Automated vehicles, in the form we see today, started off-road. Ideas, technologies, and engineers came from agriculture, aerospace, and other off-road domains. While there are cases when only on-road experience will provide the necessary learning to advance automated driving systems, there is much relevant activity in off-road domains that receives less attention. Implications of Off-road Automation for On-road Automated Driving Systems argues that one way to accelerate on-road ADS development is to look at similar experiences off-road. There are plenty of people who see this connection, but there is no formalized system for exchanging knowledge. Click here to access the full SAE EDGETM Research Report portfolio.

Design of a Secure Automated Driving Systems Test Data Interface

2023-01-0828

04/11/2023

Vehicles equipped with Level 4 and 5 autonomy will need to be tested according to regulatory standards (or future revisions thereof) that vehicles with lower levels of autonomy are currently subject to. Today, dynamic Federal Motor Vehicle Safety Standards (FMVSS) tests are performed with human drivers and driving robots controlling the test vehicle’s steering wheel, throttle pedal, and brake pedal. However, many Level 4 and 5 vehicles will lack these traditional driver controls, so it will be impossible to control these vehicles using human drivers or traditional driving robots. Therefore, there is a need for an electronic interface that will allow engineers to send dynamic steering, speed, and brake commands to a vehicle. This paper describes the design and implementation of a market-ready Automated Driving Systems (ADS) Test Data Interface (TDI), a secure electronic control interface which aims to solve the challenges outlined above. The interface consists of a communication port

Zagorski, Scott, Nguyen, An, Heydinger, Gary, Abbey, Howard

EDITORIAL: Platooning still promising for trucking

22TOFHP08_0408/01/2022

There is “no business case” for platooning, or the electronic coupling of two or more trucks in close formation. That was the assessment of Daimler Trucks in 2019 when it decided to pause its years-long platooning development activities. The OEM determined that for U.S. long-distance applications, where conditions were expected to be ideal, the fuel savings were less than stellar and diminished further when the platoon got “disconnected” and trucks had to accelerate to reconnect. Instead, the company turned its full attention to developing highly automated (SAE Level 4) trucks. The fate of Peloton Technology, a company all-in on platooning but that ceased operations in 2021, is another indicator that perhaps platooning's promise has faded.

Gehm, Ryan

Real-Sim Interface: Enabling Multi-resolution Simulation and X-in-the-Loop Development for Connected and Automated Vehicles

12-05-04-002606/27/2022

Connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications. This interface is built upon Oak Ridge National Laboratory’s (ORNL’s) Real-Sim approach, can support various simulation tools and real-time X-in-the-loop (XIL) simulations, and is based on network

Shao, Yunli, Deter, Dean, Cook, Adian, Wang, Chieh (Ross), Thompson, Bradley, Perry, Nolan

Toward an Automated Scenario-Based X-in-the-Loop Testing Framework for Connected and Automated Vehicles

12-05-04-003006/27/2022

Emerging technologies for connected and automated vehicles (CAVs) are rapidly advancing, and there is an incremental adoption of partial automation systems in existing vehicles. Nevertheless, there are still significant barriers before fully or highly automated vehicles can enter mass production and appear on public roads. These are not only associated with the need to ensure their safe and efficient operation but also with cost and delivery time constraints. A key challenge lies in the testing and validation (T&V) requirements of CAVs, which are expected to be significantly higher than those of traditional and partially automated vehicles. Promising methodologies that can be used toward this goal are scenario-based (SBT) and X-in-the-Loop (XiL) testing. At the same time, complex techniques such as co-simulation and mixed-reality simulation could also provide significant benefits. Nevertheless, the benefits of individual solutions are likely to be significantly smaller, if considered

Kyriakopoulos, Ioannis, Jaworski, Pawel, Edwards, Tim

Ensuring No Child Left Inside

22AVEP04_0504/01/2022

Up-armed with new key technologies, Gentex is making ‘holistic’ driver and cabin monitoring a core-brand hallmark. A step-change in sensing precision is underway for vehicle interior applications, with increased capabilities spurred by new technology, customer interest in occupant safety and entertainment, and global regulations. Gentex is among those setting the pace in this fast-moving and hyper-competitive field, as it integrates its long experience in mirror-based opto-electronic systems and sensor fusion with the 2021 acquisition of Tel Aviv-based Guardian Optical Technologies and the 2020 purchase of Vaporsens. The Holland, Mich.-based Tier 1 uses the term “holistic driver and cabin monitoring” to describe the interconnection of the two main development thrusts. There is discrete driver monitoring, vital for SAE Levels 2 and 3 of driving automation, with a subset of features aimed at driver distraction, drowsiness, weariness, sudden sickness, etc. And there's the ‘holistic

Brooke, Lindsay

Model Based Design and Verification of Automated Driving Features Using XIL Simulation Platforms

2022-01-010903/29/2022

The latest edition of the US Department of Energy's (DOE) Advanced Vehicle Technology Competition (AVTC) series is the EcoCAR Mobility Challenge (EMC). In the third year of the EMC, the Mississippi State University (MSU) team developed and tested a perception system and a longitudinal controller to achieve SAE level 2 autonomy. Our team leveraged model-based design approach to iterate between developing software components and executing tests in multiple environments in the loop (XIL) to verify that design requirements are met. This workflow allowed the team to detect and resolve issues early in the development process. The perception system is composed of a sensor fusion and tracking controller algorithm. It relies on detections from a front facing camera and radar to generate tracks for a leading vehicle. The tracks from the perception system are used by a model predictive controller (MPC) to maintain a safe distance to the leading vehicle. A comparison study between test results

Taoudi, Amine, Gandy, Jonah, Hudson, Vance, Luo, Chaomin, Follett, Randolph

A driving simulator for UNECE 157 homologation activities

2022-01-010803/29/2022

UNECE 157 Regulation is devoted to the homologation of SAE Level 3 automated vehicles. The research and development activities related with the Regulation may require an extensive usage of driving simulators. Developing a sickness-free driving simulator is crucial for extensive tests to be performed with ordinary drivers. The paper presents the technical features of the new driving simulator of the Politecnico di Milano. A number of biometric set of signals is recorded, namely, forces and moments at each single hand at the steering wheel, heart rate variability, skin resistance potential, eye tracking. A matching of the objective monitoring of the driver is compared with the subjective psychological status. An initial population of 30 subjects has been investigated. The new driving simulator has shown a good acceptance by ordinary drivers. Both objective and psychologic subjective assessment agree to state that the new driving simulator may be used to test ordinary driver behavior in a

Gobbi, Massimiliano, Mastinu, Giampiero, Melzi, Stefano, Previati, Giorgio, Sabbioni, Edoardo, Ronconi, Luca

Development of Steering Control Method for Steer-by-Wire System Requiring No Changes in Steering Wheel Hand Position

2022-01-106403/29/2022

Future steering systems must be compatible with automated driving systems, while also improving the flexibility of the cabin space. To address these needs, a steer-by-wire system with no mechanical linkages is being developed. This paper proposes a control method for a variable steering gear ratio and reaction force to achieve a natural steering feel in a steer-by-wire vehicle without requiring the driver to change hand position on the steering wheel. Moreover, taking advantage of the characteristics of a steer-by-wire system, this paper also demonstrates the effectiveness of a control method that transmits only the necessary road information to the driver.

Takashima, Toru, Kudo, Yoshio, Kodera, Takashi, Namikawa, Isao

Modern Product Development Platform for Living Products in Perpetual Systems

2022-01-026103/29/2022

In 1930, John Maynard Keynes predicted, that due to software and automation, 15-hour work weeks would be a reality by the end of the century. While that envisioned “utopia” has not been realized, Mr. Keynes did have the radical vision to imagine a pretty radical low code highly automated future - one to which the future of software in mobility arguably depends on. So, what went wrong? Well, its not about as much about what went wrong but about how adoption is taking place and how it needs to change. In any software development, no matter where in history, as soon as software testing became a hot topic, automation tools started springing up and then "selective parts" that were iterative and time-consuming in the software were automated away. This begs several questions. The first and obvious, why automate these parts - and the second - whether software developers are making themselves obsolete by building automation tools. If more and more machines can write code for themselves, what do

Minarcin, Monika, Rush, David

Driver Behaviors towards Autonomous Vehicles (AVs) Modeling: A Comprehensive Review

2022-01-035003/29/2022

Driving safety is currently based on how the driver acts and behaves in environmental perception, decision-making, and controlling the vehicle. Modeling such behaviors of drivers started back in 1950s and through the years these driver behavior models have been used as an input to self-coaching, accident prevention studies, and developing driver assisting systems that can foster safety of lives on the roads. But in recent years, human-like driving has revolutionized the autonomous vehicle (AV) research and traffic management studies. In heterogeneous roads where human drivers and autonomous vehicles operates, understanding the intent and action of human driver’s action is essential for AVs in conjunction with vehicle to vehicle (V2V) communications. In traffic simulations, the human driver models can also be the input to the AVs as a reference and the perception, decision-making and control of AVs can also be designed human-like. Among other driving tasks on roads, Car following (CF

Negash, Natnael M., Yang, James

HD-Map Based Ground Truth to Test Automated Vehicles

2022-01-010203/29/2022

Over the past decade there has been significant development in Automated Driving (AD) with continuous evolution towards higher levels of automation. Higher levels of autonomy increase the vehicle Dynamic Driving Task (DDT) responsibility under certain predefined Operational Design Domains (in SAE level 3, 4) to unlimited ODD (in SAE level 5). The AD system should not only be sophisticated enough to be operable at any given condition but also be reliable and safe. Hence, there is a need for Automated Vehicles (AV) to undergo extensive open road testing to traverse a wide variety of roadway features and challenging real-world scenarios. There is a serious need for accurate ground truth to locate the various roadway features which helps in evaluating the perception performance of the AV at any given condition. The results from open road testing provide a feedback loop to achieve a mature AD system. This paper presents an approach of using High Definition (HD) map data of various roadway

Martens, Joshua, Zhong, Ren, Yedida, Siddhartha, Alzu'bi, Hamzeh, Tasky, Tom, Sirkar, Deshna

Road Rough Estimation for Autonomous Vehicle Based on Adaptive Unscented Kalman Filter Integrated with Minimum Model Error Criterion

2022-01-007703/29/2022

The accuracy of road input identifiaction for autonomous vehicles (AVs) system, especially in state-based AVs control for improving road handling and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely and effectively acquire road rough information and chose the reasonable road-based control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for AVs system, e.g., varying center of gravity (C.G.) of sprung mass, controllable suspension damping force or variable spring stiffness. To tackle the above mentioned, this paper develops a novel observer approach, which combines unscented Kalman filter (UKF) and Minimum Model Error (MME) theory, to optimize the estimation accuracy of the road rough for AVs system. A full-car nonlinear model and road profile model are first established. Secondly, a MME criterion is proposed to deal with the

Wang, Zhenfeng, Yang, Jiansen, li, xin, Li, HongLiang

Multilevel Concept for Verification of Automated Driving Systems by Using Model in the Loop Simulations in Early Development Phases

2022-01-010703/29/2022

Due to the increasing complexity of Automated Driving Systems (ADS) in combination with the multitude of possible traffic scenarios, the question arises how the functionality of ADS should be verified in an early development phase. Since there is currently no comprehensive industrially feasible solution published, this paper addresses the verification of ADS by means of Model in the Loop (MiL) simulations in advance of real prototype testing. The vehicle dynamic behavior is represented by detailed three-dimensional multi-body simulation models and the considered driving functions are integrated into the simulation framework via standardized interfaces and are assessed by specific Key Performance Indicators. The developed solution can be subdivided into four phases. (1) First, the simulation environment is used to verify the driving functions based on specific standardized test scenarios. (2) Next, based on one-dimensional and multi-dimensional experimental designs, the robustness of

Sinz, Wolfgang, Angrosch, Bernhard, Gächter, Jens, Putsche, Bernhard, Rogic, Branko, Bernsteiner, Stefan

Geotagged Visual Localization System for Urban Automated Vehicles

2022-01-010103/29/2022

Vehicle localization is one of the fundamental building blocks of Automated Driving Systems. Erroneous localization information may affect the ability of the vehicle to perceive the environment and plan a safe path to the desired destination. Although, high-end satellite navigation systems can provide centimeter-level accuracy, they are limited to applications where there is sufficient satellite signal visibility. One example where signals from navigational satellites might deteriorate is urban canyons which are characterized by high rise, high density residential and commercial buildings. To overcome the limitations of satellite navigation systems, most state-of-the-art localization solutions fuse information from multiple sensors such as GNSS, LiDAR, camera, accelerometer, and encoder with the purpose of creating a full 3D map of the operating environment. Although this approach provides accurate and reliable results, it is bounded in terms of data efficiency and scalability. With

AHRABIAN, ALIREZA, SOUFLAS, IOANNIS, SONGUR, NOYAN, NIELSEN, ERIK, Broughton, Caroline, Holmes, Chris

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

2022-01-016803/29/2022

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

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

The Effect of Pre-crash Seat Rotation with and without Feet Support in Highly Automated Vehicle Rear-end Crash

2022-01-104603/29/2022

Automotive industry aims at reaching higher levels of driving automation as described in SAE J3016TM. From the perspective of occupant safety, an automated driving system (ADS) shall provide safer conditions for highly automated vehicle (HAV) users compared to standard vehicles since the human error is excluded. In following decades, however, one can expect the mixed fleet of both standard and automated vehicles on roads. Therefore, collision between manually driven cars and HAVs are to be expected. For instance, a research’s result in 2019 showed that 60% of autonomous vehicles crashes was rear-ended. At the same time, future HAVs are mostly considered with non-standard seating configurations. That is, HAVs’ occupants access more room in the vehicle which allows them to rotate their seats to have comfortable positions. This brings a challenging topics for the safety of HAVs’ occupants considering various impact directions. This work aims to address this issue using tools of numerical

Talimian, Abbas, Vychytil, Jan

A Geographically Distributed Simulation Framework for the Analysis of Mixed Traffic Scenarios Involving Conventional and Autonomous Vehicles

2022-01-105403/29/2022

We report on a technical project that interfaced the National Advanced Driving Simulator (NADS) with SynChrono, a module of the Project Chrono open source platform, to enable real-time, physics-based simulation of multiple autonomous vehicles (AVs). In this setup, a driver at NADS, at the University of Iowa, participates in a traffic scenario that involves AVs that run at the University of Wisconsin-Madison on a cluster supercomputer. The NADS simulator is a driving simulator giving the “most realistic driving simulation experience in the country”. Thanks to its actuators, it can move across its 64-foot by 64-foot bay, rotate and tilt, to emulate vehicle movement and vibrations. In addition, the human driver drives in a full-size cab, surrounded by LED monitors, resulting in an immersive, high fidelity driving simulation experience. SynChrono draws on the Chrono::Vehicle module for detailed vehicle dynamics simulation, providing simulation of vehicle subsystems and tire models

Benatti, Simone, Schwarz, Chris, Young, Aaron, Elmquist, Asher, Serban, Radu, Negrut, Dan

Lane Detection and Pixel-level tracking for Autonomous Vehicles

2022-01-007503/29/2022

Lane detection and tracking play a key role in autonomous driving, not only in the LKA System but help estimate the pose of the vehicle. While there has been significant development in recent years, traditional outdoor SLAM algorithms still struggle to provide reliable information in challenging dynamic environments such as lack of roadside landscape or surrounding vehicles at almost the same speed or on the road in the woods. On the structured road, lane markings as static semantic features may provide a stable landmark assist in robust localization. As most of the current lane detection work mainly on separated images ignoring the relationship between adjacent frames, we propose a pixel-level lane tracking method for autonomous vehicles. In this paper, we introduce a deep network to detect and track lane features. The network has two parallel branches. One branch detects the lane position, while the other extracts the point description on a pixel level. In our approach, the

Wang, Yin, Wu, Jian, Wei, Zhenqi, He, Rui, song, Shiping

Mixed Reality Driving Simulator as a Training Tool for Autonomous Vehicles

2022-01-097303/29/2022

While manufacturers have invested billions of dollars in the self-driving car technology, little has been done to prepare drivers to this paradigm shift in transportation. According to a recent survey published by PAVE (Partners for Automotive Vehicle Education, 2020) of 1,200 adult drivers, 48% of American drivers say they “would never get into a taxi or ride-share vehicle that was driven autonomously”. To overcome these issues and to address this problem, we developed a highly immersive driving simulator which provides a safe platform for drivers of all ages and abilities to get a first hand experience of a self-driving vehicle. The proposed system provides a highly innovative use of mixed reality and can be easily retrofitted to any vehicle to provide mechanical immersion. Visual immersion is obtained through a mixed reality experience delivered through a Head Mounted Display. In this paper we will discuss the various mixed reality techniques that were implemented during the

Loeb, Helen S.

Construction of Personalized Driver Models based on LSTM using Driving Simulator.

2022-01-098103/29/2022

The purpose of this study is to construct personalized driver models using Long Short-Term Memory (LSTM). The driver model is used for automated driving systems and driver assistance systems and is expected to control longitudinal and lateral directions of vehicles. For example, driver models including the individual driving characteristics adapt the control of systems, which can reduce discomfort and nuisance to drivers. The LSTM models in this study enable the time series data processing. In some previous studies, LSTM models have been used to investigate pedestrian behaviors. However, there are few studies of driver behavior models based on LSTM. In this study, we measured the data of the driver's driving operation using a Driving Simulator (DS). DS consists of a visual display and a motion display, of which motion functions consist of a 6-axis motion device and a turntable. DS has a cockpit using an actual body of a vehicle, and a visual information projected on a cylindrical

Hamada, Ayaka, Oikawa PhD, Shoko, Hirose, Toshiya

Investigating approaches for qualifying virtual testing platforms for automated vehicle certification

2022-01-098203/29/2022

The safety validation and verification of Automated Driving Systems (ADS) represent a difficult challenge for regulators. Evaluating the performance of an ADS under diverse and complex conditions becomes prohibitive for conventional physical testing. Automotive safety experts are therefore proposing to complement the traditional physical testing with virtual testing to evaluate the performance of ADS in a comprehensive and cost-effective way for a wide range of traffic scenarios. Virtual testing is particularly suited to test ADS under safety critical scenarios that would be difficult and/or unsafe to reproduce on test tracks or public roads. While virtual testing has obvious advantages over the traditional track and real world testing approach, there is a need to ensure that the virtual ADS test results are accurate, representative and can provide confidence in the safety of the ADS, an area which has been less explored. The verification of virtual testing is also required to assess

Anctil, Benoit, Khastgir, Siddartha, Zhang, Jason, Burns, Peter, Jennings, Paul

Virtual Verification of Decision Making and Motion Planning Functionalities for Automated Vehicles in Urban Edge Case Scenarios

2022-01-101803/29/2022

Traffic congestion, road accidents, environmental pollution and driving stress are usually associated with road transportation in dense urban environments. The widespread deployment of automated vehicles has the potential to eliminate these issues; autonomous vehicles would allow for the optimization of traffic flow, the reduction of road accidents and environmental pollution, and the elimination of driving stress. Despite recent advancements in Automated Driving Systems (ADS), the deployment of such systems in dense urban environments still faces a challenging problem: in comparison to motorway or rural driving, urban environments contain a significantly greater number of traffic participants. This makes it difficult to verify the Safety Of The Intended Functionality (SOTIF) across the entire Operational Design Domain (ODD). One approach to solve this problem is to virtually evaluate and verify the safety of the ADS using simulation tools. Whereas traditionally simulated validation

Souflas, Ioannis, Lazzeretti, Ludovico, Ahrabian, Alireza, Niccolini, Lorenzo, Curtis-Walcott, Shona

A Comparative Study on Various Methodologies and Solutions for Evaluation of Short-Range Radar to Validate the Features of Autonomous Vehicle.

2021-26-046809/22/2021

Autonomous vehicle is a vehicle capable of sensing its environment and taking decisions automatically with no human interventions. To achieve this goal, ADAS (Advance Driving Assistance System) technologies play an important role and even today technologies are improving and emerging. The sensing of environment can be achieved with the help of sensors like Radar and Camera. Radar sensors are used in detecting the range, speed and directions of multiple targets using complex signal processing algorithms. Radar with long range and short range are widely used in the autonomous vehicles. Radar sensors with long range can be used to realize features like Adaptive Cruise Control, Advance Emergency Brake Assist. The short-range radar sensors are used for Blind Spot Monitoring, Lane Change Assist, Rear Pre-Crash System, Rear Cross Traffic Alert, Occupant safe exist, Trailer Merge Assist and Front Cross Traffic Alert. To realize the Autonomous Vehicle functionalities four short range radar

Sujeendra, M R, Kesana, Sindhu Prabha, Saddaladinne, Jagadeesh Babu

Robust Behavioral Cloning for Autonomous Vehicles Using End-to-End Imitation Learning

12-04-03-0023-TEST-REC08/19/2021

In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle. The training phase comprised of data collection, balancing, augmentation, preprocessing, and training a neural network, following which the trained model was deployed onto the ego vehicle to predict steering commands based on the feed from an onboard camera. A novel coupled control law was formulated to generate longitudinal control commands on the go based on the predicted steering angle and other parameters such as the actual speed of the ego vehicle and the prescribed constraints for speed and steering. We analyzed the computational efficiency of the pipeline and evaluated the robustness of the trained models through exhaustive experimentation during the deployment phase. We also compared our approach against state-of-the-art

Samak, Tanmay Vilas, Samak, Chinmay Vilas, Kandhasamy, Sivanathan

Robust Behavioral Cloning for Autonomous Vehicles Using End-to-End Imitation Learning

12-04-03-0023

08/19/2021

Samak, Tanmay Vilas, Samak, Chinmay Vilas, Kandhasamy, Sivanathan

Title updates jms

SAE-PP-0040008/13/2021

Abstract

Jackson, Alyssa

Automated Driving System Data Logger

J3197_202107-TEST-REC (Historical)07/23/2021

Event Data Recorder Committee

Automated Driving System Data Logger

J3197_202107 (Current)

07/23/2021

Event Data Recorder Committee

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

AVSC0000720210707/16/2021

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

Automated Vehicle Safety Consortium

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

J3216_202107 (Current)

07/16/2021

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

Cooperative Driving Automation(CDA) Committee

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

J3216_202107-TEST-REC (Historical)07/16/2021

Cooperative Driving Automation(CDA) Committee

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

J3206_202107 (Current)

07/07/2021

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

On-Road Automated Driving (ORAD) committee

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

J3206_202107-TEST-REC (Historical)07/07/2021

On-Road Automated Driving (ORAD) committee

Stepping Into SAE Level 3

21AVEP05_0905/01/2021

Honda and Toyota release new advanced driver-assistance systems that help to demarcate - or blur - the differences between SAE Level 2 and Level 3 capability. Honda and Toyota each recently announced the release of new, more-sophisticated driver-assistance systems for a special range of vehicles available in Japan starting in March 2021. Honda attracted global attention in November 2020, when it announced the Honda Legend fitted with Honda Sensing Elite would be the first vehicle ever awarded “type designation” from Japan's Ministry of Land, Infrastructure, Transport and Tourism (MLIT) for a driver-assistance system of SAE Level 3 capability. The Legend is joined by Toyota's 2022 Mirai and Lexus LS500h equipped with Advanced Drive, a system that, according to the company, embodies its “Mobility Teammate Concept.” This is an automated driving concept unique to Toyota that “seeks to enhance communication between drivers and cars, enable them to reach out to each other for mutual assist

Visnic, Bill

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

J3016_202104 (Current)

04/30/2021

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

On-Road Automated Driving (ORAD) committee