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Unsettled Impacts of Integrating Automated Electric Vehicles into a Mobility-as-a-Service Ecosystem and Effects on Traditional Transportation and Ownership

International Transportation Innovation Center (ITIC)-Joachim Taiber
  • Research Report
  • EPR2019004
Published 2019-12-20 by SAE International in United States
The current business model of the automotive industry is based on individual car ownership, yet new ridesharing companies such as Uber and Lyft are well capitalized to invest in large, commercially operated, on-demand mobility service vehicle fleets. Car manufacturers like Tesla want to incorporate personal car owners into part-time fleet operation by utilizing the company’s fleet service. These robotaxi fleets can be operated profitably when the technology works in a reliable manner and regulators allow driverless operation.Although Mobility-as-a-Service (MaaS) models of private and commercial vehicle fleets can complement public transportation models, they may contribute to lower public transportation ridership and thus higher subsidies per ride. This can lead to inefficiencies in the utilization of existing public transportation infrastructure. MaaS platforms can also cause a reduced reliance on parking infrastructure (e.g., street parking lanes and parking garages) which can contribute to an improvement in overall traffic flow, and a reduction in capital investment for commercial and residential real-estate development. Urban planning can be better centered around the true mobility needs of the citizens without sacrificing valuable…
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Semiconductor Safety Concepts for the Power Distribution of Automated Driving

SAE International Journal of Connected and Automated Vehicles

Infineon Technologies AG, Germany-Stefan Schumi
University of Technology Graz, Austria-Daniel Watzenig
  • Journal Article
  • 12-02-04-0017
Published 2019-12-18 by SAE International in United States
Automated driving is a highly complex idea. It involves mechanics, electronics and chemistry, artificial intelligence, human intelligence and high computational efforts. Apart from those aspects, the automated intelligence is run using electricity. An unintended interrupt can easily lead to a hazard. Therefore, a highly reliable power distribution has to be developed. This work focuses on the reliability calculation of such a power distribution concept. It points out what is required and will be in future such that the algorithms for the path planning and control are running in a safe environment according to the ISO 26262 standard.
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Virtual Assessment of Automated Driving: Methodology, Challenges, and Lessons Learned

SAE International Journal of Connected and Automated Vehicles

BMW Group, Germany-Korbinian Groh
BMW of North America, USA-Thomas Kühbeck
  • Journal Article
  • 12-02-04-0020
Published 2019-12-18 by SAE International in United States
Automated driving as one of the most anticipated technologies is approaching its market release in the near future. Since several years, the research in the automotive industry is largely focused on its development and presents well-engineered prototypes. The many aspects of this development do not only concern the function and its components itself, but also the proof of safety and assessment for its market release. It is clear that previous methods used for the release of Advanced Driver Assistance Systems are not applicable. In contrast to already released systems, automated driving is not restricted to a certain field of application in terms of driving scenarios it has to take action in. This results in an infeasible amount of required testing and unforeseeable scenarios the function can face throughout its lifetime. In this article, we show a scenario-based approach that promises to overcome those challenges. In contrast to previous methods, it includes virtual test domains in a verified way to diminish the demand for real-world testing. Local verification of certain scenarios from real-world testing enables virtual…
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Open Access
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Pedestrian Collision Avoidance System for Autonomous Vehicles

SAE International Journal of Connected and Automated Vehicles

Virtual Vehicle Research Center and Graz University of Technology, Austria-Daniel Watzenig
Virtual Vehicle Research Center, Austria-Markus Schratter, Michael Hartmann
  • Journal Article
  • 12-02-04-0021
Published 2019-12-18 by SAE International in United States
Advanced driver assistance systems (ADAS) are state of the art in modern vehicles (SAE level 1-2). They support the driver and improve thereby the vehicle safety during manual driving. In critical situations, collision avoidance systems warn the driver or trigger an autonomous emergency braking maneuver to mitigate or avoid a collision. Also, automated driving vehicles (SAE level 3+) must be able to avoid critical situations and must be more capable than currently available systems. During automated driving, the vehicle is responsible for the driving task instead of the driver. Therefore, safe automated driving requires robust algorithms to avoid collisions with other traffic participants in every situation, especially in critical situations with pedestrians and a limited perception ability. In this work, we investigate how automated driving vehicles can handle critical situations with pedestrians on multilane roads with an emergency braking or evasion maneuver. We focus in detail on very critical situations, where pedestrians are crossing behind an occluded area, e.g. from behind a parked car on the side of the road. In these critical situations, a…
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Application Study of Blind Spot Monitoring System Realized by Monocular Camera with CNN Depth Cues Extraction Approach

SAE International Journal of Connected and Automated Vehicles

Jiangsu Chaoli Electric Co., Ltd., China-Chuyo Kaku
Tokyo Institute of Technology, Japan-Yuxiang Guo, Itsuo Kumazawa
  • Journal Article
  • 12-02-04-0016
Published 2019-12-17 by SAE International in United States
The image from monocular camera is processed to detect depth information of the obstacles viewed by the rearview cameras of vehicle door side. The depth information recognized from a single, two-dimensional image data can be used for the purpose of blind spot area detection. Blind spot detection is contributing to enhance the vehicle safety in scenarios such as lane-change and overtaking driving. In this article the depth cue information is inferred from the feature comparison between two image blocks selected within a single image. Convolutional neural network model trained by deep learning process with good enough accuracy is applied to distinguish if an obstacle is far or near for a specified threshold in the vehicle blind spot area. The application study results are demonstrated by the offline calculations with real traffic image data.
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Unsettled Issues in Determining Appropriate Modeling Fidelity for Automated Driving Systems Simulation

Silicon Valley Mobility-Sven Beiker
  • Research Report
  • EPR2019007
Published 2019-12-06 by SAE International in United States
This SAE EDGE™ Research Report identifies key unsettled issues of interest to the automotive industry regarding the challenges of achieving optimal model fidelity for developing, validating, and verifying vehicles capable of automated driving. Three main issues are outlined that merit immediate interest:First, assuring that simulation models represent their real-world counterparts, how to quantify simulation model fidelity, and how to assess system risk.Second, developing a universal simulation model interface and language for verifying, simulating, and calibrating automated driving sensors.Third, characterizing and determining the different requirements for sensor, vehicle, environment, and human driver models.SAE EDGE™ Research Reports are preliminary investigations of new technologies. The three technical issues identified in this report need to be discussed in greater depth with the aims of, first, clarifying the scope of the industry-wide alignment needed; second, prioritizing the issues requiring resolution; and, third, creating a plan to generate the necessary frameworks, practices, and protocols.NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of…
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Identifying Automated Driving Systems-Dedicated Vehicles (ADS-DVs) Passenger Issues for Persons with Disabilities

On-Road Automated Driving (ORAD) committee
  • Ground Vehicle Standard
  • J3171_201911
  • Current
Published 2019-11-19 by SAE International in United States
It is expected that Level 4 and 5 automated driving systems-dedicated vehicles (ADS-DVs) will eventually enable persons to travel at will who are otherwise unable to obtain a driver's license for a conventional vehicle, namely, persons with certain visual, cognitive, and/or physical impairments. This information report focuses on these disabilities, but also provides guidance for those with other disabilities. This report is limited to fleet operated on-demand shared mobility scenarios, as this is widely considered to be the first way people will be able to interact with ADS-DVs. To be more specific, this report does not address fixed route transit services or private vehicle ownership. Similarly, this report is focused on road-worthy vehicles; not scooters, golf carts, etc. Lastly, this report does not address the design of chair lifts, ramps, or securements for persons who use wheeled mobility devices (WHMD) (e.g., wheelchair, electric cart, etc.), as these matters are addressed by other committees within SAE International.
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AVSC Best Practice for In-Vehicle Fallback Test Driver Selection, Training, and Oversight Procedures for Automated Vehicles Under Test

Automated Vehicle Safety Consortium
  • Best Practice
  • AVSC00001201911
  • Current
Published 2019-11-08 by SAE Industry Technologies Consortia in United States

ABSTRACT

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Editorial

Autonomous Vehicle Engineering: December 2019

Editorial Director-Bill Visnic
  • Magazine Article
  • 19AVEP11_01
Published 2019-11-01 by SAE International in United States

After digesting some of the fascinating insights from surveys taken of automated-vehicle test riders - most of them first-timers - who participated in SAE International's Demo Days program (see pg. 31), you've got to conclude that the only thing predictable about the new-mobility landscape is its unpredictability.

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Mapping Canada - Centimeter by Centimeter

Autonomous Vehicle Engineering: December 2019

Sebastian Blanco
  • Magazine Article
  • 19AVEP11_05
Published 2019-11-01 by SAE International in United States

A Montreal-based company leverages artificial intelligence to take on the task of developing high-definition maps of Canada.

Fully-automated vehicles will only be as smart as the datasets they use to determine their driving pathways. Jakarto Cartographie 3D, a young company based in Montreal, Canada, is working on artificial-intelligence (AI)-powered, high-definition (HD) maps that it claims offer 2-3 cm (.787- to 1.2-inch) absolute precision and relative precision measured in millimeters. In other words, better maps that will allow for better automated vehicles (AVs).

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