Browse Topic: Level 4 (High driving automation)

Items (112)

J3197_202107 (Current)

7/23/2021

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 collision in an ADS-operated vehicle. The data elements defined in this document are unique to Level 3, 4, or 5 ADS features, as defined by SAE J3016, and provide additional background of the events leading up to a crash or crash-like event. The data from sensors such as camera(s), LiDAR(s) etc. will provide information in the absence of a human driver. The data included in the ADS data logger is expected to be used in conjunction with the SAE J1698 event data recorder (EDR) record and traditional accident reconstruction analysis. The EDR and ADS data logger will capture information leading up to the triggered event, at a minimum. There are no facts to support that recording data for greater than 5 seconds pre-event would change the outcome of any crash analysis. Thus, the recommended recording duration for a data logger is 5 seconds pre-event, same as an EDR. Due to the potential for sensor and/or communication failure during a crash event, the recommendation is that data should be collected post-crash for impact and rollover sensors for up to 250 ms. ADS technology is still being developed and is not yet commercially deployed. Therefore, this SAE Recommended Practice is intended as a guide toward standard practice and is subject to change to keep pace with experience and technical advances.

Event Data Recorder Committee

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

J3216_202107 (Current)

7/16/2021

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. Vehicles and infrastructure elements engaged in cooperative automation may share information, such as state (e.g., vehicle position, signal phase), intent (e.g., planned vehicle trajectory, signal timing), or seek agreement on a plan (e.g., coordinated merge). Cooperation among multiple participants and perspectives in traffic can improve safety, mobility, situational awareness, and operations. However, nothing in this document is intended to suggest that driving automation requires such cooperation in order to be performed safely. Cooperative strategies may be enabled by the sharing of information in a way that meets the needs of a given application. The needs may be expressed in terms of performance characteristics, such as latency, transmission mode (e.g., one-way, two-way), range, privacy and security, and information content and quality. There are several potential technologies for communicating information between the subject vehicle and other participants. This document focuses on application-oriented functionality and does not imply the need for or require any specific functionality associated with communications protocols or the open systems interconnection model layers in a protocol stack. This document addresses the operational and tactical timescales of dynamic driving on ADS-operated vehicles, and excludes strategic functions such as trip scheduling and selection of destinations and waypoints. This information report is intended to facilitate communication and awareness for the design and anticipated development and validation of cooperative driving automation.

Cooperative Driving Automation(CDA) Committee

AVSC Information Report for Adapting a Safety Management System (SMS) for Automated Driving System (ADS) SAE Level 4 and 5 Testing and Evaluation

AVSC000072021077/16/2021

This AVSC Information Report for Adapting a Safety Management System (SMS) for Automated Driving System (ADS) SAE Level 4 and 5 Testing and Evaluation (AVSC000007202107) shares information on a Safety Management System (SMS) framework in the context of ADS testing and evaluation operations. This report was developed to provide ADS organizations information about the role of organizational safety, which may be considered in the ADS testing and evaluation process. The SMS framework represents a method used in non-automotive industries (e.g., aviation, rail, nuclear) with the goal of enhancing an organization’s operational safety performance. A Safety Management System (SMS) is one approach designed to support organizational safety in a systematic and integrated way. The SMS framework is intended to promote a positive safety culture, assess and manage safety risk, evaluate risk control effectiveness, and support organizational safety policies and objectives. This information report affords organizations another safety management approach to add to their ADS test operations.

Automated Vehicle Safety Consortium

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

J3206_202107 (Current)

7/7/2021

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 encompass all aspects of system-level safety for an ADS-equipped vehicle, including communication with other traffic participants. Addressing all identified safety principles is intended to support, but not fully ensure, comprehensive system-level safety. As an SAE Information Report, this document is non-normative, imposes no requirements, and does not address: Requirements for methodology, metrics, and/or acceptance thresholds. Ethics-related safety principles, or any link between the safety principles defined in this document and ethical studies/frameworks. Conformance with safety principles for purposes of liability and/or fault assignment. As ADS technology and deployment are expanded in the future, this document may be reconsidered for future revision including normative requirements.

On-Road Automated Driving (ORAD) committee

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

J3016_202104 (Current)

4/30/2021

This document describes [motor] vehicle driving automation systems that perform part or all of the dynamic driving task (DDT) on a sustained basis. It provides a taxonomy with detailed definitions for six levels of driving automation, ranging from no driving automation (Level 0) to full driving automation (Level 5), in the context of [motor] vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on roadways: Level 0: No Driving Automation Level 1: Driver Assistance Level 2: Partial Driving Automation Level 3: Conditional Driving Automation Level 4: High Driving Automation Level 5: Full Driving Automation These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of driving automation features equipped on [motor] vehicles in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit public access) that collectively serve all road users, including cyclists, pedestrians, and users of vehicles with and without driving automation features. The levels apply to the driving automation feature(s) that are engaged in any given instance of on-road operation of an equipped vehicle. As such, although a given vehicle may be equipped with a driving automation system that is capable of delivering multiple driving automation features that perform at different levels, the level of driving automation exhibited in any given instance is determined by the feature(s) that are engaged. This document also refers to three primary actors in driving: the (human) user, the driving automation system, and other vehicle systems and components. These other vehicle systems and components (or the vehicle in general terms) do not include the driving automation system in this model, even though as a practical matter a driving automation system may actually share hardware and software components with other vehicle systems, such as a processing module(s) or operating code. The levels of driving automation are defined by reference to the specific role played by each of the three primary actors in performance of the DDT and/or DDT fallback. “Role” in this context refers to the expected role of a given primary actor, based on the design of the driving automation system in question and not necessarily to the actual performance of a given primary actor. For example, a driver who fails to monitor the roadway during engagement of a Level 1 adaptive cruise control (ACC) system still has the role of driver, even while s/he is neglecting it. Active safety systems, such as electronic stability control (ESC) and automatic emergency braking (AEB), and certain types of driver assistance systems, such as lane keeping assistance (LKA), are excluded from the scope of this driving automation taxonomy because they do not perform part or all of the DDT on a sustained basis, but rather provide momentary intervention during potentially hazardous situations. Due to the momentary nature of the actions of active safety systems, their intervention does not change or eliminate the role of the driver in performing part or all of the DDT, and thus are not considered to be driving automation, even though they perform automated functions. In addition, systems that inform, alert, or warn the driver about hazards in the driving environment are also outside the scope of this driving automation taxonomy, as they neither automate part or all of the DDT, nor change the driver’s role in performance of the DDT (see 8.13). It should be noted, however, that crash avoidance features, including intervention-type active safety systems, may be included in vehicles equipped with driving automation systems at any level. For automated driving system (ADS) features (i.e., Levels 3 to 5) that perform the complete DDT, crash mitigation and avoidance capability is part of ADS functionality (see also 8.13).

On-Road Automated Driving (ORAD) committee

Development of Fault Detection and Emergency Control for Application to Autonomous Vehicle

2021-01-00754/6/2021

This paper describes a failsafe system of automated driving vehicles. The failsafe system consists of the following two parts: sliding mode observer-based environment sensor, chassis sensor fault detection, and emergency deceleration control. Two sliding mode observers are designed to reconstruct the fault of acceleration and environment sensor(Lidar) in a longitudinal direction. In the environment sensor's fault detection part, the longitudinal vehicle model receives clearance and relative velocity values. Therefore, failure diagnosis is possible regardless of environmental sensors, such as radar, lidar, and camera. This paper's sensor data is the failure of Delphi's Electronically Scanning Radar (ESR) and Ibeo's LUX Lidar installed in an autonomous vehicle. The emergency deceleration control algorithm employs the sliding mode control with adaptive convergence time. In the event of a failure, it is significant to control the vehicle within a short period safely. The Adaptive convergence time concept proves a mathematical convergence of the vehicle control time after a failure occurs. As soon as the error occurred, the error was proved to always converge to zero within the final time. Thus, the proposed method introduces the concept of convergence time, and mathematically demonstrates that the state reached the reference target within the specified time when a failure occurred. In the emergency control part, two processing unit hardware structures are adopted to comply with SAE International standard J3016 and NHTSA autonomous vehicle safety report standards. The proposed fail-safe detection algorithm is evaluated through vehicle test data, and the fail-safe control algorithm evaluates through computer simulation and vehicle tests.

Jong Min, Lee, Oh, Kwang Seok, Song, Taejun

Techno-Economic Analysis of Fixed-Route Autonomous and Electric Shuttles

2021-01-00614/6/2021

This paper takes a realistic approach to develop a techno-economic analysis for fixed-route autonomous shuttles. To develop a model for analysis, the current state of technology was used to approximate three timelines for achieving SAE level 5 capabilities: progressive, realistic, and conservative. Within these timelines, there are four different increments for advancements in the technology laid out as follows: SAE Level 0 - human driver, SAE Level 4 - in-vehicle safety operator, SAE Level 4 - remote safety operator, and SAE Level 5 - no safety operator. These increments in the changes of the technology were chosen based on the trends in the industry. Various shuttle models were used based on different rider quantities and drive-train requirements (electric vs gas) in this analysis. This allows for further understanding of how these deployment plans will vary the cost for shuttles operating in high, mid, and low ridership demand environments. Additional drive-train comparison shows the savings based on the choice of electric vs ICE vehicles. Taking these parameters into consideration, simulations were run for the various vehicle models in the various ridership demand environments to produce the economic costs for each situation. It was found that in 15 years there is an economic savings of 72%, 68%, 43%, and 35% for small, medium, large, and extra-large shuttles, respectively if deployed with the conservative plan of becoming SAE Level 4 with an in-vehicle safety operator in 2 years, SAE Level 4 with a remote safety operator in 4 years, and SAE Level 5 in 8 years.

Goberville, Nick, Zoardar, Md Marsad, Rojas, Johan, Brown, Nicolas, Motallebiaraghi, Farhang, Navarro, Anthony, Asher, Zachary

AVSC Best Practice for Metrics and Methods for Assessing Safety Performance of Automated Driving Systems (ADS)

AVSC000062021033/25/2021

This AVSC Best Practice for Metrics and Methods for Assessing Safety Performance of Automated Driving Systems (ADS) (AVSC00006202103) recommends a set of metrics that may be used to assess ADS safety performance of the dynamic driving task (DDT). These metrics and methods are principally designed to provide evidence of safety performance for a manufacturer’s decision to deploy (and monitor) fleet-operated/managed SAE level 4 and 5 ADS-dedicated vehicles (ride-hailing or product delivery). This document lays out a performance-based, technology-neutral approach for measuring and analyzing safety performance. It supports long-term, socially-important safety goals (like reducing crashes). ADS safety performance metrics in this document support system-level analyses, i.e. they are practical to implement for any system regardless of architecture. The best practice provides: Metrics to Support ADS Safety Recommended Safety Outcomes Recommended Predictive Safety Metrics Methods for Assessing DDT Performance Metrics The metrics and methods provided in this document are intended for use by the technical community (developers, manufacturers, testers, etc.) to aid in the safe development and deployment of ADS. They may also be useful to stakeholders who have interest in better understanding the safety posture of ADS deployments.

Automated Vehicle Safety Consortium

AVSC Best Practice for First Responder Interactions with Fleet-Managed Automated Driving System-Dedicated Vehicles (ADS-DVs)

AVSC0000520201212/10/2020

This Automated Vehicle Safety Consortium (AVSC) Best Practice for First Responder Interactions with Fleet-Managed Automated Driving System-Dedicated Vehicles (ADS-DVs) (AVSC00005202012) recommends a common approach for describing the interactions and associated protocols for incorporating vital information, regarding first responder interactions with SAE level 4 and level 5 fleet operated vehicles, into on-going development and deployment plans. This best practice provides the following: Definitions of roles associated with emergency situations / processes, Descriptions of expected interactions (use cases) between first responders and ADS-DVs, Recommendations to address first responder and ADS-DV interactions, and Recommends a First Responder Interaction Plan framework. This best practice is also intended to provide the means for continued engagement and dialogue between first responders, developers, manufacturers, fleet operators and local communities to promote safe operations and consumer acceptance of fleet-managed ADS-DVs.

Automated Vehicle Safety Consortium

A Practical Fail-Operational Steering Concept

02-13-03-0013

10/2/2020

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

AVSC Best Practice for Data Collection for Automated Driving System-Dedicated Vehicles (ADS-DVs) to Support Event Analysis

AVSC000042020099/23/2020

As technology and functionality of vehicle systems change, so do data recording needs. In ADS-dedicated vehicles (DV), the ADS perceives the environment and handles vehicle motion control, i.e., the dynamic driving task (DDT), as described in SAE J3016. When an ADS takes the place of a human driver, its sensing, processing, and control systems necessitate new considerations for data recording. Data recording is important to crash reconstruction, system performance investigations, and event analysis. It enables industry-wide improvements in ADS safety. This best practice makes recommendations for the ADS-DV data needed to support: (1) information about what the ADS "saw" and "did" and (2) identify the technology-relevant factors that contributed to the event. The Best Practice for Data Collection for Automated Driving System Dedicated Vehicles (ADS-DVs) to Support Event Analysis makes recommendations for the collection, storage, and retrievability of onboard motor vehicle ADS event data. A list of 39 data elements are recommended for ADS-DVs to collect, including 14 new elements specific to ADS-DVs and 25 elements either previously defined or adapted from legacy standards in data collection.

Automated Vehicle Safety Consortium

AVSC Best Practice for Passenger-Initiated Emergency Trip Interruption

AVSC000032020066/30/2020

As passengers take rides in fleet-managed automated driving system-dedicated vehicles (ADS-DVs), they may feel the need to interrupt the trip due to a perceived emergency. There is currently no industry consensus on the proper balance between ADS passenger agency and the potential for introducing unexpected outcomes in dynamic traffic environments. In order to build public trust in automated vehicles, passengers should be given an option to exercise some type of control (agency) to intervene during situations they perceive as emergencies. Passenger-initiated emergency trip interruption features — however they manifest in a given vehicle - can help establish this confidence in ADS technologies. AVSC Best Practice for Passenger-Initiated Emergency Trip Interruption recommends processes surrounding aspects of passenger-initiated features in SAE level 4 and 5 fleet-managed ADS-DVs. It recommends criteria and processes for passenger initiation of these features from inside the vehicle; communication with passengers and fleet operations; enhanced diagnoses of the situation, interaction outside the vehicle with other road users, and general post-stop actions. Also, precautions against some types of foreseeable misuse are addressed. These recommendations apply to commercially available, deployed ADS-DV’s providing trips to people. The AVSC recommends that every fleet-managed SAE level 4 and 5 ADS-DV be equipped with a (PES) or (PEC) or both.

Automated Vehicle Safety Consortium

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

J3216_202005 (Historical)

5/7/2020

Cooperative Driving Automation(CDA) Committee

Chromaticity Requirements for Ground Vehicle Lamps and Lighting Equipment

J578_202004 (Current)

4/29/2020

This SAE Standard defines and provides a means for the control of colors employed in motor vehicle external lighting equipment, including lamps and reflex reflectors. The document applies to the overall effective color of light emitted by the device in any given direction, and not to the color of the light from a small area of the lens. It does not apply to pilot, indicator, or tell-tale lights.

Lighting Standard Practices Committee

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

AVSC000022020044/15/2020

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 Operational Design Domain: Conceptual Framework and Lexicon is a critical first step. It offers a conceptual framework for manufacturers and developers to use when communicating with public agencies and the general public about their ADS’s ODD. It also details a list of potential variables with definitions that manufacturers and developers might use to describe certain aspects of the ODDs of their ADS-operated vehicles. It was developed with fleet-managed, SAE Level 4 vehicles in mind — i.e. vehicles requiring no human intervention to operate within their ODD. These vehicles are NOT privately owned.

Automated Vehicle Safety Consortium

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

2020-01-12044/14/2020

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 four scenario testing modes, and compares them using eight criteria. It also identifies several possible implementations of mixed-reality scenario testing, including different strategies for data mixing. The paper closes with twelve recommendations for choosing among the four modes.

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

Control Model of Automated Driving Systems Based on SOTIF Evaluation

2020-01-12144/14/2020

In partially automated and conditionally automated vehicles, a part of the work of human drivers is replaced by the system, and the main source of safety risks is no longer system failures, but non-failure risks caused by insufficient system function design. The absence of unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or by reasonably foreseeable misuse by persons, is referred to as the Safety Of The Intended Functionality. Drivers have the responsibility to supervise the automated driving system. When they don't agree with the operation behavior of the system, they will interfere with the instructions. However, this may lead to potential risks. In order to discover the causes of human misuse, this paper takes the trust feeling between the driver and the automated driving system as the starting point, and based on the collected data of track test, establishes the evaluation indicator -- degree of confidence to show the trust feeling between the driver and the automated system. Degree of confidence is a comprehensive interpretation of the driver's physical and psychological feelings. In the process of track test, we simultaneously collect the dynamics indicators of the vehicle. After the test, the drivers' driving feeling was evaluated by questionnaire. Then, the relationship between objective indicator and subjective score was established by machine learning method, and the development of evaluation indicator was completed. Finally, this paper optimizes the automatic driving motion planning algorithm based on this indicator, and verifies the effectiveness of the algorithm through simulation.

Guo, Mengge, Shang, Shiliang, Haifeng, Cui, Zhang, Kaijiong, Deng, Weishun, Zhang, Xi, Yu, Fan

On Perception Safety Requirements and Multi Sensor Systems for Automated Driving Systems

2020-01-01014/14/2020

One major challenge in designing SAE level 3-5 Automated Driving Systems (ADS) is to define requirements for the perception system that would enable argumentation for safe operation. The safety requirements on the perception system can only be fulfilled through redundancy in the sensor hardware. It is, however, a challenge to specify the redundancy that is required in the sensor system. Safe operation for an ADS is significantly more difficult compared to advanced driver assistance systems (ADAS). The safety argumentation for ADAS typically argues that in case of a failure in the sensor array a fail-silent behavior is acceptable because the human driver can take control of the vehicle back. This argumentation however is not possible when developing level 4 or higher automation. This paper investigates prerequisites for applying a systematic methodology for analyzing redundancy in a multi-sensor system and the relation to a conceptual ADS functional architecture. This analysis must address the complexity that comes with partially overlapping sensor data from different sensors and considers variations in performance and characteristics due to changes in the environmental conditions. The paper introduces the term incomplete redundancy and presents a systematic methodology for analyzing redundancy. The aim is to provide arguments for how several sensors in a system, when appropriately combined, meet an assigned safety requirement on a higher level. Each sensor will then be assigned a certain responsibility and contributes with a sub-set of information. A set of questions of importance to address as a foundation for such a methodology are defined and discussed. The definitions of redundancy and independence between sensors are discussed as well as contract-based functional safety to adapt to different environmental and operating conditions.

Cassel, Anders, Bergenhem, Carl, Christensen, Ole Martin, Heyn, Hans-Martin, Leadersson-Olsson, Susanna, Majdandzic, Mario, Sun, Peng, Thorsén, Anders, Trygvesson, Jörgen

Observer for Faulty Perception Correction in Autonomous Vehicles

2020-01-06944/14/2020

Operation of an autonomous vehicle (AV) carries risk if it acts on inaccurate information about itself or the environment. The perception system is responsible for interpreting the world and providing the results to the path planning and other decision systems. The perception system performance is a result of the operating state of the sensors, e.g. is a sensor in fault or being adversely affected by the weather or environmental conditions, and approach to sensor measurement interpretation. We propose a trailing horizon switched system observer that minimizes the difference between reference tracking values developed from sensor fusion performed at an upper level and the values from a potentially faulty sensor based upon the convex combination of different sensor observation model outputs; the sensor observations models are associated with different sensor operating errors. The preferred observer target is a stationary landmark so as to remove additional uncertainty resulting from tracking of moving targets. Results for five scenarios show the observer identifies the appropriate sensor model in no more than a few sample intervals.

Omwansa, Mark, Meyer, Rick, Asher, Zachary, Goberville, Nick

Development of a Procedure to Correlate, Validate and Confirm Radar Characteristics of Surrogate Targets for ADAS Testing

2020-01-07164/14/2020

Surrogate targets are used throughout the automotive industry to safely and repeatably test Advanced Driver Assistance Systems (ADAS) and will likely find similar applications in tests of Automated Driving Systems. For those test results to be applicable to real-world scenarios, the surrogate targets must be representative of the real-world objects that they emulate. Early target development efforts were generally divided into those that relied on sophisticated radar measurement facilities and those that relied on ad-hoc measurements using automotive grade equipment. This situation made communication and interpretation of results between research groups, target developers and target users difficult. SAE J3122, “Test Target Correlation - Radar Characteristics”, was developed by the SAE Active Safety Systems Standards Committee to address this and other challenges associated with target development and use. J3122 addresses four topics. First, it describes standardized equipment and procedures for making various types of calibrated radar measurements using automotive grade equipment, with minimal measurement site restrictions. Second, a correlation procedure is provided that is used to define validity regions and properties of representative real-world objects. Third, a validation procedure is provided for comparing candidate targets against measurements of representative objects using an objective correlation score. Finally, a confirmation procedure is provided for checking in-use targets to verify that they continue to be acceptable for testing. This paper describes each of these topics as well as the process development.

Silberling, Jordan, Nicols, George, Buller, William, Lenkeit, John

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

2020-01-01074/14/2020

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 scenario and ADS/ADAS system ODD; fulfillment or violation of which can help in runtime dynamic risk assessment of ADS/ADAS to plan minimal safe behavior such that necessary safety requirements can be achieved. Finally, through experiment we show that proposed runtime active assurance safety module can handle complex driving scenario, and present simulation and experimental results that emphasizes the importance of the proposed runtime safety assurance module and shows that the proposed system is capable of performing runtime dynamic risk assessment in order to keep the automated driving systems always within the safe sate that is the automated driving system always perform within its ODD.

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

Automated Driving System Data Logger

J3197_202004 (Historical)

4/11/2020

This 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. The data elements defined in this document are unique to Levels 3, 4, or 5 ADS features, as defined by SAE J3016, and provide additional background of the events leading up to a crash or crash-like event. The data from sensors such as camera(s), LiDAR(s) etc. will provide information in the absence of a human driver. The data included in the ADS data logger is expected to be used in conjunction with the SAE J1698 EDR record and traditional accident reconstruction analysis. The event data recorder (EDR) and ADS data logger will capture information leading up to the triggered event, at a minimum. ADS technology is still being developed and is not yet commercially deployed. Therefore, this SAE Recommended Practice is intended as a guide toward standard practice and is subject to change to keep pace with experience and technical advances.

Event Data Recorder Committee

Unsettled Topics Concerning Automated Driving Systems and the Development Ecosystem SAAS Demo 10-17-23

EPR20200043/17/2020

With over 100 years of operation, the current automobile industry has settled into an equilibrium with the development of methodologies, regulations, and processes for improving safety. In addition, a nearly $2-trillion market operates in the automotive ecosystem with connections into fields ranging from insurance to advertising. Enabling this ecosystem is a well-honed, tiered supply chain and an established development environment. Autonomous vehicle (AV) technology is a leap forward for the existing automotive industry; now the automobile is expected to manage perception and decision-making tasks. The safety technologies associated with these tasks were presented in an earlier SAE EDGE™ Research Report, “Unsettled Technology Areas in Autonomous Vehicle Test and Validation.” In a later SAE EDGE™ Research Report, “Unsettled Topics Concerning Automated Driving Systems and the Transportation Ecosystem,” senior executives from the automotive ecosystem explored the impact of AV technology as they faced the prospect of this disruptive technology entering their marketplace. Interestingly, stable use-models and market penetration were all gated primarily by the demonstration of AV safety. Building on these previous verification and validation (V&V)-related reports, “Unsettled Topics Concerning Automated Driving Systems and the Development Ecosystem” explores the open issues in the shift of the development and supplier environment toward a new AV-enabled future. 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 SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.

Razdan, Rahul

The Mind of Argo AI

20AVEP03_033/1/2020

Co-founder and CEO Bryan Salesky brings a realistic approach to the self-driving technology “mission.” Creating technology solutions that work well in one area but don't scale out is a traditional problem in the robotics field. It's a particular challenge for engineers developing self-driving vehicles, acknowledged Bryan Salesky, the CEO and co-founder of Argo AI. The Pittsburgh-based company is a leader in SAE Level 4 systems development, partnering with Ford and Volkswagen to put autonomous people-movers and delivery vehicles into revenue-generating road use in the next few years. Ford plans to deploy its first units in three U.S. cities in late 2021. “It's no small task,” Salesky asserted. “We're on a mission to make roads safer at the end of the day, and to give people options as to how they get around” with greater convenience and affordability. But such inflection points “don't just roll out overnight-it's going to be gradual deployments in cities that can support the platform,” he said.

Brooke, Lindsay

Editorial

20AVEP03_013/1/2020

Some may remember - from a time before “brands” were intricately managed corporate personalities - a stays-in-your-head advertising campaign for the soft drink 7 Up. Pushing for share in a largely one-dimensional market dominated by Coke and Pepsi, 7 Up billed itself as the “Uncola.” Nobody knew it when Argo AI was founded four years ago, but it was the Uncola of the autonomous-vehicle (AV) world. During our interview with him for this month's cover feature, CEO Bryan Salesky repeatedly stressed that AV development is in its early stages and SAE Level 4 autonomy is years away.

Visnic, Bill

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

2020-01-5017

2/24/2020

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_031/1/2020

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

Democratize AV Technology!

20AVEP01_041/1/2020

Aptiv's new generation of open-sourced architectures based on a few central processors aims to speed AV adoption. CTO Glen DeVos explains. The software-intensive, electrified and increasingly automated vehicle will define the 2020s. Its rise is driving both the industry-wide re-thinking of electrical architectures and the growth of engineering employment behind it. At the forefront of this trend is Aptiv, the technology Tier 1 spun off from Delphi in 2017. It now has more than 19,000 engineers among its 160,000 staff, comprising one of the highest engineer-to-employee ratios among large suppliers. “We've been adding about 1,500 engineers per year, primarily in software and systems engineering, at our 15 major technical centers,” said CTO Glen DeVos. These resources, he noted, will help Aptiv accelerate its customers' development of new vehicle platforms with greater active-safety capability, including automated-driving functionality.

Brooke, Lindsay

HEAT SEEKERS

20AUTP02_011/1/2020

While engineers debate the use of thermal-imaging sensors for ADAS, their capability and value are being proven for AVs of all levels. What specific sensor types will comprise the advanced driver-assistance systems (ADAS) of the 2020s? That's a controversial subject among engineers who are developing SAE Level 2 and 3 (and the so-called “L2+”) ADAS sensing suites for new vehicles. Many of them believe that visible-light cameras fused with radar will suffice to deliver the object-identification accuracy, redundancy-and cost effectiveness-that OEMs and the driving public expect of ADAS-equipped vehicles. But a case is building for additional sensing capability, particularly for automatic emergency braking (AEB) and pedestrian-detection. Those safety-critical functions currently rely on camera-radar inputs to “see” ahead. They enable the vehicle to react to a range of scenarios-from stalled traffic on a highway to humans and animals suddenly appearing in the road. (In Michigan alone, there were 53,464 traffic accidents involving deer in 2018, up 14% from 2016, according to state DoT data.)

Brooke, Lindsay

Unsettled Topics Concerning the Field Testing of Automated Driving Systems

EPR201900912/19/2019

Automated driving systems (ADS) have the potential to revolutionize transportation. Through the automation of driver functions in the application of advanced technology within the vehicle, significant improvements can be made to safety, efficiency, user experience, and the preservation of the environment. According to the US Department of Transportation [1], there are more than 1,400 cars, trucks, buses, and other vehicles being tested by more than 80 companies across the USA. Implementation of ADS technology is well advanced, with many sites across the USA incorporating automated vehicles (AVs) into wider programs to apply advanced technology to transportation. Discussions with the public sector’s implementing agencies suggest that one of the barriers to faster progress lies in the lack of consistent and standardized field-testing protocols. This report looks at the state of the art of field testing for ADS and identifies areas for improved consistency and standardization. It will define the problem to be addressed by AVs and the challenges associated with the introduction of such vehicles and open-road situations. In particular, the report will look at the possibilities for big data and analytics to enable the sharing of lessons learned and convergence on standard field-testing approaches. 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 SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio.

McQueen, Bob

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 emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny.

Beiker, Sven

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 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 collision avoidance system is not enough anymore because of the limited time-to-react. It is not acceptable that an automated driving vehicle passes obstacles very slowly. Therefore, a collision avoidance system is combined with a situation awareness planner to optimize the driving velocity. The situation awareness planner considers the sensor’s visibility and the capability of the collision avoidance system to provide a set of collision-free trajectories. This combination has the advantage that the vehicle does not need to pass objects on the side very conservative. We evaluate the approach rigorously on a set of well-defined scenarios from the Euro NCAP test protocol.

Schratter, Markus, Hartmann, Michael, Watzenig, Daniel

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

EPR2019007

12/6/2019

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 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 SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio.

Beiker, Sven

Q&A

19TOFHP12_1312/1/2019

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 technical session at the SAE Government/Industry Meeting taking place January 22-24, 2020, in Washington, DC (www.sae.org/attend/government-industry/).

Gehm, Ryan

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 other committees within SAE International.

On-Road Automated Driving (ORAD) committee

AVSC Best Practice for In-Vehicle Fallback Test Driver Selection, Training, and Oversight Procedures for Automated Vehicles Under Test

AVSC0000120191111/8/2019

ABSTRACT Best Practice for in-vehicle fallback test driver (safety operator) selection, training, and oversight procedures for automated vehicles under test (AVSC000012019MM) addresses the qualifications and training for on-board human oversight of testing for automated driving system (ADS)-operated vehicles. It provides an outline with criteria commonly agreed to by members of the AVSC, which includes: Driver selection Basic Driver training ADS-operation training Initial driving on public roads Periodic re-evaluation and training The Best Practice applies to humans within the vehicle responsible for the safe oversight of development and testing SAE Level 4 and Level 5 automated driving systems on public roads. In-Vehicle Fallback Test Driver (IFTD) Characteristics and Framework Several factors are considered, including driver selection, driver training, ADS training, best practices for initial ADS driving on public roads, and periodic IFTD evaluations, re-evaluation and training of driving skills. Driver Selection and Verification IFTD driver selection criteria are explored, include driving experience, record checks, driving evaluation, mindset, and criminal background checks. Training The AVSC recommends a progressive framework for IFTD basic driver training – building skills over time while introducing increasing levels of complexity. Controlled environment training Interaction training Evaluation / assessment of skills IFTD training on ADS systems are addressed at length. Topics for classroom training are addressed awareness of ADS sensor technologies, system behaviors, trust calibration the human-machine interface and effective communication among IFTDs. Recommendations for closed-course IFTD training are introduced, including scenario-based exercises, fault-injection training, and testing of IFTD general awareness and attention. The best practice follows closed course training with recommendations for supervised ADS driving on public roads and considerations of IFTD evaluation and on-going training. Best practices for evaluation include: Monitoring Evaluation / assessment of skills On-going training Remedial training Refresher training The AVSC sets forth best practices for ADS-operated vehicle on-road testing protocols. These basic guidelines for incident response protocols, pre-trip, in-trip, and post-trip protocols. The best practice emphasizes minimizing distraction and communicating known ADS changes and limitations, including software and hardware, to ensure safe operation during testing. SUMMARY The IFTD’s sole responsibility in an ADS-operated vehicle is ensuring its safe operation. Companies engaged in ADS-operated vehicle testing on public roads should follow a rigorous process to attract and train individuals with a technology focus and safety mindset. Thinking through and thoroughly documenting processes that reinforce a culture of safety and continuous improvement should be the cornerstones of any on-road testing program. Adhering to best practices like those outlined in this document can reduce risk and engender public trust in automated driving systems and the companies that develop and deploy them. About the AVSC The Automated Vehicle Safety ConsortiumTM (AVSC) is an industry program of SAE Industry Technologies Consortia (SAE ITC®) building on principles that will inform and help lead to industrywide standards for advancing automated driving systems. The members of this consortium have long been focused on the development of safe, reliable and high-quality vehicles, and are committed to applying these same principles to Level 4 and Level 5 automated vehicles so communities, government entities and the public can be confident that these vehicles will be deployed safely.

Automated Vehicle Safety Consortium

Unsettled Topics Concerning Automated Driving Systems and the Transportation Ecosystem

EPR2019005

11/5/2019

Over the last 100 years, the automobile has become integrated in a fundamental way into the broader economy. A broad and deep ecosystem has emerged, and critical components of this ecosystem include insurance, after-market services, automobile retail sales, automobile lending, energy suppliers (e.g., gas stations), medical services, advertising, lawyers, banking, public planners, and law enforcement. These components - which together represent almost $2 trillion of the U.S. economy - are in equilibrium based on the current capabilities of automotive technology. However, the advent of autonomous vehicles (AVs) and technologies like electrification have the potential to significantly disrupt the automotive ecosystem. The critical cog governing the rate and pace of this shift is the management of the test and verification of AVs. In this SAE EDGE™ report, six senior industry leaders in the impacted ecosystems essay articles which describe sectors of the current automotive ecosystem and the manner in which AV technology can potentially reshape them - providing a mosaic of the massive infrastructure shifts which will be required to absorb AV technologies. 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 SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio.

Razdan, Rahul