Automated vehicles require some level of subsystem redundancy, whether to allow a transition time for driver re-engagement (L3) or continued operation in a faulted state (L4+). Highly automated vehicle developers need to have safe miles accumulated by vehicles to assess system maturity and experience new environments. This article presents a conceptual framework suggesting that hardware newly available to commercial vehicle application can be used to form a steering system that will remain operational upon a failure. The key points of a provisional safety case are presented, giving hope that a complete safety case is possible. This article will provide autonomous vehicle developers a view of a near term possibility for a highly automated commercial vehicle steering solution.

Pandy, Ananda, Pathuri, Nagamani, Salunke, Pranav, Subba, Srujana Sree, Williams, Dan

TAKING AUTONOMY TO THE NEXT LEVEL

20TOFHP10_0210/01/2020

More work still needs done on AI, reaction strategies and assessing technology readiness before L4 trucks are market ready. Despite a pandemic that continues to punish global economic markets, not to mention vehicle manufacturers' and suppliers' product plans, many heavy-duty truck makers are not relaxing on efforts to bring high-level (SAE Level 4 or 5) autonomous trucks to market. Partnerships with self-driving tech startups can be a big part of the roadmap, the latest example coming in mid-July as Navistar announced it will co-develop SAE L4 trucks, targeted for production by 2024, with San Diego-based TuSimple. The producer of International commercial trucks and IC Bus school and commercial buses also has taken a minority stake in TuSimple. These moves follow a two-year “technical relationship” with the startup, which operates a U.S. fleet of 40 self-driving trucks that move freight between Arizona and Texas for companies like UPS and McLane Company. TuSimple plans to demonstrate

Gehm, Ryan

Autonomy is Coming to Commercial Vehicles

20AVEP09_1009/01/2020

But not without its share of technical and implementation hurdles - and tech-startup casualties. Despite a pandemic that continues to punish global economic markets, not to mention vehicle manufacturers' and suppliers' product plans, many heavy-duty truck makers are not relaxing on efforts to bring to market high-level (SAE Level 4 or 5) autonomous trucks. Partnerships with self-driving tech startups can be a big part of the roadmap, the latest example coming in mid-July as Navistar announced it will co-develop SAE L4 trucks, targeted for production by 2024, with San Diego-based TuSimple. The producer of International commercial trucks and IC Bus school and commercial buses also has taken a minority stake in TuSimple. These moves follow a two-year “technical relationship” with the startup, which operates a U.S. fleet of 40 self-driving trucks that moves freight between Arizona and Texas for companies like UPS and McLane Company. TuSimple plans to demonstrate completely driverless

Gehm, Ryan

Training Data-hungry AI Algorithms

20AVEP09_0509/01/2020

Large-scale data refinement is key to bringing more sophisticated automated-driving functions to series production. Training the algorithms of Artificial Intelligence (AI)-based systems for autonomous or highly automated driving requires enormous volumes of data to be captured and processed. The algorithms must be able to master numerous challenges so that self-driving cars can detect all essential details of their environment, make the right decisions and safely take people to their destination. Why does training require this much data? AI-based systems enable quick progress, but this progress slows down after a certain point. It must be ensured that systems can also sensibly and reliably handle rare events. Bringing sophisticated AI-based driving functions to the road safely therefore requires a growing amount of ever higher-quality data.

Rödler, Daniel

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

Making Data Logging, Replay and Prototyping More Efficient

20AVEP07_0507/01/2020

High levels of continuity and compatibility are vital to avoid interruptions in the development process - and reduce cost. The development of advanced driver assistance (ADAS) and highly automated driving systems poses great challenges for OEM and supplier engineers. It is important, therefore, that powerful, flexibly configurable development systems optimally support the various use cases in data-driven development to make it as efficient as possible. In the case of functionality for both ADAS autonomous driving, the chain of effects can be divided into several steps, starting with environment detection with the aid of extensive sensor technology - including camera, radar, lidar and ultrasonics - and ending with the final implementation of the driving strategy and actuator control (Fig. 2).

Mertens, Frank, Müller, Marius

Remote Control Reduces Risk

20AVEP05_1105/01/2020

Caterpillar's new electro-hydraulic platform is ready for expanded automated and remote-operation technologies. Remote control for heavy construction and earthmoving machines was a major focus area across Caterpillar product groups at the ConExpo-Con/Agg 2020 tradeshow in Las Vegas. Cat Command, the term it uses for remote control and other automated capabilities, now is available for select machines: D6 and D7 dozers; 320-340 next-generation excavators and a variety of wheel loaders. “Applications where you're putting that machine in harm's way. You're trying to lower that risk level for the operator,” said product application specialist Brian Stellbrink, during a ConExpo press event. For example, machines operating in ship holds or in high-risk environments for remediation or demolition work. “Whether it's out of the factory or added to an existing machine, that's key. It's a kit that you can put on a machine if and when a customer decides that's the route they want to go.”

Gehm, Ryan

AVSC Best Practice for Describing an Operational Design Domain: Conceptual Framework and Lexicon

AVSC0000220200404/15/2020

An ADS-operated vehicle’s operational design domain (ODD) is defined by the manufacturer based on numerous factors. Research is underway at other organizations to define and organize ODD elements into taxonomies and other relational constructs. In order to enhance collaboration and communication between manufacturers and developers and transportation authorities, common terms and consistent frameworks are needed. The conceptual framework presented by Automated Vehicle Safety Consortium establishes a lexicon that can be used consistently by ADS developers and manufacturers responsible for defining their ADS ODD. A common framework and lexicon will reduce confusion, align expectations, and therefore build public trust, acceptance, and confidence. The guidance in this document is intended for: The technical community (e.g. manufacturers and developers) Public agencies (e.g. regulatory authorities) Infrastructure owner-operators The public This document, Best Practice for Describing an

Automated Vehicle Safety Consortium

Defining Fundamental Vehicle Actions for the Development of Automated Driving Systems

2020-01-071204/14/2020

Automated Driving Systems (ADSs) show great potential to improve our transport systems. Safety validation, before market launch, is challenging due to the large number of miles required to gather enough evidence for a proven in use argumentation. Hence there is ongoing research to find more effective ways of verifying and validating the safety of ADSs. It is crucial both for the design as well as the validation to have a good understanding of the environment of the ADS. A natural way of characterizing the external conditions is by modelling and analysing data from real traffic. Towards this end, we present a framework with the primary ultimate objective to completely model and quantify the statistically relevant actions that other vehicles conduct on motorways. Two categories of fundamental actions are identified by recognising that a vehicle can only move longitudinally and laterally. The fundamental actions are defined in detail to create a set that is collectively exhaustive and

Gyllenhammar, Magnus, Zandén, Carl, Törngren, Martin

Perceptions of Two Unique Lane Centering Systems: An FOT Interview Analysis

2020-01-010804/14/2020

The goal of this interview analysis was to explore and document the perceptions of two unique lane centering systems (S90’s Pilot Assist and CT6’s Super Cruise). Both systems offer a similar type of functionality (adaptive cruise control and lane centering), but have significantly different design philosophies and HMI (Human-Machine Interface) implementations. Twenty-four drivers drove one of the two vehicle models for a month as part of a field operational test (FOT) study. Upon vehicle return, drivers took part in a 60-minute semi-structured interview covering their perceptions of the vehicle’s various advanced driver-assistance systems (ADAS). Transcripts of the interviews were coded by two researchers, who tagged each statement with relevant system and perception code labels. For analysis, the perception codes were grouped into larger thematic bins of safety, comfort, driver attention, and system performance. Perceptions of adaptive cruise control (ACC) were similar across vehicles

Landry, Steven, Seppelt, Bobbie, Russo, Luca, Mehler, Bruce, Angell, Linda, Gershon, Pnina, Reimer, Bryan

Human-Driving Highway Overtake and Its Perceived Comfort: Correlational Study Using Data Fusion

2020-01-103604/14/2020

As an era of autonomous driving approaches, it is necessary to translate handling comfort - currently a responsibility of human drivers - to a vehicle imbedded algorithm. Therefore, it is imperative to understand the relationship between perceived driving comfort and human driving behaviour. This paper develops a methodology able to generate the information necessary to study how this relationship is expressed in highway overtakes. To achieve this goal, the approach revolved around the implementation of sensor Data Fusion, by processing data from CAN, camera and LIDAR from experimental tests. A myriad of variables was available, requiring individuating the key-information and parameters for recognition, classification and understanding of the manoeuvres. The paper presents the methodology and the role each sensor plays, by expanding on three main steps: Data segregation and parameter selection; Manoeuvre detection and processing; Manoeuvre classification and database generation. It

Carello, Massimiliana, Ferraris, Alessandro, de Carvalho Pinheiro, Henrique, Cruz Stanke, Diego, Gabiati, Giovanni, Camuffo, Isabella, Grillo, Massimo

Modes of Automated Driving System Scenario Testing: Experience Report and Recommendations

2020-01-120404/14/2020

With the widespread development of automated driving systems (ADS), it is imperative that standardized testing methodologies be developed to assure safety and functionality. Scenario testing evaluates the behavior of an ADS-equipped subject vehicle (SV) in predefined driving scenarios. This paper compares four modes of performing such tests: closed-course testing with real actors, closed-course testing with surrogate actors, simulation testing, and closed-course testing with mixed reality. In a collaboration between the Waterloo Intelligent Systems Engineering (WISE) Lab and AAA, six automated driving scenario tests were executed on a closed course, in simulation, and in mixed reality. These tests involved the University of Waterloo’s automated vehicle, dubbed the “UW Moose”, as the SV, as well as pedestrians, other vehicles, and road debris. Drawing on both data and the experience gained from executing these test scenarios, the paper reports on the advantages and disadvantages of the

Antkiewicz, Michał, Kahn, Maximilian, Ala, Michael, Czarnecki, Krzysztof, Wells, Paul, Acharya, Atul, Beiker, Sven

Runtime Active Safety Risk-Assessment of Highly Autonomous Vehicles for Safe Nominal Behavior

2020-01-010704/14/2020

Fatal crashes involving automated driving systems, has been raising the concern of minimum standard requirement for safety, reliability and performance required for Autonomous Driving System (ADS)/Advanced Driver Assistance System (ADAS) before this cutting-edge technology takes on public roads. Hence, in order to ensure necessary safety requirements of ADS/ADAS systems we propose a runtime active safety assurance module known as SConSert. SConSert performs dynamic risk assessment of “Sensing, Planning and Action module of ADS/ADAS”; to provide minimal risk maneuver in any given driving scenario. The dynamic risk assessment of ADS/ADAS system is based on the operational design domain (ODD) knowledge of the driving scenario plus the sensor capability, ADS/ADAS algorithm requirement and capability, and finally smooth and collision free maneuver requirement. So, the main concept behind SConSert is runtime derivation of situational and conditional set of contracts for a given driving

Rathour, Swarn Singh, Ishigooka, Tasuku, Otsuka, Satoshi, MARTIN, RAUL

Driving Safety Performance Assessment Metrics for ADS-Equipped Vehicles

2020-01-120604/14/2020

The driving safety performance of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using metrics in order to be able to assess the driving safety performance and compare it to that of human-driven vehicles. In this research, driving safety performance metrics and methods for the measurement and analysis of said metrics are defined and/or developed. A comprehensive literature review of metrics that have been proposed for measuring the driving safety performance of both human-driven vehicles and AVs was conducted. A list of proposed metrics, including novel contributions to the literature, that collectively, quantitatively describe the driving safety performance of an AV was then compiled, including proximal surrogate indicators, driving behaviors, and rules-of-the-road violations. These metrics, which include metrics from on- and off-board data sources, allow the driving safety performance of an AV to be measured in a variety of situations, including crashes

Wishart, Jeffrey, Como, Steven, Elli, Maria, Russo, Brendan, Weast, Jack, Altekar, Niraj, James, Emmanuel

The Effects of Small Seat Swiveling Angles on Occupant Responses during a Frontal Impact

2020-01-057104/14/2020

In highly automated vehicles (HAVs), new seat configurations may be desirable to allow occupants to perform new activities. One of the current HAV concepts is the swiveled seat layout, which might facilitate communication between occupants. The main objective of this study was to investigate the effects of seat swiveling angles on occupant kinematics and injury risk predicted by a Human Body Model (HBM) during a frontal impact. A detailed 50th percentile male HBM (GHBMC M50-O) was subjected to two frontal crash pulses in a sled setup. The model was positioned on a semi-rigid seat and restrained using a pre-inflated airbag and a three-point seatbelt. Simulations included four seat swiveling angles (0, -10, -20, and -30 degrees), three occupant positions (Sedan driver, large VAN driver or Laptop user), two airbag initial locations (nominal or matching the head Y location), and the inclusion of lateral supports on the seat pan. The effects of the seat swiveling angle were similar for all

Grébonval, Cyrille, Trosseille, Xavier, Petit, Philippe, Wang, Xuguang, Beillas, Philippe

The Design of Safe-Reliable-Optimal Performance for Automated Driving Systems on Multiple Lanes with Merging Features

2020-01-012204/14/2020

Safety function for automated driving systems including advanced driver assistance systems and autonomous vehicle systems is very important. Inside safety function, predictive judge sub-function should be designed with the consideration of more and more penetration of automated driving vehicles. This paper presents the design on multiple lanes with merging features based on the author's previous Patent JP2019-147944 using predictive time-head-way and time-to-collision maps. In the author's previous work (Model Predictive Control for Hybrid Electric Vehicle Platooning Using Slope Information-Published on IEEE Transactions on Intelligent Transportation Systems), a model predictive control framework was designed. Due to the difficulty to detail the sub-safety function deeply with merging features, few works are found to deal with sensor platforms focusing on rear side, and situations of merging lane side with the consideration of relative relation variations with other vehicles and road

Yu, Kaijiang

Unsettled Issues Concerning Semi-Automated Vehicles: Safety and Human Interactions on the Road to Full Autonomy

EPR202000102/28/2020

Across the span of the SAE International-defined Levels of Driving Automation, human drivers occupy a diverse range of responsibilities and authority on the vehicle movement and the monitoring of the outside environment. From both a technological and a regulatory perspective, there is a gap that divides lower levels of automation (L1 through L3) and higher levels of automation (L4 and L5). For those vehicles that require the cooperation between a human driver and the autonomous technology, it is important to ascertain the safety consequences of such a design choice. It is also important to understand what the transition between automated driving and manual driving entails for the human driver, as well as for the surrounding traffic. This SAE EDGE™ Research Report investigates unsettled issues concerning what is commonly referred to as “semi-automation,” including an overview of the role of human drivers, the quantification of the “transition-to-manual” problem, the role played by L3

Favaro, Francesca

Takeover Level of Autonomous Vehicle Based on Driver’s Neuromuscular Characteristic

2020-01-5017

02/24/2020

Taking over vehicle control from a Level 3 conditionally automated vehicle can be a demanding task for a driver, to which great research effort has been contributed in recent years. Nevertheless, more attention should be given to the following aspects. The present research of take-over either only considers the influence of drivers’ visual and second task in single scenarios. However, the drivers’ NMS (Neuromuscular) characteristic hasn’t been investigated yet, especially in complex traffic scenarios. In this paper, a take-over experiment with complex traffic scenarios are conducted to observe the state of vehicle state and arm’ EMG (Electromyography) signal. After that, the driving styles are recognized based on the experimental data. Finally, a take-over level with driving style is proposed by clustering based on the condition of human-vehicle-road.

Hanbing, Wei, Yanhong, Wu, Yuxuan, Zhang, Rui, Xu

Superior Interiors

20AVEP01_0301/01/2020

New-mobility thinking is changing how vehicle interiors will work for everyone. Future-vehicle interior renderings have always stoked our imaginations for what may be possible in the cabin environment. Automated vehicle (AV) developments, including high-function driver assistance features and mobility as a service (MaaS), are bringing new dimensions to those possibilities. Ironically, while near-term prospects for privately-owned autonomous (SAE Level 4) AVs have hit the hype curve's downward slope, research and development into how passengers will interact with new-mobility technology promises interior designs and features that will likely be on the market long before most consumers experience self-driving cars.

Visnic, Bill

Unsettled Issues in Balancing Virtual, Closed-Course, and Public-Road Testing of Automated Driving Systems

EPR201901112/19/2019

This SAE EDGE™ Research Report identifies key unsettled issues of interest to the automotive industry regarding the challenges of determining the optimal balance for testing automated driving systems (ADS). Three main issues are outlined that merit immediate interest: First, determining what kind of testing an ADS needs before it is ready to go on the road. Second, the current, optimal, and realistic balance of simulation testing and real-world testing. Third, the challenges of sharing data in the industry. SAE EDGE™ Research Reports are preliminary investigations of new technologies. The three technical issues identified in this report should 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

Beiker, Sven

Autonomous Vehicles Scenario Testing Framework and Model of Computation

12-02-04-001512/18/2019

Autonomous Vehicle (AV) technology has the potential to fundamentally transform the automotive industry, reorient transportation infrastructure, and significantly impact the energy sector. Rapid progress is being made in the core artificial intelligence engines that form the basis of AV technology. However, without a quantum leap in testing and verification, the full capabilities of AV technology will not be realized. Critical issues include finding and testing complex functional scenarios, verifying that sensor and object recognition systems accurately detect the external environment independent of weather conditions, and building a regulatory regime that enables accumulative learning. The significant contribution of this article is to outline a novel methodology for solving these issues by using the Florida Poly AV Verification Framework (FLPolyVF).

Alnaser, Ala Jamil, Akbas, Mustafa Ilhan, Sargolzaei, Arman, Razdan, Rahul

Pedestrian Collision Avoidance System for Autonomous Vehicles

12-02-04-002112/18/2019

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

Schratter, Markus, Hartmann, Michael, Watzenig, Daniel

Unsettled Issues in Determining Appropriate Modeling Fidelity for Automated Driving Systems Simulation

EPR2019007

12/06/2019

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

Beiker, Sven

Q&A

19TOFHP12_1312/01/2019

Together with its many partners, ZF supplies camera and radar technology and advanced components for both the passenger car and commercial truck markets, the latter being especially suited for the move to more complex driver-assistance systems, according to Dan Williams, director of ADAS & Autonomy at ZF. “The business case in commercial vehicle for reduction in driver hours of service, fuel cost reduction and safety have strong economic incentives to adopt ADAS/automated driving technology. Additionally, the regulations placed on the industry will require our customers to utilize certain solutions,” he said. ZF is working on both highly automated “revolutionary” systems and on “evolutionary” driver-assistance systems that are increasingly complex, he said, citing the supplier's OnTraX lane keep assist that will launch in 2020 with its first major OEM customer. Williams spoke with TOHE at the recent NACV Show in Atlanta, and he's scheduled to participate in a Commercial Vehicle Safety

Gehm, Ryan

V2X Standardization in Over-the-Air Solution

2019-28-243511/21/2019

Today in-vehicle software is growing significantly resulting into increase in importance of over-the-air software/firmware updates dramatically. Various suppliers are providing the update-over-the-air solution in the automotive sector. Due to limited standardization in solution, each supplier needs more customization for specific customer. This paper is to bring in the required standardization in V2X resulting into ‘vehicle on-board components’ as one solution to multiple customer. “Update over-the-air improved solution through various standardization resulting into ‘vehicle on-board components’ as the product”

Kumaraswamy, Pavithra

Identifying Automated Driving Systems-Dedicated Vehicles (ADS-DVs) Passenger Issues for Persons with Disabilities

J3171_201911 (Current)11/19/2019

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

On-Road Automated Driving (ORAD) committee

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