Browse Topic: Level 2 (Partial driving automation)

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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
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
Design of a Mild Hybrid Electric Vehicle with CAVs Capability for the MaaS Market2020-01-14374/14/2020
There is significant potential for connected and autonomous vehicles to impact vehicle efficiency, fuel economy, and emissions, especially for hybrid-electric vehicles. These improvements could have large-scale impact on oil consumption and air-quality if deployed in large Mobility-as-a-Service or ride-sharing fleets. As part of the US Department of Energy's current Advanced Vehicle Technology Competition (AVCT), EcoCAR: The Mobility Challenge, Mississippi State University’s EcoCAR Team is redesigning and doing the development work necessary to convert a conventional gasoline spark-ignited 2019 Chevy Blazer into a hybrid-electric vehicle with SAE Level 2 autonomy. The target consumer segments for this effort are the Mobility-as-a-Service fleet owners, operators and riders. To accomplish this conversion, the MSU team is implementing a P4 mild hybridization strategy that is expected to result in a 30% increase in fuel economy over the stock Blazer. MATLAB models of the vehicle system shows the potential for additional improvement with the use of connected and autonomous features in the vehicle. This paper presents the design rationale for selection of the P4 strategy, vehicle modeling, and fuel economy simulation results completed during Year 1 of the competition. A detailed discussion of further improvements arising from incorporating connected and autonomous technology strategies, focusing on longitudinal control methods is also presented.
Taoudi, Amine, Haque, Moinul Shahidul, Strzelec, Andrea, Follett, Randolph
GM deploying electricals of the future20AUTP03_083/1/2020
In tandem with its corporate strategy to “skip” vehicle hybridization and focus solely on electric vehicles (EVs) for future propulsion (see cover story), General Motors recently offered media a deeper look at the company's all-new vehicle electrical architecture that was first confirmed in mid-2019, saying the vehicle-wide electrical system is associated with more than 100 new patents. The advanced “digital vehicle platform,” known internally as Global B, debuted with the 2020 Cadillac CT5 sedan. It serves as the electrical foundation for the 2020 Chevrolet Corvette Stingray and the 2021 Chevrolet Tahoe and Suburban, GM Yukon and Cadillac Escalade and will carry into all of the company's future conventionally-powered and EV models.
Visnic, Bill
Tier 1s tout newest mobility tech at CES 202020AUTP02_091/1/2020
The mammoth annual techfest formerly known as the Consumer Electronics Show has become a must-attend for the mobility industry, with dozens of displays by OEMs and suppliers of all dimensions. At this year's CES, Automotive Engineering was impressed by new developments in ADAS and AV sensing and processing, V2X connectivity, vehicle cabin sensing and related technologies, electrification advances and a number of vehicle introductions. Highlights of our reporting include:
Brooke, Lindsay
Democratize AV Technology!20AVEP01_041/1/2020
Aptiv's new generation of open-sourced architectures based on a few central processors aims to speed AV adoption. CTO Glen DeVos explains. The software-intensive, electrified and increasingly automated vehicle will define the 2020s. Its rise is driving both the industry-wide re-thinking of electrical architectures and the growth of engineering employment behind it. At the forefront of this trend is Aptiv, the technology Tier 1 spun off from Delphi in 2017. It now has more than 19,000 engineers among its 160,000 staff, comprising one of the highest engineer-to-employee ratios among large suppliers. “We've been adding about 1,500 engineers per year, primarily in software and systems engineering, at our 15 major technical centers,” said CTO Glen DeVos. These resources, he noted, will help Aptiv accelerate its customers' development of new vehicle platforms with greater active-safety capability, including automated-driving functionality.
Brooke, Lindsay
HEAT SEEKERS20AUTP02_011/1/2020
While engineers debate the use of thermal-imaging sensors for ADAS, their capability and value are being proven for AVs of all levels. What specific sensor types will comprise the advanced driver-assistance systems (ADAS) of the 2020s? That's a controversial subject among engineers who are developing SAE Level 2 and 3 (and the so-called “L2+”) ADAS sensing suites for new vehicles. Many of them believe that visible-light cameras fused with radar will suffice to deliver the object-identification accuracy, redundancy-and cost effectiveness-that OEMs and the driving public expect of ADAS-equipped vehicles. But a case is building for additional sensing capability, particularly for automatic emergency braking (AEB) and pedestrian-detection. Those safety-critical functions currently rely on camera-radar inputs to “see” ahead. They enable the vehicle to react to a range of scenarios-from stalled traffic on a highway to humans and animals suddenly appearing in the road. (In Michigan alone, there were 53,464 traffic accidents involving deer in 2018, up 14% from 2016, according to state DoT data.)
Brooke, Lindsay
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:
WHAT WE'RE DRIVING19AUTP09_089/1/2019
As the sporting player in Volvo's C-segment sedan lineup, this all-wheel drive, turbocharged-and-supercharged R-Design S60 faces benchmark competition in the BMW 3-Series, Audi A4 and Mercedes-Benz C-Class. In pure performance, the stylish Swede stacks up nicely against the Germans: Its 316 hp (236 kW) pushes the 3,900 lb (1769 kg) car plus my 200-lb (91 kg) of dead weight from a standstill to 60 mph (97 km/h) in 5.5 s, according to my stopwatch. Dynamically, the S60 T6 (not my choice of nomenclature, as it's a 4-cyl., not a six) feels solid and secure, if not exceptionally nimble, when pushed hard in the corners. But where the S60 R-Design clearly beats the others is inside, the area where Volvo shows its taillamps to the other premium brands. The S60's cabin is a beautifully integrated blend of rich materials and tasteful accents - brushed aluminum of the lower IP contrasting with deep ebony, offset by delightful-to-touch control knobs. The front seats are both handsome and comfortable. Rear-seat legroom is excellent, as my 37-in. (940-mm) inseam can attest.
SUPPLIER EYE19AUTP09_079/1/2019
The value - and risks - of Technology Testbeds The world has been awaiting the arrival of the latest generation Chevrolet Corvette for some time. This past July, among much fanfare the mid-engined C8 (8th generation of this iconic nameplate) Stingray was revealed to the world. As an early owner of the C7, I know my car will be the last front-engined Corvette. And while some fellow enthusiasts are defiantly asserting that “real” Corvettes have their engines ahead of the driver, there is no looking back. Nostalgia aside, this industry is always in need of ‘technology testbeds’, a role that Corvette has played since the C1 debuted in 1953. Such production vehicles stretch the limit in terms of performance, design, efficiency, or all of the above. During the past decade, General Motors has introduced other products which the organization, suppliers and dealers have been using as incubators for related technologies, business processes, and new sourcing models.
Dining on Data19AVEP05_065/1/2019
Processing, in real-time, the enormous data stream that's flowing through AVs is increasingly the job of NVIDIA's mighty GPUs. Danny Shapiro relishes the feast. Every year, NVIDIA hosts its GPU Technology Conference (GTC), a multi-day news conference/expo to highlight the advances the tech company has made in the past 12 months. In 2018, the big automotive news at GTC was the introduction of DRIVE Constellation, a simulation system designed to “train” the artificial intelligence (AI) algorithms used for autonomous driving. One year and countless line of code updates later, NVIDIA announced at GTC 2019 that the Toyota Research Institute-Advanced Development (TRI-AD) would be the first publicly named customer for DRIVE Constellation. Danny Shapiro, NVIDIA's senior director of automotive, could have used the moment as an excuse to celebrate. Instead, he was all business as he sat down with SAE's Autonomous Vehicle Engineering and contemplated the work still to do.
Blanco, Sebastian
An Immersive Vehicle-in-the-Loop VR Platform for Evaluating Human-to-Autonomous Vehicle Interactions2019-01-01434/2/2019
The deployment of autonomous vehicles in real-world scenarios requires thorough testing to ensure sufficient safety levels. Driving simulators have proven to be useful testbeds for assisted and autonomous driving functionalities but may fail to capture all the nuances of real-world conditions. In this paper, we present a snapshot of the design and evaluation using a Cooperative Adaptive Cruise Control application of virtual reality platform currently in development at our institution. The platform is designed so to: allow for incorporating live real-world driving data into the simulation, enabling Vehicle-in-the-Loop testing of autonomous driving behaviors and providing us with a useful mean to evaluate the human factor in the autonomous vehicle context.
Rastogi, Vibhor, Merco, Roberto, Kaur, Manveen, Rayamajhi, Anjan, Gavelli, Marco, Papa, Gianluca, Pisu, Pierluigi, Babu, Sabarish, Robb, Andrew, Martin, Jim
The Swedish Word for AV Tech19AVEP03_083/1/2019
Veoneer, a new Tier 1 supplier with well-established roots, is moving rapidly into AI, says veteran research boss Ola Boström. The first automotive camera with built-in Deep Learning-considered to be a significant step forward in autonomous-vehicle sensor technology-is due to launch this year. It was developed and will be manufactured by a Tier 1 whose name is still seeking broader recognition in the industry. “What is a…Veoneer?” asked an engineer waiting in the taxi line at this year's CES, when a monorail train wrapped with the company's logo flashed past above. Building brand cred among countless other spin-offs and tech start-ups who are clawing for visibility takes time, of course, even for the independent, publicly-traded Swedish company with 8,600 employees, formerly known as Autoliv's Electronics segment.
Brooke, Lindsay
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
2021-12-29.TEST A Systematic Scenario Typology for Automated Vehicles Based on China-FOT2018-01-00394/3/2018
To promote the development of automated vehicles (AVs), large scale of field operational tests (FOTs) were carried out around the world. Applications of naturalistic driving data should base on correlative scenarios. However, most of the existing scenario typologies, aiming at advanced driving assistance system (ADAS) and extracting discontinuous fragments from driving process, are not suitable for AVs, which need to complete continuous driving tasks. In this paper, a systematic scenario-typology consisting of four layers (from top to bottom: trip, cluster, segment and process) was first proposed. A trip refers to the whole duration from starting at initial parking space to parking at final one. The basic units ‘Process’, during which the vehicle fulfils only one driving task, are classified into parking process, long-, middle- and short-time-driving-processes. A segment consists of two neighboring long-time-driving processes and a middle or/and short one between them. A cluster refers to a series of segments during which the vehicle drives on the same type of roads. Using this typology, a trip can be divided into continuous fragments at all the four layers, making the functional definition and test & evaluation possible. An application in functional definition was carried out as example. Naturalistic driving data from China-FOT were analyzed to obtain statistics in travel time and mileage. In shielded roads, high speed cruise and automatic lane change are in high demand. In open roads, automatic lane change and intersection passing are urgent demanded. Method and results from this paper fill the gap of studies on scenarios typology for AVs.
Liu, Lin, ZHU, Xichan, Chen, Mingyang, Ma, Zhixiong
XPONENTIAL 2018 – An AUVSI ExperienceTBMG-287794/1/2018
The Association for Unmanned Vehicle Systems International (AUVSI) is bringing this year's XPONENTIAL 2018 to the Colorado Convention Center in Denver, CO. The event, which runs from April 30 – May 3, will feature more than 200 presentations and panel discussions focused on all aspects of the unmanned vehicle and robotics market. Over 725 exhibitors representing more than 20 different industries will be showcasing their latest technology to an estimated 7,000 attendees from all over the world.
Toyota rethinks THE FLAGSHIP17AUTP11_0211/1/2017
The 2018 Lexus LS moves to twin-turbo V6 power and piles on the takumi. Toyota has engineered a complete road-to-roof redo of its flagship Lexus LS sedan, dropping the V8 engine in favor of a twin-turbo V6, adding Aisin's new 10-speed planetary automatic and retaining a hybrid-electric powertrain option-and that's just the beginning. The 2018 model marks the fifth generation of the luxury sedan that rocked the luxury-sedan establishment almost 30 years ago. While the LS has represented less than 2% of the brand's U.S. sales in recent years, its significance to Toyota exceeds its sheer numbers. Chief Engineer Toshio Asahi and his team began work on the new LS500 and hybrid LS500h in late 2011. One key goal was to attract a younger buyer with what Asahi calls “Brave Design,” picking up the Lexus family ‘spindle’ grille theme and developing a longer, more muscular exterior form. Lowering the hood by 1.2 in (30.5 mm), the decklid by 1.6 in (40.6 mm) and dropping the roof 0.6 in (15 mm) versus the 2017 LS helps the new version look more ground-hugging as well.
Sessions, Ron
THE NAVIGATOR17AUTP11_0811/1/2017
The uncanny valley of partially-automated driving Automated driving has the potential to be the most disruptive force in transportation since Henry Ford rolled out the Model T. The potential impact on an industry that generates an estimated $4 trillion in annual revenues globally is enormous. No company wants to be left without a chair when the music stops. However, in that rush to win the automation race, are automakers producing interim products that might be better left parked in the storage lot behind the research labs?
Abuelsamid, Sam
SUPPLIER EYE17AUTP11_0711/1/2017
Facing the new disruptors Industry leaders remain so by staying ahead of the next ‘big’ trend. They view emerging shifts in business and technology as opportunities, regardless of the disruption such trends portend. Even when faced with unexpected (i.e., a natural disaster) or expected (global regulatory) shifts, suppliers must be agile and decisive. Ignorance of disruption is not an excuse. Recent history is littered with examples of the swift exit of key technologies that were once the lifeblood of many suppliers. Sparkplug wires, incandescent bulbs, mechanical throttle control-the list is long indeed. In many cases, the decline was signaled well in advance and took several vehicle cycles to unfold-allowing those impacted to integrate new capabilities, find partners or divest. Other times, the decline was rapid and resulted in hard decisions with few alternatives.
Robinet, Michael
THE NAVIGATOR17AUTP10_0710/1/2017
Time for standard naming of safety features Smart cruise control. Intelligent cruise control. Adaptive cruise control. Radar speed control. As The Bard wrote so long ago, “A rose by any other name would smell as sweet.” Sadly that tale did not end well for the protagonists. In today's world of increasingly sophisticated active safety systems, engineers and consumers alike are being bombarded by more and more brand-specific labels for essentially the same technology. Unfortunately, imprecise branding driven more by marketers than technologists threatens to put us all at risk.
Abuelsamid, Sam
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
Automation of Road Vehicles Using V2X: An Application to Intersection Automation2017-01-00783/28/2017
Today, automated vehicles mostly rely on ego vehicle sensors such as cameras, radar or LiDAR sensors that are limited in their sensing capability and range. Vehicle-to-everything (V2X) communication has the potential to appropriately complement these sensors and even allow for a cooperative, proactive interaction of vehicles. As such, V2X communication might play a vital role on the way to smart and efficient traffic solutions. In the public funded research project UK Autodrive, we are currently investigating and experimentally evaluating V2X-based applications based on dedicated short range communication (DSRC). Moreover, the novel application intersection priority management (IPM) is part of the research project. IPM aims at automating intersections in such a way that vehicles can pass safely and even more efficiently without the use of traffic lights or signs. After a brief outline of our research project and its experimental setup, we will discuss the challenges that arise with IPM. Assuming fully automated vehicles as a first step, we propose a control concept based on distributed model predictive control to solve the intersection automation problem. Compared to previous concepts, our control scheme is less restrictive such that more vehicles can pass the intersection simultaneously and allows for an implementation in an actual vehicle due to a fully parallelized optimization approach. Finally, first simulation results highlight the efficacy of the proposed control concept.
Katriniok, Alexander, Kleibaum, Peter, Ress, Christian, Eckstein, Lutz
Region Proposal Technique for Traffic Light Detection Supplemented by Deep Learning and Virtual Data2017-01-01043/28/2017
In this work, we outline a process for traffic light detection in the context of autonomous vehicles and driver assistance technology features. For our approach, we leverage the automatic annotations from virtually generated data of road scenes. Using the automatically generated bounding boxes around the illuminated traffic lights themselves, we trained an 8-layer deep neural network, without pre-training, for classification of traffic light signals (green, amber, red). After training on virtual data, we tested the network on real world data collected from a forward facing camera on a vehicle. Our new region proposal technique uses color space conversion and contour extraction to identify candidate regions to feed to the deep neural network classifier. Depending on time of day, we convert our RGB images in order to more accurately extract the appropriate regions of interest and filter them based on color, shape and size. These candidate regions are fed to a deep neural network. In this paper, we focus on a region of interest (ROI) proposal method, which works to minimize false negative and false positive candidate regions that are then fed to the deep neural network for classification. This camera-only solution has applications for many levels of autonomy, from driver assistance technology (SAE Level 2) to fully automated vehicles (SAE Level 4).
Moosaei, Maryam, Zhang, Yi, Micks, Ashley, Smith, Simon, Goh, Madeline J., Nariyambut Murali, Vidya
Human Factors Definitions for Automated Driving and Related Research TopicsJ3114_201612 (Current)12/8/2016
The aim of this Information Report is to provide terms and definitions that are important for the user’s interaction with L2 through L4 driving automation system features per SAE J3016, which provides a basis for this document.
Driving Automation Systems Committee
I.D. Concept heralds VW's electric Bolt fighter for 202016AUTP11_0911/1/2016
“Our future is in efficient and ‘connected,’” said Volkswagen CEO Herbert Diess as he introduced his company's battery-electric product future at the 2016 Paris Motor Show. The VW I.D. Concept, with a claimed range of up to 373 mi (600 km) on a single charge, heralds a blitz of new electrified vehicles based on the company's new MEB modular platform dedicated to battery-electric models. A production version of the I.D. is planned for 2020, Diess said, with an autonomous version following by 2025. Diess compared the new BEV to the original VW Beetle, promising “it will be a real people's car” priced about the same as today's Golf.
Brooke, Lindsay
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
Harman advanced concept brings intuitive HMI to automated driving16MEIP06_126/1/2016
Harman International is demonstrating an advanced connected-car platform that previews a fundamental shift in the human-machine interface of automated and autonomous vehicles. For this project Harman is collaborating with Swiss-based concept vehicle specialist Rinspeed, which developed the BMW i8-based Etos demonstrator featuring V2X (vehicle to everything) capability and incorporating the latest Microsoft driver “assistant.” The Harman platform, known as LIVS (Life-Enhancing Intelligent Vehicle Solution), is based on RoadLINK technology from NXP Semiconductors. The demonstrator shows how vehicle occupants will safely interact with the outside world, whether driving in SAE Level 2 or 3 automated mode, or in fully autonomous SAE Level 5.
Adcock, Ian
Situation Awareness, Scenarios, and Secondary Tasks: Measuring Driver Performance and Safety Margins in Highly Automated Vehicles2016-01-01454/5/2016
The rapid increase in the sophistication of vehicle automation demands development of evaluation protocols tuned to understanding driver-automation interaction. Driving simulators provide a safe and cost-efficient tool for studying driver-automation interaction, and this paper outlines general considerations for simulator-based evaluation protocols. Several challenges confront automation evaluation, including the limited utility of standard measures of driver performance (e.g., standard deviation of lane position), and the need to quantify underlying mental processes associated with situation awareness and trust. Implicitly or explicitly vehicle automation encourages drivers to disengage from driving and engage in other activities. Thus secondary tasks play an important role in both creating representative situations for automation use and misuse, as well as providing embedded measures of driver engagement. Latent hazards-hazards that exist in the road environment and merit driver attention, but do not materialize to require a driver response-have been used with great success for understanding the vulnerability of novice drivers. Latent hazards might provide a similarly useful index of driver attention to the road during periods where the automation is vulnerable to failure. With highly automated vehicles, latent hazards include potential roadway threats that might not be sensed by the automation and would require driver attention. This paper describes driving simulator scenarios used to operationalize automation-relevant latent hazards, secondary tasks tuned to index driver disengagement from the driving task, and measures that reflect safety margins rather than driving performance, such as drivers’ trust, situation awareness, and expected time to transition to manual control.
Gibson, Madeleine, Lee, John, Venkatraman, Vindhya, Price, Morgan, Lewis, Jeffrey, Montgomery, Olivia, Mutlu, Bilge, Domeyer, Joshua, Foley, James
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