ABSTRACT A simulation capable of modeling grid-tied electrical systems, vehicle-to-grid (V2G) and vehicle-to-vehicle(V2V) resource sharing was developed within the MATLAB/Simulink environment. Using the steady state admittance matrix approach, the unknown currents and voltages within the network are determined at each time step. This eliminates the need for states associated with the distributed system. Each vehicle has two dynamic states: (1) stored energy and (2) fuel consumed while the generators have only a single fuel consumed state. One of its potential uses is to assess the sensitivity of fuel consumption with respect to the control system parameters used to maintain a vehicle-centric bus voltage under dynamic loading conditions.

Jane, Robert S., Parker, Gordon G., Weaver, Wayne W., Goldsmith, Steven Y.

Lend a Range: Vehicle-to-Vehicle (V2V) charging.

2024-26-010701/16/2024

With increase in number of EVs on Indian roads, poised EV makers to produce innovative and pragmatic concept of electric vehicle features. The concept of bidirectional charging is one of that and which is creating buzz and curiosity among EV buyers. The bidirectional charging enables EV owners to lend the power to grid, other vehicles or use for other auxiliary applications. This paper focuses on idea of vehicle-to-vehicle (V2V) level 1 or level 2 AC charging using J1772 standard, where one user can lend a range of few kilometers to other based on requirement as a helping hand. This paper majorly focuses on how existing EV powertrain system can be upgraded to implement V2V charging with minimal changes. In V2V charging, source vehicle shall function as a mobile charging source (EVSE) and other shall function as a sink (EV). The idea of making source vehicle as charging station involves sink vehicle authentication and managing the power flow. This paper discusses the overall system

KUMAR, ROHIT, Penta, Amar, Venugopal, Karthick Babu, Sahu, Hemant, Arya, Harshita

Vehicle diagnostics and Vehicle to Infrastructure Communication through Visible Light Communication

2024-26-008201/16/2024

High Fidelity Communication has become a necessity in various sectors. Different wireless data transfer methods play a vital role in various far field and near-field communications. Wireless communication for transferring data through radio spectrum has been a continuous evolving trend, especially in Automotive Sector, with fleet monitoring, platooning and even connected vehicles. Some important parameters considered in selecting a wireless platform would be bandwidth, data transfer, speed, and security. Some interesting advantages of communication over the visible spectrum has led to the evolution of Light Fidelity. Implementation of Visible Light Communication (VLC) in the automotive field might enable safer driving conditions through vehicle-to-vehicle (V2V) and vehicle to Infrastructure (V2I) communication with high data transmission rates and efficient-bandwidth usage. The principle of VLC is based on “line of sight” data transmission through modulation of the light source. Highly

Ali, Rifat, N, Prem, Natarajan, Akshay, Kashi, Anitha

New 5G opportunities, Risk and autonomous vehicles.

2024-26-008501/16/2024

With the rapid advancement in 5G technologies and the global network roll out, the autonomous vehicle space is seeing many Opportunities, The Automotive Industry with its Connected Vehicles is expected to heavily utilize the 5G network features such as low latency, high-bandwidth, and network slicing. Current Network capabilities has restricted the Connected space in Automotive to Basic features like track & trace, DTCs, Online Diagnostics and Predictive maintenance at best. Almost all of the control functions in vehicles today are processed in on-board ECUs but Novel technologies like Autonomous Fleet/Vehicles, ADAS demands this functions to be integrated with connected environment. With the low latency High speed environment of 5G such time critical processing is possible at the cloud end. We can move some ECU functions of autonomous vehicles to the cloud or use a hybrid of cloud and on-board processing model in autonomous vehicles with 5G connectivity. Cloud-based ECUs will create

Mahali, Rakesh, JAGTAP, Pramod

Design and Development of a Virtual Autonomous System

2022-01-507210/03/2022

An autonomous vehicle or a driverless vehicle is a ground vehicle that is capable of sensing its environment and moving safely with little to no human input. The present study aims to validate existing literature on vehicle following, emergency braking, lane changing, overtaking, and jerk trajectory with regard to autonomous vehicles. By using the jerk trajectory equation, we are able to optimize it by implementing a complete overtaking procedure. We are also able to compare the variation in both the dynamic and kinematic bicycle model for the same vehicle and traveling the same distance. A new kinematic bicycle model is built, which is more simple to construct. This project is performed using MATLAB and Simulink for creating and modifying the system blocks. There has also been an integration of 3D visualization software such as VR sink and Unreal Engine. We construct the vehicles by simple transfer functions, ACC and PID as the main system blocks for vehicles 1, 2, and 3, respectively

Narain, C.M., Patil, Sharanbassapa S.

Functional verification and validation of secure controller area network (CAN) communication

2022-01-011803/29/2022

In agriculture industry, increasing use of Vehicle Internet of Things (IoT), telematics and emerging technologies are resulting in smarter machines with connected solutions. Inter and Intra Communication with vehicle to vehicle and inside vehicle - Electronic Control Unit (ECU) to ECU or ECU to sensor, requirement for flow of data increased in-turn resulting in increased need for secure communication. In this paper, we focus on functional verification and validation of secure Controller Area Network (CAN) for intra vehicular communication to establish confidentiality, integrity, authenticity, and freshness of data, supporting safety, advanced automation, protection of sensitive data and IP (Intellectual Property) protection. Network security algorithms and software security processes are the layers supporting to achieve our cause. Test environment setup with secured hardware and simulated models, test scenarios and test data created to achieve our objective. For validation, we relied

Khatik, Gourab, Katragadda, Praveen, Labade, Chetan

5G Network Connectivity Automated Test and Verification for Autonomous Vehicles Using UAVs

2022-01-016803/29/2022

The significance and the number of vehicle safety features enabled via connectivity continue to increase. OnStar, with its automatic airbag notification, was one of the first vehicle safety features that demonstrate the enhanced safety benefits of connectivity. Vehicle connectivity benefits have grown to include remote software updates, data analytics to aid with preventative maintenance and even to theft prevention and recovery. All of these services require available and reliable connectivity. However, except for the airbag notification, none have strict latency requirements. For example, software updates can generally be postponed till reliable connectivity is available. Data required for prognostics use cases can be stored and transmitted at a later time. A new set of use cases are emerging that do demand continuous, reliable and low latency connectivity. For example, remote control of autonomous vehicles may be required in unique situations. Further, additional autonomous vehicle

Evans, Matthew, Talty, Tim, Dietrich, Carl, Gomez, Xavier

A Novel Vehicle-to-Vehicle Fast Charging Control System Utilizing Motor and Inverter in EV

2022-01-019403/29/2022

As electric vehicles become more widespread, such vehicles may be subject to “range anxiety” due to the risk of discharging during driving or the discharging when left unused for a long period. Accordingly, a vehicle equipped with a mobile charger that can provide a charge in an emergency. The vehicle with the mobile charger is usually composed of a large capacity battery, a power converter in a small truck. However, the large capacity battery and the power converter are disadvantageous in that they are large in size and expensive and should be produced as a special vehicle. In this paper, we propose a method to solve the problem using an internal EV system without requiring an additional power generation, battery and a charging-and-discharging device. The method is a novel Vehicle-to-Vehicle fast charging control system utilizing motor and inverter in EV.

Choi, Jaehyuk, Song, ByeongSeob, Kim, SungKyu, Kim, Hyunsup, Kim, Talchol, Kim, SeokJoon, Cho, InYoung, Choi, YoungGon

High-Fidelity Heavy-Duty Vehicle Modeling Using Sparse Telematics Data

2022-01-063503/29/2022

Heavy-duty commercial vehicles tend to have poor energy efficiency due to relatively high mass, poor aerodynamic characteristics, and high auxiliary power loads. These factors lead directly to high fuel cost per mile, perpetuate domestic dependence on imported oil, and contribute to climate change and environmental damage in the form of air pollution. The optimization of energy efficiency in these vehicles presents a way to reduce industry expenses while also taking action on the growing issue of climate change. Vehicle modeling provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption. This research focused on creating higher fidelity models using real-world, sparsely collected telematics data from a large fleet of heavy-duty vehicles. The largest challenge encountered in this research was the use of sparse data, where samples are sporadically collected, leading to infrequent sampling rate and

Carow, Kyle, Cantwell, Nathaniel, Ivanco, Andrej, Holden, Jacob, Baker, Chad, Miller, Eric, Asher, Zachary

Full Vehicle Global Modal Identification Based on Deep Neural Network

2021-01-111308/31/2021

Full vehicle modal identification is a major challenge for both experimental and simulated modal results. A global modal is usually masked by nearby local modes, so that even well-experienced engineers have difficulty to identify vehicle modes efficiently. Besides different vehicle configurations e.g. SUV, MPV and hatchback can make the challenge even greater, since the same modal for them will present different characteristics. This paper proposes a deep neural network for vehicle modal identification. This method takes advantage of the deep learning method which has achieved outstanding performance in language translation, computer vision and image processing. It also has the potentiality to improve modal identification efficiency. In general commercial neural network applications, a large number of data is available for training to achieve a robust output. However, the training data for a vehicle modal identification is always limited, as a result, training the network becomes

Lee, Zhen

Automated Driving System Data Logger

J3197_202107 (Current)

07/23/2021

This SAE Recommended Practice provides common data output formats and definitions for a variety of data elements that may be useful for analyzing the performance of automated driving system (ADS) during an event that meets the trigger threshold criteria specified in this document. The document is intended to govern data element definitions, to provide a minimum data element set, and to specify a common ADS data logger record format as applicable for motor vehicle applications. Automated driving systems (ADSs) perform the complete dynamic driving task (DDT) while engaged. In the absence of a human “driver,” the ADS itself could be the only witness of a collision event. As such, a definition of the ADS data recording is necessary in order to standardize information available to the accident reconstructionist. For this purpose, the data elements defined herein supplement the SAE J1698-1 defined EDR in order to facilitate the determination of the background and events leading up to a

Event Data Recorder Committee

Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles

J3216_202107 (Current)

07/16/2021

This document describes machine-to-machine (M2M) communication to enable cooperation between two or more participating entities or communication devices possessed or controlled by those entities. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle with driving automation feature(s) engaged. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of manually operated vehicles or pedestrians or cyclists carrying personal devices), or road operators (e.g., those who maintain or operate traffic signals or workzones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be used to influence (directly or indirectly) DDT performance by one or more nearby road users

Cooperative Driving Automation(CDA) Committee

Taxonomy and Definition of Safety Principles for Automated Driving System (ADS)

J3206_202107 (Current)

07/07/2021

This SAE Information Report classifies and defines a harmonized set of safety principles intended to be considered by ADS and ADS-equipped vehicle development stakeholders. The set of safety principles herein is based on the collection and analysis of existing information from multiple entities, reflecting the content and spirit of their efforts, including: SAE ITC AVSC Best Practices CAMP Automated Vehicle Research for Enhanced Safety - Final Report RAND Report - Measuring Automated Vehicle Safety: Forging a Framework U.S. DOT: Automated Driving Systems 2.0 - A Vision for Safety Safety First for Automated Driving (SaFAD) UNECE WP29 amendment proposal UNECE/TRANS/WP.29/GRVA/2019/13 On a Formal Model of Safe and Scalable Self-Driving Cars (Intel RSS model) SAE J3018 This SAE Information Report provides guidance for the consideration and application of the safety principles for the development and deployment of ADS and ADS-equipped vehicles. This SAE Information Report is not intended to

On-Road Automated Driving (ORAD) committee

Driver Identification Using Driving Behavior, Habits and Driver Characteristics

2021-01-018504/06/2021

In this paper, a driver identification scheme is studied using general driver inputs such as accelerating, braking and steering behavior, in addition to the settings related to driver’s physical characteristics, such as driver’s seat position. Several drivers are selected with various ages, genders and driving skills to participate in the study. Their driving data is collected using the same test vehicle, and while driving on the same routes. This helps eliminate the inherent vehicle to vehicle variations and the impact of the route differences and enables the identification algorithm to focus on the driving behavior. The driving routes are broken down into shorter segments where the driving features are calculated and populated in these segments. To reduce the identification bias towards certain rare events in the ride, the features are reset at the beginning of each trip segment. This additionally helps to ensure that there is no spill of feature values across the segments. Trip

Jalaliyazdi, Milad, Sheikh, Tooba, Bani Milhim, Alaeddin, Dixon, Regan, CHIM, Huong

Arrangement and Control Method of Cooperative Vehicle Platoon

2021-01-0113

04/06/2021

With the development of cellular communication technology and for the sake of reducing drag resistance, the multi-lane platoon technology will be more prosperous in the future. In this article, the cooperative vehicle platoon method on the public road is represented. The method’s architecture is mainly composed of the following parts: decision-making, path planning and control command generation. The decision-making uses the finite state machine to make decision and judgment on the cooperative lane change of vehicles, and starts to execute the lane change step when the lane change requirements are met. In terms of path planning, with the goal of ensuring comfort, the continuity of the vehicle state and no collision between vehicles, a fifth-order polynomial is used to fit every vehicle trajectory. In terms of control command generation module, a model predictive control algorithm is used to solve the multi-vehicle centralized optimization control problem. We use the two DOF vehicle

Chen, Guosheng, Wu, Jian, Li, Shuai, Zhang, Jinghua, Du, Zhiqiang, Wang, Guojun, Chen, Zhicheng

TCU Testing at the Heart of Vehicle Connectivity

21AVEP03_0803/01/2021

Adding a telematics control unit to the vehicle and keeping it operational demands a focused approach towards testing this vital component. The wireless connectivity between the modern vehicle and the outside world is possible thanks to a constantly evolving component called the telematics control unit (TCU). The TCU is at the center of all connectivity-related use cases, so its operation must be flawless. Closely related to the TCU are over-the-air tests (OTA), electromagnetic compatibility (EMC), electromagnetic interference (EMI) and automotive Ethernet. All should be kept in mind when designing the strategy to test TCUs. The TCU is loaded with multiple wireless technologies that enable the vehicle to exchange information with its surroundings. It is also connected to multiple components inside the vehicle, including other electronic control units and domain controls, via wired connections using automotive Ethernet and controller area network (CAN) bus. Connecting the vehicle to the

Kumar, Nikhil

Facility Focus: Rochester Institute of Technology

TBMG-3798711/01/2020

Rochester Institute of Technology (RIT) was founded in 1829 and adopted its current name in 1944. Today, in addition to its home in New York, RIT has partnerships on nearly every continent and overseas campuses located in China, Croatia, Dubai, and Kosovo.

Candidate Improvements to Dedicated Short Range Communications (DSRC) Message Set Dictionary [SAE J2735] Using Systems Engineering Methods

J3067_202010 (Current)

10/06/2020

This document is not a standard, it is a candidate for a standard being submitted to SAE for their consideration as a comment to SAE J2735. The term SAE J2735 SE candidate is used within this document to refer to this submission. This document specifies dialogs, messages, and the data frames and data elements that make up the messages specifically for use by applications intended to utilize the 5.9 GHz Dedicated Short Range Communications for Wireless Access in Vehicular Environments (DSRC/WAVE, referenced in this document simply as “DSRC"), communications systems. Although the scope of this Standard is focused on DSRC, these dialogs, messages, data frames and data elements have been designed, to the extent possible, to be of use for applications that may be deployed in conjunction with other wireless communications technologies. This standard therefore specifies the definitive message structure and provides sufficient background information to allow readers to properly interpret the

V2X Communications Steering Committee

Unsettled Issues Facing Automated Vehicles and Insurance

EPR202001508/05/2020

This SAE EDGE™ Research Report explores how the deployment of automated vehicles (AVs) will affect the insurance industry and the principles of liability that underly the structure of insurance in the US. As we trade human drivers for suites of sensors and computers, who (or what) is responsible when there is a crash? The owner of the vehicle? The automaker that built it? The programmer that wrote the code?Insurers have over 100 years of experience and data covering human drivers, but with only a few years’ worth of information on AVs – how can they properly predict the true risks associated with their deployment? Without an understanding of the nature and risks of AVs, how can the government agencies that regulate the insurance industry provide proper oversight? Do the challenges AVs present require a total reworking of our insurance and liability systems, or can our current structures be adapted to fit them with minor modifications?{"bold":"Unsettled Legal Issues Facing Automated

Williams, Ian

V2X Communications Message Set Dictionary™ Set

J2735SET_202007 (Historical)07/23/2020

This Abstract Syntax Notation (ASN.1) File is the precise source code used for SAE International Standard J2735. As part of an international treaty, all US ITS standards are expressed in "ASN.1 syntax". ASN.1 Syntax is used to define the messages or "ASN specifications". Using the ASN.1 specification, a compiler tool produces the ASN library which will then be used to produce encodings (The J2735 message set uses UPER encoding). The library is a set of many separate files that collectively implement the encoding and decoding of the standard. The library is then used by any application (along with the additional logic of that application) to manage the messages. The chosen ASN tool is used to produce a new copy of the library when changes are made, and it is then linked to the final application being developed. The ASN library manages many of the details associated with ASN syntax, allowing for subtle manipulation to make the best advantage of the encoding style. The J2735 Standard

V2X Core Technical Committee

V2X Communications Message Set Dictionary

J2735_202007 (Historical)

07/23/2020

This SAE standard specifies a message set, and its data frames and data elements, for use by applications that use vehicle-to-everything (V2X) communications systems. While the data dictionary was originally designed for use over DSRC, this document is intended to be independent of the underlying communications protocols used to exchange data between participants in V2X applications.

V2X Core Technical Committee

V2X Communications Message Set Dictionary™ ASN file

J2735ASN_202007 (Historical)07/23/2020

V2X Core Technical Committee

Vehicle Level Validation Test Procedures for V2V Safety Communications

J2945/1A_202007 (Current)07/08/2020

This document provides vehicle-level data collection, data analysis, and data verification procedures that may be used to verify that an instrument under test (IUT) satisfies the vehicle-level requirements specified in the SAE International (SAE) J2945/1 standard. For the purposes of this recommended practice, “vehicle-level requirements” primarily consist of those requirements which can be verified external to the vehicle. The IUT for these procedures is a configured dedicated short range communications (DSRC) vehicle-to-vehicle (V2V) device as defined in SAE J2945/1 and is installed on a light vehicle. While the IUT is conceptually separated from the vehicle it is installed on, the tests outlined in this document are primarily vehicle-level so the terms “vehicle” and “IUT” can generally be considered interchangeable. Additionally, non-vehicle-level complementary tests, not included in this document, are required to verify that the entire set of requirements specified in SAE J2945/1

V2X Core Technical Committee

New SAE Standard for Automated-Driving Developers

20AVEP07_0407/01/2020

Developed in less than a year, SAE's new J3216 standard will impact traffic management, operations and safety for automated mobility. In May, amid quarantine and lockdowns for the Coronavirus pandemic, SAE International published SAE J3216 Standard: Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles. The new standard provides clarity to support advancement of full automation. SAE J3216 builds on 2016's SAE J3016 that defines six levels of driving automation, from SAE Level Zero (no automation) to SAE Level 5 (full vehicle autonomy). While J3016 aims to provide clarity and offer a foundation for advancing vehicle automation technologies, the newly-published J3216 does so with terms, definitions and taxonomy focused on Cooperative Driving Automation (CDA), a key building block that supports all levels of automation.

Shuttleworth, Jennifer

Motor Vehicle Seat Dimensions

J2732_202007-TEST-REC (Historical)07/01/2020

This document provides dimensional definitions that facilitate geometric quantification and evaluation of seats. Linear, radial, and angular surface dimensions included in this document are intended to approximate shape characteristics based on defined points of interest and not as a method needed to reproduce complex surface contours. In many cases, other points across the seat surface shape may exceed or not reach the boundary defined by these simple geometric definitions. Dimensions described in this document have been designed to be measured in a CAD environment; however, many dimensions require the HPD position and attitude. This can be obtained by physically establishing H-point using benchmark or auditing procedures OR by measuring the HPD within a CAD or modelling system. Refer to the appropriate document for these procedures. Three types of seat geometry reference points and measurements have been developed: 1 Simple reference points and measurements not related to H-point. 2

Human Accom and Design Devices Stds Comm

Aircraft Ground Deicing/Anti-Icing Training and Qualification Program

AS6286B (Historical)

06/11/2020

This document establishes the minimum training and qualification requirements for ground-based aircraft deicing/anti-icing methods and procedures. All guidelines referred to herein are applicable only in conjunction with the applicable documents. Due to aerodynamic and other concerns, the application of deicing/anti-icing fluids shall be carried out in compliance with engine and aircraft manufacturers’ recommendations. The scope of training should be adjusted according to local demands. There are a wide variety of winter seasons and differences of the involvement between deicing operators, and therefore the level and length of training should be adjusted accordingly. However, the minimum level of training shall be covered in all cases. As a rule of thumb, the amount of time spent in practical training should equal or exceed the amount of time spent in classroom training.

G-12T Training and Quality Programs Committee