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A DIGITALIZED VALIDATION APPROACH FOR REAL TIME AND REMOTE MONITORING OF AN OFF HIGHWAY VEHICLE PERFORMANCE

Research & Devlopment Institute-JAGANNATHAN VASU
  • Technical Paper
  • 2019-28-2531
To be published on 2019-11-21 by SAE International in United States
A DIGITALIZED VALIDATION APPROACH FOR REAL TIME AND REMOTE MONITORING OF AN OFF-HIGHWAY VEHICLE PERFORMANCE V.Jagannathan 1.a* , B.Jaiganesh 2.b & S.Sudarsanam 3.c Mahindra & Mahindra Limited, Mahindra Research Valley, Mahindra World City, Anjur PO, TN, India Corresponding author Email- V.JAGANNATHAN@mahindra.com Validation of agricultural tractors is necessary to ensure that these machines perform to their expected potential and are aptly matched with implements. Testing these machineries in real-time while performing activities in the field allows a bigger picture to be seen; the performance data incorporates the effects of many external factors (Soil, Climate etc.). Tractor Performance data apprehending is the vital part of validation. Data acquisition of key performance parameters during field validation in different application/different countries is of utmost importance. Most prevailing methodology in Tractor validation is by capturing the performance parameters such as Fuel consumption, Area coverage, Wheel slip, Engine rpm drop, implement depth of cut, Tractor speed etc. in manual and physical way. These methodologies of capturing performance parameters are tedious, time consuming, involves manpower, not so secured or safe. The readings…
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DEVELOPMENT OF A FLEET MANAGEMENT SYSTEM FOR AN OFF-HIGHWAY VEHICLE

Research & Devlopment Institute-JAGANNATHAN VASU
  • Technical Paper
  • 2019-28-2439
To be published on 2019-11-21 by SAE International in United States
DEVELOPMENT OF A FLEET MANAGEMENT SYSTEM FOR AN OFF-HIGHWAY VEHICLE V.Jagannathan 1.a* , B.Jaiganesh 2.b & S.Sudarsanam 3.c Mahindra & Mahindra Limited, Mahindra Research Valley, Mahindra World City, Anjur PO, TN, India Corresponding author Email- V.JAGANNATHAN@mahindra.com Managing an off-highway vehicle fleet during validation is a challenging task. Complexity is acquainted when more than 100 vehicles with different horse power (hp) & with different product configuration working across India and other parts of countries. Traditionally, a tractor validation involves data collection such as usage hours (Hour meter reading on cluster), locations etc. which are recorded in spread sheet and updated to the respective project owners on daily basis through mail communications. A manual recording and consolidation of tractors validation status is prone to error, reiterative work, consumes more resource and effort. Moving towards digitalization, IT enabled system for updating the tractor validation status on daily basis was added with advantage of huge data storage capacity, history data retrieval and data access anytime & anywhere, a step ahead to traditional method but with few limitations of not…
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BIG DATA, BIG BENEFITS

SAE Truck & Off-Highway Engineering: August 2019

Terry Costlow
  • Magazine Article
  • 19TOFHP08_02
Published 2019-08-01 by SAE International in United States

Data mining helps users and equipment developers use data from on-vehicle sensors to work more efficiently.

The huge volumes of data created by on-vehicle systems are being mined to bring a range of benefits to vehicle operators and fleet managers. Predictive maintenance is becoming more common, while data mining is helping OEM design and manufacturing teams enhance their programs.

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What M&E Can Teach the AV Industry About Data

Autonomous Vehicle Engineering: July 2019

Jason Coari, Mark Pastor
  • Magazine Article
  • 19AVEP07_05
Published 2019-07-01 by SAE International in United States

Media & entertainment offers important learnings on data retention, management, scalability and security.

At first glance, autonomous vehicles would seem to have little in common with the Media and Entertainment (M&E) industry, beyond action-movie car chases and in-vehicle entertainment screens.

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Sensors Expo Preview 2019

  • Magazine Article
  • TBMG-34584
Published 2019-06-01 by Tech Briefs Media Group in United States

The 2019 Sensors Expo and Conference will be held at the McEnery Convention Center, San Jose, CA from June 25 – 27. Both the expo and conference are excellent opportunities to get a good sense of important sensor trends. Among the wide variety of themes at the conference, some strike me as being very significant for the current state of the industry.

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Editorial

Autonomous Vehicle Engineering: May 2019

Editorial Director-Bill Visnic
  • Magazine Article
  • 19AVEP05_01
Published 2019-05-01 by SAE International in United States

AVs, data and ‘surveillance capitalism’

I finally hit my limit after seeing a Twitter post-from, almost paradoxically, a privacy engineer at Google-showing a taxi cab in Asia with a passenger-area screen announcing it was employing facial recognition “to deliver the most optimized content” to the rider.

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An Efficient Trivial Principal Component Regression (TPCR)

General Motors LLC-Balakrishna Chinta
Published 2019-04-02 by SAE International in United States
Understanding a system behavior involves developing an accurate relationship between the explanatory (predictive) variables and the output response. When the observed data is ill-conditioned with potential collinear correlations among the measured variables, some of the statistical methods such as least squared method (LSM) fail to generate good predictive models. In those situations, other methods like Principal Component Regression (PCR) are generally applicable. Additionally, the PCR reduces the dimensionality of the system by making use of covariance relationship among the variables. In this paper, an improved regression method over PCR is proposed, which is based on the Trivial Principal Components (TPC). The TPC regression (TPCR) makes use of the covariance of the output response and predictive variables while extracting principal components. A new method of selecting potential principal components for variable reduction in TPCR is also proposed and validated. Two example problems, which are highly collinear, were considered for illustration. Results are also compared with the Partial Least Squares Regression (PLS1), which is another widely used statistical method, for ill-conditioned data analysis. From these results, it…
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Analyzing and Preventing Data Privacy Leakage in Connected Vehicle Services

Ford Motor Co., Ltd.-Yu Seung Kim, Pramita Mitra
University of Michigan-Huaxin Li, Di Ma, Brahim Medjahed
Published 2019-04-02 by SAE International in United States
The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. We also propose corresponding countermeasures to defend against such privacy attacks. We evaluate the feasibility of such attacks and our defense strategies using real world vehicular data.
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New Paradigm in Robust Infrastructure Scalability for Autonomous Applications

Wayne State University-Kyle W. Brown
Published 2019-04-02 by SAE International in United States
Artificial Intelligence (A.I.) and Big Data are increasing become more applicable in the development of technology from machine design and mobility to bio-printing and drug discovery. The ability to quantify large amounts of data these systems generate will be paramount to establishing a robust infrastructure for interdisciplinary autonomous applications. This paper purposes an integrated approach to the environment, pre/post data processing, integration, and system security for robust systems in intelligent transportation systems. The systems integration is based on a FPGA embedded system design and computing (EDGE) platform utilizing image processing CNN algorithms from High Energy Physics (HEP) experiments in data centers with associative memory to ROS- FPGA technology in vehicles for hyper-scale infrastructure scalability. The ability to process data in the future is equivalent to collision particle detection that the Large Hadron Collider (LHC) produces at CERN. The future of robust scalability will depend upon how seamlessly several applications can be integrated into a high-performance package with minimal consumption. The proposed architecture will entirely be dependent on a digital network with special attention paid to…
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Transforming ZF

Automotive Engineering: April 2019

Lindsay Brooke
  • Magazine Article
  • 19AUTP04_01
Published 2019-04-01 by SAE International in United States

The Tier 1 giant re-gears for the new-mobility zukunft by adopting a new way to drive technology innovation. It still makes transmissions, too.

We are looking for a new colleague in the Digitalization department,” announced the job opening posted online February 28, 2019. The position: Artificial Intelligence Engineer.

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