses cabin cameras and biometric sensors communicating continuously with vehicle telematics systems to enhance real-time monitoring and predictive intervention. The data from the camera and biometric sensors is sent to the machine learning algorithm (LSBoost) which processes the data and if anything is wrong concerning the driver's behavior then immediately it communicates with vehicle telematics and sends information to the emergency services. This approach enhances driver safety and reduces accidents caused due to health-related driver impairment. This system comprises several sensors and fusion algorithms are applied between different sensors like cabin camera and biometric sensors, all these sensors are placed inside the driver compartment without disturbing the driver's comfort and functionality. The input from all these sensors is feed to a centralized processing unit, where advanced sensor fusion technology and machine learning algorithms are used for processing the raw data information. Especially selective machine learning models are used to detect patterns indicative of driver behavior such as drowsiness, fatigue, etc. The alerts will initially notify the driver. If the driver doesn’t respond, the alerts will then be forwarded to the vehicle’s telematics system, which in turn will notify emergency services like ambulance, or designated contacts. This paper presents the architecture of the driver health monitoring system and the effectiveness of the proposed system is validated to the simulated model in the MATLAB environment showcasing its potential and ability to significantly enhance driver safety.
Good driving practices, encompassing actions like maintaining smooth acceleration, sustaining a consistent speed, and avoiding aggressive maneuvers, can yield several benefits. These practices enhance energy efficiency, reduce accident risks, and significantly lower maintenance costs. Consequently, the presence of a system capable of providing actionable insights to promote such driving behavior is crucial. Addressing this need, the Drive-GPT model is introduced, representing an AI-based generative pre-trained transformer. Within this study, the transformative potential of deep learning networks, specifically based on transformers, is showcased in capturing the typical driving patterns exhibited by individuals in diverse road, traffic, weather, and vehicle health scenarios. The model's training dataset comprises an extensive 90 million data points from multivariate time series originating from telematics systems in 100 vehicles traversing eight distinct Indian cities over a six-month span. These pre-trained models offer substantial utility for downstream applications, including the computation of driving scores, generation of driving recommendations, and the classification of driving behavior as either proficient or suboptimal. The performance evaluation on test data indicates commendable results, with a coefficient of determination (R-squared) of 0.98 and a root mean square error (RMSE) of 0.0346. Furthermore, a discernible differentiation emerges in terms of energy efficiency and regenerative braking between good and suboptimal driving behaviors. Notably, this differentiation leads to a notable 25% improvement in energy efficiency and an 18% enhancement in regenerative capabilities.
The Auto industry has relied upon traditional testing methodologies for product development and Quality testing since its inception. As technology changed, it brought a shift in customer demand for better vehicles with the highest quality standards. With the advent of EVs, OEMs are looking to reduce the going-to-market time for their products to win the EV race. Traditional testing methodologies have relied upon data received from various stakeholders and based on the same tests are planned. The data used is highly subjective and lacks variety. OEMs across the world are betting big on telematics solutions by pushing more and more vehicles with telematics devices as standard fitment. The data from such vehicles which gets generated in high levels of volume, variety and velocity can aid in the new age of vehicle testing. This live data cannot be simply simulated in test environments. The device generates hundreds of signals, frequently in a fraction of seconds. Multiple such signals can be combined to create KPIs that correspond to specific traits of vehicle health. Specific telematics KPIs can be used as inputs in test benches. With this data, the vehicle gets tested against real-world conditions. Another important aspect of vehicle testing is road conditions. OEMs can only do so much in testing the vehicles in various different road conditions. However, with telematics devices road condition data can be directly generated corresponding to the international roughness index. This data along with KPIs will bring in a new perspective of vehicle development testing and enable the manufacturer to better understand market problems and to take effective countermeasures. With these KPIs, Possibilities are limitless, and these data can be used to create a digital twin of the vehicle, enabling the OEM to assess the vehicle without even physically attending.
The power of advanced driver assistance systems (ADAS) continues to increase alongside vehicle code and software complexity. To ensure ADAS functionality and maximize safety, cost efficiency, and customer satisfaction, original equipment manufacturers (OEMs) must adopt a solution that allows them to mine data, extract meaningful information, send remote software updates and bug fixes, and manage software complexity. All of this is possible with an embedded telematics-based software and data management solution. Event-based logging enables OEMs to actively measure ADAS effectiveness and performance. It allows them to analyze driver behaviors, such as whether response times increase after a certain time of day, and adjust the ADAS settings to increase functionality, such as providing an earlier warning or automated response. A vertically integrated solution also enables the identification and correction of software and calibration defects for the entire vehicle life cycle through over-the-air (OTA) software update packages. This eliminates the need for costly and time-consuming dealer visits and allows troubleshooting, updates, repairs, and recalibration to be performed remotely. There are industry-wide benefits of deep connectivity as well, such as the sharing of critical and safety-related data to make functional enhancements and use in the creation of a real-world safety rating system instead of one based on lab data and general speculation. Connected services are essential for not only the future of ADAS but also for the creation of a safer driving environment for all. In this paper we will: Look at emerging trends in ADAS vehicle systems Show how connected vehicle data can measure real-world ADAS performance, including false positives and false negatives Show how connected vehicle data can be used to understand driver behaviors including usage patterns and feedback responsiveness Show how systems can be maintained and enhanced throughout the product life cycle in a cost-effective manner
Jobsites look to overcome challenges posed by mixed fleets and proprietary telematics to achieve “one-dashboard” vision and increased machine utilization. Wouldn't it be great if the entire jobsite - the general contractor, subcontractors, designers, owners, equipment vendors and material suppliers - were all working in sync with the data that shifts with each condition change, progress report, change order, telematics warning and machine inspection? That the right people got the right information at the right time to make informed decisions? This “one-dashboard” vision is much easier said than done. The journey of one equipment manager illuminates the roadblocks. Several years ago, Langdon Mitchell, equipment division general manager for heavy civil contractor Morgan Corp., needed to have someone physically go machine-by-machine to update the software in his fleet.
Connected vehicles has lot of applications coming up which involves Vehicle to Vehicle(V2V), to Infrastructure(V2I) communication techniques and many are getting deployed through frugal connectivity and security solutions. However, executing multiple customer centric, OEM centric and productivity services and applications needs lot of rigorous validation and bulk deployments. This needs lot of time, man efforts, and cost. Also quality of data collected from thousands of vehicles at every few seconds /minutes is matter of concern. There is a desperate need of telematics validation tools which can be customized as per OEM ad-hoc architecture. The suggested telematics smart testing and validation tool involves easy, configurable, and seamless options to test software updates in multiple variable nodes in connected onboard system architecture like telematics ECUs, multiple ECUs (EMS, ACM, IC, and Gateway ECU). The accuracy of the validation is more than 98% considering high sampling frequency of CAN messages on J1939 CAN bus. The system covers overall aspects and gives option to print direct reports to get the insights of the validation software which is under test. This tool does not need any human intervention once installed inside the vehicle and thereby reducing time and man efforts. The data from the telematics device and parallel CAN trans-receiver and connected module is compared and acceptable deviation is configured for quick checks and reports. FTP server is required for data ingress from telematics and ECUs. Tool uses low cost hardware and server to manage data. This tool has potential to have good data quality from vehicles.
A CAN transceiver with built-in security functions can avoid the complexity of end-to-end security solutions that are especially hard to implement on CVs. Commercial road vehicles are the backbone of the modern consumer economy. Almost any business from construction, to energy, to online retail at some point relies on the delivery of goods by commercial vehicles, which in turn are becoming increasingly connected both to the external world and to each other via telematics. This enables CV owners to optimize and manage their fleets via platooning for safety and efficiency improvements as well as cost and fuel-consumption reduction to meet the increasingly stringent CO2 emissions requirements necessitated by climate change. However, the increased connectivity brings with it an increase in cyberattack surfaces and CV fleets are prime targets for cybercrime due to the high value of the cargo they carry, and their importance to large businesses and the greater economy. While CV manufacturers are familiar with and prepared for the risk of physical attacks - typically carried out on one vehicle, such as odometer manipulation or theft - they may risk being caught by surprise at the scale and impact of what is possible with remote cyberattacks. Hackers can exploit a vehicle's wireless network or internet connection to gain entry into the vehicle's communication network and compromise security to access a vehicle's CAN (Controller Area Network) and take over remote management of the vehicle while it is in motion.
The logistics process in Brazil and the world represents a significant portion of the cost of manufactured products, either for export or import. The availability of technologies that make the logistic process more efficient directly affects the product’s transportation productivity and makes them more competitive. This paper presents a telemetry model of commercial vehicles integrated with harvest machines in agriculture operations, allowing accurate scheduling of loading and unloading processes at the field. In this study, we introduce a conceptual model of a technological matrix, where the shared topologies of vehicle information processing help predict failures, identification of wear of vehicle and machine’s components. The opportunity is demonstrated to collect data from agricultural machines and combine them with data extracted from trucks. The sharing of information on farm machinery and trucks in real-time establishes an essential change in crop management in the field.
It is commonly believed that running-in behavior is related to engine reliability and fuel economy. This paper uses a methodology to find the influence of running-in, based on telematics data. In this paper, the key related telematics parameters are identified to assess running-in behaviors through feature analytics with telematics vehicle real-road data. By analyzing these parameters, truck groups subjected to different running-in behaviors are classified to evaluate the relationship between running-in behaviors and fuel economy.
To achieve accuracy in model development with large-scale actual customer data in low cost and limited time usage of telematics system was adopted. Honda’s OBD II diagnostic connecting device Honda Connect was used as transceiver for this telematics system, which was used as an accessory in Honda vehicles. Data collected with this device with large sample size and regional diversity across India was used in product development for Honda System. Control system development for BSVI vehicles, Idle start stop hardware specification selection and Battery electric vehicle target range study was done with Honda Connect Data.
Present-day vehicles come with a variety of new features like the pre-crash warning, the vehicle-to-vehicle communication, semi-autonomous driving systems, telematics, drive by wire. They demand very high bandwidth from in-vehicle networks. Various ECUs present inside the automotive transmits useful information via automotive multiplexing. Transmission of data in real-time achieves optimum functionality. The high bandwidth and high-speed requirement can be achieved either by using multiple buses or by implementing higher bandwidth. But, by doing so, the cost of the network as well as the complexity of the wiring increases. Another option is to implement higher layer protocol which can reduce the amount of data transferred by using data reduction (DR) techniques, thus reducing the bandwidth usage. The implementation cost is minimal as the changes are required in the software only and not in hardware. This article presents a new data reduction algorithm termed as “Comprehensive Data Reduction (CDR)” algorithm. The article also demonstrates a comparison of the proposed algorithm with the boundary of fifteen compression algorithms and compression area selection algorithms. The results show that the proposed CDR algorithm provides better data reduction compared to the earlier proposed algorithms. The proposed algorithm has been developed for automotive environment, but it can also be utilized in any applications where extensive information transmission among various control units is carried out via a multiplexing bus.
As mobile data traffic expands 10-fold from 30 Exabytes in 2014 to 292 Exabytes in 2019, and the total mobile service subscriptions reaches 6.8 billion, in a global population of 7.3 billion, there is a compelling move towards connected cars and services. Though SDB research suggests US$ 18 billion additional revenue from these services, but most important question is “Are the consumers, who buy cars, willing to pay extra for these services”. Traditional business models of OEMs who buy parts from Tier-1 suppliers, and sell the vehicles to consumers as a one-time sales revenue per consumer, have to learn a lot for new successful business models. Telematics services, introduced in early 2000, bears testimony. For the New Business Models, the OEMs have to work with the ecosystem of service providers, who themselves have different a business model of operations. They do not believe in charging everything to the consumers. Even their accounts (revenue generators) and suppliers are different. The Digital Technology Ecosystem includes mobile equipment players (like Samsung), telecom service providers (like Vodafone), telecom network providers (like CISCO), Internet OS provider (like Microsoft), Search engine and app platform providers (like Google), cloud providers (like Amazon) and a combination of such providers (like Apple). To be successful in this digital age, the Automotive OEMs have to look at cars generating valuable data, and devise Business Models for monetizing this data, keeping the confidentiality compliance into consideration. This paper looks at these alternative Business Models available with the Automotive OEMs and Tier-1 suppliers, in partnership with the Digital Technology Ecosystem service providers. A mathematical approach to business models, with the parameters, constraints and options, has been presented, which can be adopted by the Automotive Industry.
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