Browse Topic: Telematics
ABSTRACT Building embedded systems is nothing like building desktop applications, as the hard real time requirements and relative harshness of the operating environment further constrains design choices to meet real world needs. Those familiar with mainframe or server farm hosted, high volume, wide bandwidth applications know similar harsh computing environments for application development. Given that more man-hours have been devoted to web application development over the past decade than have been devoted to embedded application development, there may be some valuable lessons to be learned that can be adopted by the embedded community for in-vehicle computing. The best web application development teams successfully apply the notions of Representational State Transformation (REST) and Resource Description Framework (RDF) to handle the increasing demands on their sites. We have taken these technologies and applied them to the smaller scale vehicle telematics platforms (PowerPC, ARM
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
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
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
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
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
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
This SAE Standard defines methods and messages to efficiently translate sequences of text and other types of data into and out of indexed values and look-up tables for effective transmission. This document defines: a Methods and Data Elements for handling indexes and strings in ATIS applications and message sets b Message Sets to support the delivery and translations of tables used in such strings c Tables of Nationally standardized strings for use in ATIS message descriptions And examples of each in illustrative portions. While developed for ATIS use, the methods defined in this document are useful for any textual strings in any Telematics applications found both in Intelligent Vehicles and elsewhere
This SAE Information Report provides a comparative summary between the various messages found in the SAE ATIS standards work (notably SAE J2313, J2353, J2354, J2369 and J2374) and that found in the GATS standard (Global Automotive Telematics Standard). GATS is a message set meant to be deployed on mobile phone systems based on the GSM (Global System for Mobile Communication) phone system which is being deployed in European markets and which the SAE may need to harmonize with as part of the World Standards activities of TC204. This document provides an overview of the various types of supported messages and how they compare with US terms and messages. Some selected features of the GATS work are recommended for assimilation into the next revision of ATIS standards. No attempt at determining a U.S. policy in this regard is provided. This document seeks to provide the reader familiar with SAE ATIS with a high level overview of technical knowledge of the GATS approach in similar areas
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
NREL completed a temporal and geospatial analysis of telematics data to estimate the fraction of platoonable miles traveled by class 8 tractor trailers currently in operation. This paper discusses the value and limitations of very large but low time-resolution data sets, and the fuel consumption reduction opportunities from large scale adoption of platooning technology for class 8 highway vehicles in the US based on telematics data. The telematics data set consist of about 57,000 unique vehicles traveling over 210 million miles combined during a two-week period. 75% of the total fuel consumption result from vehicles operating in top gear, suggesting heavy highway utilization. The data is at a one-hour resolution, resulting in a significant fraction of data be uncategorizable, yet significant value can still be extracted from the remaining data. Multiple analysis methods to estimate platoonable miles are discussed. Results indicate that 63% of total miles driven at known hourly-average
Several external networks like telematics, and SOTA and many in-vehicle networks by gateways and domain controllers have been increasingly introduced. However, these trends may potentially make many critical data opened, attacked and modified by hackers. These days, vehicle security has been significantly required as these vehicle security threats are related to the human life like drivers and pedestrians. Threat modeling is process of secure software development lifecycle which is developed by Microsoft. It is a systematic approach for analyzing the potential threat in software and identifying the security risk associated with software. Through threat modeling, security risk is be mitigated and eliminated. In vehicle software System, one of vulnerability can affect critical problem about safety. An approach from experience and hacking cases is not enough for analyzing the potential threat and preparing new hacking attack. Thus, as specified J3061, in concept phase, threat analysis and
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
Due to the continuous increasing highway transport and the decreasing investments into infrastructure a better usage of the installed infrastructure is indispensable. Therefore the operation and interoperation of assistance and telematics systems become more and more necessary. Regarding these facts Highway Pilot was developed at Daimler Trucks. The Highway Pilot System moves the truck highly automated and independent from other road users within the allowed speed range and the required security distance. Daimler Trucks owns diverse permissions in Germany and the USA for testing these technologies on public roads. Next generation is the Highway Pilot Connect System that connects three highly automated driving trucks. The connection is established via Vehicle-to-Vehicle communication (V2V
Items per page:
50
1 – 50 of 211