Testing Methods and Recommended Validation Strategies for Active Safety to Optimize Time and Cost Efficiency

<div class="section abstract"><div class="htmlview paragraph">New challenges and complexities are continuously increasing in advanced driver assistance systems (ADAS) development (e.g. active safety, driver assistant and autonomous vehicle systems). Therefore, the health management of ADAS’ components needs special improvements. Since software contribution in ADAS’ development is increasing significantly, remote diagnosis and maintenance for ADAS become more important. Furthermore, it is highly recommended to predict the remaining useful life (RUL) for the prognosis of ADAS’ safety critical components; e.g. (Ultrasonic, Cameras, Radar, LIDAR). This paper presents a remote diagnosis, maintenance and prognosis (RDMP) framework for ADAS, which can be used during development phase and mainly after production. An overview of RDMP framework’s elements is explained to demonstrate how/when this framework is connected to database servers and remote analysis servers. Moreover, Sensors fusion is used in RDMP to detect some sensor failures and even to predict their RUL. Additionally, some well-known machine learning algorithms (MLA) are used to predict RUL of ADAS’ components, and different types of input attributes to these MLA are proposed for some basic ADAS’ components. MLA use training data set, which shall be constructed ideally from actual records reported remotely by RDMP (Prognosis Analysis and Self-Learning System). However, initial dataset before production of the vehicle can be created from ADAS laboratory tests (e.g. Assessments on test tracks), ADAS simulation and theoretical analytical methods. Also, experiments of using the proposed RDMP in some ADAS’ components (Sensor fusion and Braking system as ADAS actuator) are presented. Summary, conclusion with proven results and future work are explained.</div></div>

With the rise of Advanced Driver Assistant Systems (ADAS) and the introduction of Highly Automated Driving (HAD), understanding and predicting road traffic accidents becomes increasingly important. Especially for the assessment of HAD/ADAS systems and of road safety, the precise prediction of the system’s impact on the occurrence of road traffic accidents is essential.Traffic simulations, as one option of virtual assessment, enable the assessment of safety systems in virtual test fields. By modelling the human driver, it is possible to simulate and predict future accident constellations and accident severities. In addition, the influence of vehicle factors, such as the presence of safety systems or their characteristics, on the occurrence of accidents can be investigated.This article elucidate virtual assessment by executing the whole procedure for an automatic emergency braking (AEB) system capable to detect cyclists at urban intersections. The authors implement a new driver behaviour model (DReaM) to predict road traffic accidents at urban intersections. Using the AEB as an example, the influence of different sensor opening angles on crash frequency is investigated by varying the sensors in three steps: 100°, 180°, and 210°. A total of 240,000 situations are simulated of which 6,674 are crashes. Within the two investigation scenarios, the crash frequency is reduced by up to 88.43%, or 93.92% by introducing the AEB system.The article shows that virtual assessment enables the prediction of new safety systems regarding road safety at early design stages. In addition, different system characteristics can be compared efficiently, e.g., the sensor opening angle.

<div class="section abstract"><div class="htmlview paragraph">This paper presents a cost effective development platform, named FEV-Driver, for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD). The FEV-Driver platform is an electric go-kart that was converted into an x-by-wire vehicle which represents the behavior of a full-scale electric vehicle. FEV-Driver has the advantage of being a small-scale vehicle that can be used with a significant lower safety risk compared to full-sized vehicles. The ADAS/AD algorithms for this platform were developed in both Simulink and C++ software and implemented within the Robot Operating System (ROS) middleware. Besides the description of the platform, Lane Keep Assist (LKA) and Automatic Emergency Braking (AEB) algorithms are discussed, followed by a path planning algorithm which enables the vehicle to drive autonomously after a manually controlled training lap. The modular system architecture allows for complete controller exchange or adaptation to different vehicles. The adaptation and implementation of the platform into a full-scale passenger vehicle is described in the last section of this paper. The presented platform has proven to be a low-cost scalable platform for development and verification of ADAS and AD functions.</div></div>

Objective: Forward collision warning (FCW) and automatic emergency braking (AEB) have the potential to prevent or mitigate many large truck crashes. While these systems are known to be effective in passenger vehicles, less is known about their effectiveness in large trucks. The objective of this study was to estimate the effectiveness of these systems in reducing real-world crash rates of large trucks. Methods: Data on Class 8 trucks operating on limited-access highways during 2017–2019 were obtained from SmartDrive Systems. Detailed data on exposure measures and crash circumstances were extracted from video footage by both automated means and manual coding. Crash rates were compared by front crash prevention technology (FCW, AEB, neither), both for all police-reportable crashes overall and for relevant crash types. Results: FCW was associated with a statistically significant 22% reduction in the rate of police-reportable crashes per vehicle miles traveled, and a significant 44% reduction in the rear-end crash rate of large trucks. AEB also was associated with significant reductions—12% overall and 41% for rear-end crashes. Warnings were issued in 31% of rear-end crashes for FCW-equipped trucks. AEB intervened in 43% of rear-end crashes; about two thirds of these interventions involved autobrake activations. On average, speed was reduced by over half between the time of the intervention and impact for both systems. Observed reductions in same-direction sideswipe and roadway departure crashes per mile traveled were smaller in magnitude than those of rear-end crashes; these were consistent with other crash avoidance technologies suspected to be bundled with FCW/AEB in some cases, and very few front crash prevention interventions occurred in these types of crashes. Conclusions: FCW and AEB are effective countermeasures for crashes in which large trucks rear-end other vehicles. Large truck safety is expected to improve as new trucks are increasingly equipped with these systems. FCW has the advantage that some of these systems can be retrofitted to existing trucks, so benefits can be realized sooner and with less investment.

<div class="section abstract"><div class="htmlview paragraph">This paper presents National Highway Traffic Safety Administration’s 2017 and 2018 test track research results with heavy vehicles equipped with forward collision warning and automatic emergency braking systems. Newly developed objective test procedures were used to perform and collect performance data with three single-unit trucks equipped with the crash avoidance systems. The results of this research show that the test procedures are applicable to many heavy vehicles and indicate that performance improvements in heavy vehicles equipped with these safety systems can be objectively measured.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Automotive industry is inclined towards connected, comfortable, environment friendly, efficient and smarter systems. Advanced Driver Assist System (ADAS) technology assist drivers to achieve a safer as well as better ride by automation and improvisation of the vehicular systems. With the advent of ADAS system, there is a significant focus not only in the development of Electronic Control Units (ECUs) and its features to cater to the emerging market but also on the information that could be displayed to meet the functional as well as safety requirements. This ADAS information display ensures timely notification to the driver with unique alerts that can be acoustic or visual. These systems should be tested thoroughly to ensure reliability as failures may impose severe risk on the OEM. Hardware in the loop testing has been largely adopted by industry against manual testing in lieu of the testing constraints imposed by the latter. This paper discusses on the existing test methods for ADAS information display system testing using HIL. It also details on the automation trends adopted and highlights the key challenges associated with the development as well as reusability of the conventional test methods. It discusses an Artificial Intelligence based strategy for automation of cluster objects teaching, evaluation and illustrates the adoption of the same for ADAS HIL testing and brings out the efficiency and benefits achieved by the proposed strategy.</div></div>

<div class="section abstract"> <div class="htmlview paragraph">This Recommended Practice provides a taxonomy for motor vehicle <i>driving automation systems</i> that perform part or all of the <i>dynamic driving task</i> (<i>DDT</i>) on a <i>sustained</i> basis and that range in level from <i>no driving automation</i> (level 0) to <i>full driving automation</i> (level 5). It provides detailed definitions for these six levels of <i>driving automation</i> in the context of <i>motor vehicles</i> (hereafter also referred to as “<i>vehicle</i>” or “<i>vehicles</i>”) and their <i>operation</i> on roadways. These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of <i>driving automation features</i> equipped on <i>motor vehicles</i> 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 <i>vehicles</i> of all classes and <i>driving automation</i> levels (including no <i>driving automation</i>), as well as motorcyclists, pedal cyclists, and pedestrians.</div> <div class="htmlview paragraph">The levels apply to the <i>driving automation feature(s)</i> that are engaged in any given instance of on-road <i>operation</i> of an equipped <i>vehicle</i>. As such, although a given <i>vehicle</i> may be equipped with a <i>driving automation system</i> that is capable of delivering multiple <i>driving automation features</i> that perform at different levels, the level of <i>driving automation</i> exhibited in any given instance is determined by the <i>feature(s)</i> that are engaged.</div> <div class="htmlview paragraph">This document also refers to three primary actors in <i>driving</i>: the (human) <i>driver</i>, the <i>driving automation system</i>, and other <i>vehicle</i> systems and components. These other <i>vehicle</i> systems (or the <i>vehicle</i> in general terms) do not include the <i>driving automation system</i> in this model, even though as a practical matter a <i>driving automation system</i> may actually share hardware and software components with other <i>vehicle</i> systems, such as a processing module(s) or operating code.</div> <div class="htmlview paragraph">The levels of <i>driving automation</i> are defined by reference to the specific role played by each of the three primary actors in performance of the <i>DDT</i>. “Role” in this context refers to the expected role of a given primary actor, based on the design of the <i>driving automation system</i> in question and not necessarily to the actual performance of a given primary actor. For example, a <i>driver</i> who fails to <i>monitor</i> the roadway during engagement of a level 1 adaptive cruise control (ACC) system still has the role of <i>driver</i>, even while s/he is neglecting it.</div> <div class="htmlview paragraph"><i>Active safety systems</i>, 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 <i>driving automation</i> taxonomy because they do not perform part or all of the <i>DDT</i> on a <i>sustained</i> basis and, rather, merely provide momentary intervention during potentially hazardous situations. Due to the momentary nature of the actions of <i>active safety systems</i>, their intervention does not change or eliminate the role of the <i>driver</i> in performing part or all of the <i>DDT,</i> and thus are not considered to be <i>driving automation</i>.</div> <div class="htmlview paragraph">It should, however, be noted that crash avoidance features, including intervention-type <i>active safety systems</i>, may be included in <i>vehicles</i> equipped with <i>driving automation systems</i> at any level. For <i>ADS</i>-equipped <i>vehicles</i> (i.e., levels 3-5) that perform the complete <i>DDT</i>, crash avoidance capability is part of <i>ADS</i> functionality.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Now-a-days, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology.</div><div class="htmlview paragraph">ADAS uses the technology like radar, vision and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicle for controlled and safe driving. And to integrate these advance technologies, the ADAS needs labeled data to train the algorithm to detect the various objects and moments of driver. Image annotation is one the well-known service to create such training data for computer vision.</div><div class="htmlview paragraph">There are number of open source annotated datasets available viz. COCO, KITTI etc. But these datasets are limited to either US or European road environment scenarios. There is hardly any dataset available for Indian Specific road conditions. In order to capture all the India Specific objects on road, we are capturing data through various environmental conditions, road conditions and annotating objects on the collected camera images. 2-D bounding boxes are being used to annotate objects in an image.</div><div class="htmlview paragraph">These annotated images will then be used to train a machine learning model. Finally this trained model will be then used to detect Indian specific objects with real time data acquired through camera sensors mounted on the vehicle. Object detection is one of the prime aspects for ADAS functionality. The complete process will demonstrate end to end solution for object detection techniques from data generation to real-time object identification and recognition.</div></div>

With the introduction of radars in the automotive domain, the automobile, as a product, has evolved greatly. Modern automobiles are highly advanced. Part of this advancement are the Active Safety (AS) systems and Advanced Driver Assistance Systems (ADAS). These systems work by gathering information about the vehicle's surroundings and processing them at a high speed. The main component of such systems is the automotive radar. Development of such systems poses a formidable challenge due to the complexity of the technical design and engineering involved; influenced by stringent quality assurance, reliability and safety standards. With the aim to alleviate some burden of development and improve the time-to-market, the following paper presents a strategy and a framework to analyze operational data from automotive radars. The paper illustrates the details of the approach in Hardware-in-the-loop (HIL), Software-in-the-loop (SIL) and real-world environments, while comparing with the conventional strategy. The paper enlists the advantages and disadvantages of using this approach. It also suggests methods to increase the speed of analysis.

<div class="htmlview paragraph">Digital maps have large potential to enhance or enable Advanced Driver Assistance Systems (ADAS) by extending driver horizon and therefore to contribute to safer, smarter and cleaner road transport. As a predictive sensor called ADAS Horizon, in-vehicle digital maps are an important source of information providing look-ahead capability for ADAS applications and providing further information for on-board sensors to enhance environment perception.</div> <div class="htmlview paragraph">This paper reports on results from the European Commission project MAPS&ADAS, part of PReVENT Integrated Project, dedicated to the development, test and validation of a standardized interface (ADAS Interface) between ADAS applications and ADAS map data sources for accessing map data regarding vehicle position.</div>

<div class="section abstract"><div class="htmlview paragraph">As a result of the development of Event Data Recorders (EDR) and the recent FMVSS regulation 49 CFR 563, today’s automobiles provide a limited subset of electronic data measurements of a vehicle’s state before and during a crash. Prior to this data, the only information available about the vehicle movements before or during a collision had come from physical evidence (e.g. tire marks), witnesses, aftermarket camera systems on vehicles, and ground-based cameras that were monitoring vehicle traffic or used for security surveillance. Today’s vehicles equipped with Advanced Driver Assistance Systems (ADAS) have vehicle-based sensors that measure information about the environment around a vehicle including other vehicles, pedestrians, and fixed wayside objects. Vehicles equipped with these ADAS systems use primarily radar, lidar, ultrasonic, and/or image sensors either in standalone operation or in combination to establish the range and movement of potential hazardous objects (e.g. other vehicles, poles, and pedestrians) around a vehicle. The data from these sensors is unique because it measures objective environment information surrounding a vehicle, which can play a role in reconstructing and understanding the contributing factors of an accident. As more vehicles become equipped with advanced safety systems and the requisite sensors for automated vehicles, accident investigators will need to become familiar with system functionality, the data that the sensors measure and may record, as well as the limitations of that data in order to effectively and accurately analyze a crash.</div><div class="htmlview paragraph">This paper reviews the sensors in ADAS collision avoidance systems that may be present on current vehicles and how the data from a vehicle based radar may be used to reconstruct a collision. A radar sensor mounted on a host vehicle is experimentally evaluated using stationary objects, and a stationary and moving radar. The results demonstrate how an investigator would use the radar data to recognize a fixed object in the environment and analyze the relative position with respect to the host vehicle. The analysis of this radar data includes the limitations, accuracy, and validation of the particular radar sensor data. In addition, the authors propose in general how data from radars could be used to elucidate the location of fixed objects in the environment surrounding a vehicle for the purpose of understanding an accident.</div></div>

PurposeAdvanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.Design/methodology/approachA random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.FindingsThe acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.Originality/valueFew research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

<div class="htmlview paragraph">This paper describes an effort to develop a valid and reliable process for comprehension testing of candidate automotive symbols and to conduct comprehension testing on a set of new symbols being considered for in-vehicle active safety systems. The comprehension testing process was developed though a multi-year effort, supported by Society of Automotive Engineering (SAE) and other organizations, aimed at generating a test methodology that would: yield high-quality comprehension data for new automotive symbols, provide clear and specific guidance back to symbol developers based on the test results, and could be adopted and performed internationally to support international standards efforts. Seventeen (17) candidate symbols were evaluated for three classes of in-vehicle active safety systems: forward collision warning (4 symbols), side collision warning (6 symbols), and lane departure warning (7 symbols). So far, testing has been completed in Germany, Sweden, Japan, and the United States. In the US testing, the study yielded comprehension data, appropriateness rankings, and diagnostic design feedback for all 17 icons tested. Based on these data, US recommendations have been made to the International Standards Organization (ISO) for all three classes of in-vehicle safety systems. This paper describes the process associated with developing the procedure, including the international outreach required to obtain support from major ISO countries, as well as the methods and results from the US testing.</div>

<div class="section abstract"><div class="htmlview paragraph">This paper compares the results from three human factors studies conducted in a motion-based simulator in 2008, 2014 and 2023, to highlight the trends in driver's response to Forward Collision Warning (FCW). The studies were motivated by the goal to develop an effective HMI (Human-Machine Interface) strategy that enables the required driver's response to FCW while minimizing the level of annoyance of the feature. All three studies evaluated driver response to a baseline-FCW and no-FCW conditions. Additionally, the 2023 study included two modified FCW chime variants: a softer FCW chime and a fading FCW chime. Sixteen (16) participants, balanced for gender and age, were tested for each group in all iterations of the studies. The participants drove in a high-fidelity simulator with a visual distraction task (number reading). After driving 15 minutes in a nighttime rural highway environment, a surprise forward collision threat arose during the distraction task. The response times from the FCW event were recorded and analyzed. The results indicated no statistically significant difference in driver response times between the baseline-FCW condition and the two new variants of the 2023 study. However, a trend of longer driver response times was seen when comparing the results from 2023 study to previous studies. First, the median response times for the baseline-FCW group increased from 2008 to 2023 by about 0.25s. Furthermore, in the 2008 study, a few cautious drivers in the control group had reacted even before the distraction task had finished. However, in the 2023 study, all participants only reacted after the distraction task had finished. Finally, there was a statistically significant difference between the 2023 fading-FCW chime variant and the 2008 baseline-FCW. All of this indicates that drivers are likely becoming more complacent to warnings and are less situationally aware due to distraction from non-driving activities.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Advanced driver assistance systems (ADAS) and automated driving systems (ADS) continue to expand into the market at a rapid pace. As improved (i.e., next generation) versions of these systems become available, they will continue to face many challenges in their implementation and benefits for safety and driving operations. The solution will involve many parties, including road safety professionals and researchers who see the potential in these systems but may have difficulties keeping up with them, and safety advocates who are calling for these systems to achieve higher levels of safety now.</div><div class="htmlview paragraph"><b>The Challenges of Next-gen ADAS and ADS and Related Vehicle Safety Topics</b> explores these challenges that will fall on the National Highway Traffic Safety Administration (NHTSA) and automakers as they balance costs and benefits; establish reasonable regulations and standards; and determine how to improve, test, deliver, and use these systems successfully. Perhaps the most formidable challenge will be overcoming the expectation that ADAS—and especially ADS—will always work perfectly in every scenario.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>