Browse Topic: Level 4 (High driving automation)

Items (133)

Implications of Off-road Automation for On-road Automated Driving Systems

EPR2023029

12/12/2023

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

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

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

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

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

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

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 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

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

J3016_202104-TEST-REC (Historical)04/30/2021

On-Road Automated Driving (ORAD) committee

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

2021-01-007504/06/2021

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

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

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

2021-01-006104/06/2021

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

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

Driving Automation System Test Scenario Development Process Creation and Software-in-the-Loop Implementation

2021-01-006204/06/2021

Automated driving systems (ADS) are one of the key modern technologies that are changing the way we perceive mobility and transportation. In addition to providing significant access to mobility, they can also be useful in decreasing the number of road accidents. For these benefits to be realized, candidate ADS need to be proven as safe, robust, and reliable; both by design and in the performance of navigating their operational design domain (ODD). This paper proposes a multi-pronged approach to evaluate the safety performance of a hypothetical candidate system. Safety performance is assessed through using a set of test cases/scenarios that provide substantial coverage of those potentially encountered in an ODD. This systematic process is used to create a library of scenarios, specific to a defined domain. Beginning with a system-specific ODD definition, a set of core competencies are identified. These core competencies are then considered both in isolation and in conjunction with other

Patil, Mayur, Lybarger, Alexander, Midlam-Mohler, Shawn, Stoddart, Evan

IAMTS Best Practice for A Comprehensive Approach for the Validation of Virtual Testing Toolchains

IAMTS000120210404/01/2021

This white paper by the International Alliance for Mobility Testing and Standardization (IAMTS) is the first release of an ongoing effort to develop best practices for validating virtual testing of automated driving systems (ADSs). It provides a best practices approach for validating simulation testing against physical baselines for the purposes of validation/homologation of an ADS. This approach is presented in four steps with examples of their application and an explanation of further work being pursued on this topic. This publication is funded by IAMTS members and is provided free of charge to the public.

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

AVSC0000620210303/25/2021

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

Automated Vehicle Safety Consortium

The Navigator

21AVEP03_0203/01/2021

When it comes to automated driving systems (ADS), Tesla CEO Elon Musk often runs counter to the consensus of almost everyone else in the sector. His disdain for lidar sensing is well-known, but during Tesla's April 2019 Autonomy Day, Musk said “high-precision GPS maps are a really bad idea.” Almost every other AV developer is on the other side of that idea - including sensor-systems powerhouse Mobileye - and makes extensive use of high-definition maps. During his now-annual keynote at the CES conference, Mobileye CEO Amnon Shashua discussed several aspects of the SAE Level 4 automated driving system the company is developing, but one section was devoted exclusively to mapping. In December 2020, Mobileye expanded its automated-driving development program to Detroit, with Tokyo, Shanghai and possibly New York being added this year.

The Sensing Systems That Make ADAS Work

TBMG-3863603/01/2021

To find out the latest information about sensors for automated driving systems, I interviewed Alberto Marinoni, Director of Product Marketing, TDK/Invensense (San Jose, CA).

Engineering FORD'S FUTURE

21AUTP03_0103/01/2021

Product platform and operations chief Hau Thai-Tang on navigating the microchip shortage, compact-unibody trucks, EV and AV challenges, and driving engineering efficiencies amid the lockdown. Security at Ford's Dearborn Proving Ground was tight on a frigid January day when we arrived for SAE's interview with Hau Thai-Tang. The gate guards were armed with infrared thermometers, and signs everywhere demanded masks be worn. Not even Bill Ford was permitted on site without a temperature check and PPE, we were told. The reasonable and safe ground rules for our wide-ranging conversation with Ford's chief product platform and operations officer included sitting 10 feet apart in an empty hall adjacent to the test track. We elevated our voices to cut through the mask muffling. A striking blue Mustang Mach-E provided a backdrop for talking electrification strategy. It also gave context for sharing his view of the other pandemic: the industry's worsening microchip shortage.

Brooke, Lindsay

Flash! Lidar's Next Generation Arrives

21AVEP03_0603/01/2021

Technology solutions from new players and alliances are poised to drive down cost. Electronics engineers may best know what typically happens in an emerging, rapidly moving technology space. First, innovation: Start-ups bring new solutions amid rounds of funding. Next is the often-protracted shakeout period. OEMs evaluate prototypes based on performance, quality, and cost; the technologies are refined. Product announcements are made. The “best way of doing it” becomes clearer. Then come the Tier 1s, bringing their proficiency in manufacturing and cost engineering. They take the most valid architectures, knock most if not all the cost out of them, and create a cost-effective solution, at scale.

Brooke, Lindsay

Level 2+: Taking High-Value ADAS Mainstream

21AVEP01_0301/01/2021

Enhanced SAE Level 2 automated driving systems avoid the cost, complexity - and uncertainty - of Level 3. Soon after SAE International released its J3016 “Levels of Automated Driving” standard in January 2014, engineers and product planners working in automated-vehicle development were probing the space between Level 2 and Level 3. New sensing and processor capabilities were poised to alter the lines of demarcation between L2's focus on driver assist and the controversial (and tricky) driver alerts and “handback” required by L3. There was room to expand the ADAS (advanced driver-assistance systems) frontier. Those doing the probing believed SAE Level 2 could be effectively built upon, with greater functionality and safety potential, as new innovations became available. The terms “enhanced L2” and “Level 2-point-something” coined by engineers gave way to a more market-friendly “Level 2+.” And as 2021 begins, Level 2+ is the dominant ADAS trend, engineers and analysts tell Autonomous

Brooke, Lindsay

The Navigator

21AVEP01_0201/01/2021

It's been nearly six years since word first leaked out of Cupertino, California, about Project Titan, the now near-mythical Apple Car project. While that program has stalled, or changed direction, or may never enter production, it still could make a significant contribution to the auto industry. Because thanks to Apple, lidar sensor production volumes are likely to explode - and cost to plummet - in the next few years. The only thing we know for certain about Project Titan is that it has a fleet of Lexus crossovers equipped with automated driving systems and it is testing them in California. Vehicles photographed on Bay Area roads have been festooned with a dozen or more Velodyne-made “puck” lidars, along with radar and cameras. However, given Apple's tendency for vertical integration, the expectation has been that it would follow in the path of Waymo and engineer its own sensors.

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

Automated Vehicle Safety Consortium

Startups emerge as accelerators

20TOFHP10_0410/01/2020

Newcomers are helping commercial-vehicle OEMs and Tier 1s speed up development processes. Speed has become a more critical factor as the commercial vehicle (CV) industry races to meet global market demands and stricter emissions regulations. The need for quicker decision-making and action is enabling startup companies to play a growing role, helping established manufacturers improve operations, even during this era of pandemic-driven isolation. Several speakers at the 2020 SAE COMVEC Digital Summit agreed that startups are gaining a foothold throughout the industry, supplying technologies and products that range from systems and software through full vehicles. For most startups, the ability to devise ways to augment development efforts by established companies is a critical step. Technologies like software and artificial intelligence are being used in new areas like electrified powertrains and autonomous operation, opening a space for young companies in an industry that has shown a

Costlow, Terry

TAKING AUTONOMY TO THE NEXT LEVEL

20TOFHP10_0210/01/2020

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

Gehm, Ryan

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

AVSC0000420200909/23/2020

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

Automated Vehicle Safety Consortium

Autonomy is Coming to Commercial Vehicles

20AVEP09_1009/01/2020

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

Gehm, Ryan

Unsettled Issues Facing Automated Vehicles and Insurance

EPR202001508/05/2020

This SAE EDGE™ Research Report explores how the deployment of automated vehicles (AVs) will affect the insurance industry and the principles of liability that underly the structure of insurance in the US. As we trade human drivers for suites of sensors and computers, who (or what) is responsible when there is a crash? The owner of the vehicle? The automaker that built it? The programmer that wrote the code?Insurers have over 100 years of experience and data covering human drivers, but with only a few years’ worth of information on AVs – how can they properly predict the true risks associated with their deployment? Without an understanding of the nature and risks of AVs, how can the government agencies that regulate the insurance industry provide proper oversight? Do the challenges AVs present require a total reworking of our insurance and liability systems, or can our current structures be adapted to fit them with minor modifications?{"bold":"Unsettled Legal Issues Facing Automated

Williams, Ian

The Navigator

20AVEP07_0207/01/2020

As predicted countless times over the past decade, 2020 has indeed proved to be an inflection point for the automated vehicle (AV) industry. Unfortunately for all companies and investors that have poured tens of billions of dollars into the technologies aimed at eliminating human drivers, that inflection is absolutely nothing like what was hoped for. The combination of a global pandemic and economic meltdown stacked on top of technological challenges mean that AVs aren't ready for prime time. And when they are ready, they may be used very differently.

AVSC Best Practice for Passenger-Initiated Emergency Trip Interruption

AVSC0000320200606/30/2020

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

Automated Vehicle Safety Consortium

Unsettled Topics in Automated Vehicle Data Sharing for Verification and Validation Purposes

EPR2020007

06/03/2020

Unsettled Topics in Automated Vehicle Data Sharing for Verification and Validation Purposes discusses the unsettled issue of sharing the terabytes of driving data generated by Automated Vehicles (AVs) on a daily basis. Perception engineers use these large datasets to analyze and model the automated driving systems (ADS) that will eventually be integrated into future “self-driving” vehicles. However, the current industry practices of collecting data by driving on public roads to understand real-world scenarios is not practical and will be unlikely to lead to safe deployment of this technology anytime soon. Estimates show that it could take 400 years for a fleet of 100 AVs to drive enough miles to prove that they are as safe as human drivers.Yet, data-sharing can be developed – as a technology, culture, and business – and allow for rapid generation and testing of the billions of possible scenarios that are needed to prove practicality and safety of an ADS – resulting in lower research

Khalkhali, Mohsen, Khalighi, Yaser