Browse Topic: Level 1 (Driver assistance)

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Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor VehiclesJ3216_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
Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor VehiclesJ3016_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
Correlation between Sensor Performance, Autonomy Performance and Fuel-Efficiency in Semi-Truck Platoons2021-01-00644/6/2021
Semi-trucks, specifically class-8 trucks, have recently become a platform of interest for autonomy systems. Platooning involves multiple trucks following each other in close proximity, with only the lead truck being manually driven and the rest being controlled autonomously. This approach to semi-truck autonomy is easily integrated on existing platforms, reduces delivery times, and reduces greenhouse gas emissions via fuel economy benefits. Level 1 SAE fuel studies were performed on class-8 trucks operating with the Auburn Cooperative Adaptive Cruise Control (CACC) system, and fuel savings up to 10-12% were seen. Enabling platooning autonomy required the use of radar, global positioning systems (GPS), and wireless vehicle-to-vehicle (V2V) communication. Poor measurements and state estimates can lead to incorrect or missing positioning data, which can lead to unnecessary dynamics and finally wasted fuel. This is especially an issue if deceleration is applied in response to a bad measurement. In this study, a faulty radar was shown to cause a greater than 5% increase in fuel consumption. The mechanism of this fuel consumption increase is investigated and applied to other types of sensor failures to indicate their potential effects on fuel economy. This analysis indicates that poor GPS signals over short time can be largely filtered out, with no real gain or loss of fuel economy. V2V communications were intentionally limited by causing interference, which resulted in dropped communication packets over a small physical area, but not an appreciable impact on fuel economy.
Adam, Cristian, Lakshmanan, Sridhar, Richardson, Paul, Stegner, Evan, Ward, Jacob, Hoffman, Mark, Bevly, David M.
Safety-Relevant Guidance for On-Road Testing of Prototype Automated Driving System (ADS)-Operated VehiclesJ3018_202012 (Current)12/4/2020
This document provides preliminary1 safety-relevant guidance for in-vehicle fallback test driver training and for on-road testing of vehicles being operated by prototype conditional, high, and full (Levels 3 to 5) ADS, as defined by SAE J3016. It does not include guidance for evaluating the performance of post-production ADS-equipped vehicles. Moreover, this guidance only addresses testing of ADS-operated vehicles as overseen by in-vehicle fallback test drivers (IFTD). These guidelines do not address: Remote driving, including remote fallback test driving of prototype ADS-operated test vehicles in driverless operation. (Note: The term “remote fallback test driver” is included as a defined term herein and is intended to be addressed in a future iteration of this document. However, at this time, too little is published or known about this type of testing to provide even preliminary guidance.) Testing of driver support features (i.e., Levels 1 and 2), which rely on a human driver to perform part of the dynamic driving task (DDT) and to supervise the driving automation feature’s performance in real time. (Refer to SAE J3016.) Closed-course testing. Simulation testing (except for training purposes). Component-level testing. These guidelines also do not address prototype vehicle and IFTD performance data collection and retention. The collection of data invokes various legal and risk management considerations that users of this document should nevertheless bear in mind, such as: Maintaining auditable procedures and documentation. Adhering to applicable privacy laws and principles. Ensuring adequate data collection and recording integrity to support post-crash forensic analysis.
On-Road Automated Driving (ORAD) committee
Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor VehiclesJ3216_202005 (Historical)5/7/2020
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
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
Q&A19AUTP10_1610/1/2019
Swamy Kotagiri is Magna's chief technology officer and head of the company's Power & Vision segment. Automotive Engineering contributor Sebastian Blanco spoke with him at the 2019 opening of Magna Electronics' new manufacturing facility near Holly, Michigan. There, the company will be consolidating production lines to make and fully test around 12 million automotive-grade cameras a year. Some highlights of our conversation:
Safety-Relevant Guidance for On-Road Testing of SAE Level 3, 4, and 5 Prototype Automated Driving System (ADS)-Operated VehiclesJ3018_201909 (Historical)9/4/2019
This document provides safety-relevant guidance for on-road testing of vehicles being operated by prototype conditional, high, and full (Levels 3 to 5) ADS, as defined by SAE J3016. It does not include guidance for evaluating the performance of post-production ADS-equipped vehicles. Moreover, this guidance only addresses testing of ADS-operated vehicles as overseen by in-vehicle fallback test drivers (IFTD). These guidelines do not address: Remote driving, including remote fallback test driving of prototype ADS-operated test vehicles in driverless operation. (Note: The term “remote fallback test driver” is included as a defined term herein and is intended to be addressed in a future iteration of this document. However, at this time, too little is published or known about this type of testing to provide even preliminary guidance.) Testing of driver support features (i.e., Levels 1 and 2), which rely on a human driver to perform part of the dynamic driving task (DDT) and to supervise the driving automation feature’s performance in real time. (Refer to SAE J3016.) Closed-course testing. Simulation testing (except for training purposes). Component-level testing.
On-Road Automated Driving (ORAD) committee
Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor VehiclesJ3016_201806 (Historical)6/15/2018
This SAE Recommended Practice 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. 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 users of vehicles of all classes and driving automation levels (including no driving automation), as well as motorcyclists, pedal cyclists, and pedestrians. 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 and automated emergency braking, and certain types of driver assistance systems, such as lane keeping assistance, 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 and, rather, merely 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. It should, however, be noted 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 3-5) that perform the complete DDT, crash avoidance capability is part of ADS functionality.
On-Road Automated Driving (ORAD) committee
Fusing sensors for the AUTOMATED-DRIVING FUTURE18AUTP01_021/1/2018
Complex processing architectures, faster networks and even more sophisticated software are vital for 100% accurate Level 4 and 5 systems. Piloted and autonomous driving systems depend on inputs from many types of sensors arrayed to create a highly accurate view of vehicle surroundings. Fusing all these inputs into a single image of each car, pedestrian and roadway marking takes a sophisticated architecture with hefty amounts of computing power and deterministic communications. At highway speeds, combining inputs from multiple camera views is a complex challenge by itself. But different sensor types are needed to avoid errors, so vehicle controllers must blend inputs coming from multiple sensors and multiple modalities.
Costlow, Terry
700 MILES, hands-free!17AUTP11_0111/1/2017
GM's Super Cruise turns Cadillac drivers into passengers in a well-engineered first step toward greater vehicle autonomy. Two sunny days in late September, a state-of-the-art luxury sedan, and good company make for a potentially great road trip. But this one was special. “Pretty remarkable that we've already driven about 500 miles without either of us touching the steering wheel; I'm impressed,” I noted to co-driver Sam Abuelsamid, as we headed southbound from Chicago, on Interstate 57 in Illinois.
Brooke, Lindsay
Electronic Horizon: A Map as a Sensor and Predictive Control2017-01-19458/25/2017
During recent years, all major North American and European commercial vehicle OEMs have introduced predictive functionalities based on an electronic horizon for their on-highway fleets. This is a system concept that lets vehicles know what is happening on the road ahead and allows them to react to that information without driver involvement. When an electronic horizon is used in heavy-duty trucks, a significant reduction in fuel consumption is possible as a key application. This is achieved by optimizing the algorithms in the engine control unit, the transmission control device or other control units in the vehicle. There is a clear business case for the vehicle owners. In this paper we review the long development from early navigation technologies to an in-vehicle sensor, called an electronic horizon. We present an overview of different architectures from several perspectives as well as multiple use cases for commercial vehicles. Furthermore, we look at the future, in which the electronic horizon will be able to save location-related vehicle data and transfer it to a cloud or deliver dynamic data for the road ahead such as traffic jams, accidents or weather conditions. Finally, we examine the relevance of the electronic horizon for important future innovations such as the connected powertrain and autonomous driving.
Varnhagen, Dr. Raimund
Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor VehiclesJ3016_201609 (Historical)9/30/2016
This Recommended Practice provides a taxonomy for motor vehicle driving automation systems that perform part or all of the dynamic driving task (DDT) on a sustained basis and that range in level from no driving automation (level 0) to full driving automation (level 5). It provides detailed definitions for these six levels of driving automation in the context of motor vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on roadways. 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 users of vehicles of all classes and driving automation levels (including no driving automation), as well as motorcyclists, pedal cyclists, and pedestrians. 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) driver, the driving automation system, and other vehicle systems and components. These other vehicle systems (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. “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 and automated emergency braking, and certain types of driver assistance systems, such as lane keeping assistance, 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 and, rather, merely 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. It should, however, be noted that crash avoidance features, including intervention-type active safety systems, may be included in vehicles equipped with driving automation systems at any level. For ADS-equipped vehicles (i.e., levels 3-5) that perform the complete DDT, crash avoidance capability is part of ADS functionality.
On-Road Automated Driving (ORAD) committee
Automated Driving Impediments2016-01-80079/27/2016
Since the turn of the millennium, automated vehicle technology has matured at an exponential rate, evolving from research largely funded and motivated by military and agricultural needs to a near-production market focused on everyday driving on public roads. Research and development has been conducted by a variety of entities ranging from universities to automotive manufacturers to technology firms demonstrating capabilities in both highway and urban environments. While this technology continues to show promise, corner cases, or situations outside the average driving environment, have emerged highlighting scenarios that impede the realization of full automation anywhere, anytime. This paper will review several of these corner cases and research deficiencies that need to be addressed for automated driving systems to be broadly deployed and trusted.
Mentzer, Chris, Lamm, Ryan D., Towler, Jerry
Time Required for Take-over from Automated to Manual Driving2016-01-01584/5/2016
Automation of vehicles can be expected to improve safety, comfort and efficiency, and is being developed in various countries. Introduction of automated driving can be ranked from 0 to 5 (0: no automation, 1: driver assistance, 2: partial automation, 3: conditional automation, 4: high automation, 5: full automation). Currently, feasible automation levels are considered to be levels 2 or 3, and human manual take-over from the automated system is needed when the automated system exceeds these levels. In this situation, time required for take-over is an important issue. This study focuses on describing driving simulator experimental results of time required for take-over. The experimental scenario is that the automated system finds an object ahead during automated driving on the highway, and issues a take-over request to the driver. The subject driver can be in the following driver situations: hands-on or hands-off the steering, and strong or weak distractions. In these situations, we examine two kinds of take-over time, which are 7 and 5 seconds. The results show all subjects complete take-over actions properly even if the required time is 5 seconds. In this paper, details of these results are described.
Ito, Toshio, Takata, Arata, Oosawa, Kenta
Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving SystemsJ3016_201401 (Historical)1/16/2014
This Information Report provides a taxonomy for motor vehicle automation ranging in level from no automation to full automation. However, it provides detailed definitions only for the highest three levels of automation provided in the taxonomy (namely, conditional, high and full automation) in the context of motor vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on public roadways. These latter levels of advanced automation refer to cases in which the dynamic driving task is performed entirely by an automated driving system during a given driving mode or trip. Popular, media, and legislative references to “autonomous” or “self-driving” vehicles encompass some or all of these levels of automation. These definitions can be used to describe the automation of (1) on-road vehicles, (2) particular systems within those vehicles, and (3) the operation of those vehicles. “On-road” refers to public roadways that collectively serve users of vehicles of all classes and automation levels (including no automation), as well as motorcyclists, pedal cyclists, and pedestrians. This document does not provide complete definitions applicable to lower levels of automation (namely, no automation, assisted, or partial automation), but they are described as points of reference to help bound the full range of vehicle automation. Active safety and driver assistance systems that partially and/or temporarily automate certain aspects of vehicle operation (including systems that automatically intervene to avoid and/or mitigate an emergency situation and then immediately disengage), but otherwise rely on a human driver to operate the motor vehicle in real time, are also not included within the conditional, high, and full automation taxonomy types that are the focus of this document.
On-Road Automated Driving (ORAD) committee
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