Browse Topic: Level 5 (Full driving automation)

Items (61)
When vehicle accidents occur, investigators rely on event data recorders for accident investigations. However current event data recorders do not support accident investigation involving automated or self-driving vehicles when there is state information that needs to be recorded, for example ADS modes, changes in the ODD that the vehicle operates under, and the various states of vehicle features such as intelligent cruise control, automated lane changes, autonomous emergency braking, and others. In this paper, we propose a model to design new types of event data recorders that supports accident investigations involving automated vehicles when there is state information to be recorded. The model is generic enough to be adapted to any automation level and any set of automated vehicle functional features. The model has been instantiated to a specific ADAS system.
Pimentel, Juan
The Automated Mobility Partnership (AMP) is a consortium of industry and academic stakeholders dedicated to advancing Automated Driving Systems (ADS) through a comprehensive suite of tools, datasets, and methodologies. The AMP portal integrates events from over 35 million miles of naturalistic driving data including thousands of annotated crashes and near-crashes and a decade of U.S. police-reported crash data curated by the Virginia Tech Transportation Institute. The portal enables data discovery, visualization, processing, and analysis through secured web access. This paper briefly describes the AMP portal and examines its utility in developing and evaluating the safety of ADS using standardized processes. For the examination, we provide examples based on generic automated driving functions, guided by the Safety of the Intended Functionality (SOTIF) framework. The results show that AMP is instrumental in identifying recorded real-world cases in which the hazardous behavior of a
Antona-Makoshi, JacoboWilliams, VickiAli, GibranSullivan, KayeTerranova, PaoloKefauver, KevinHatchett, Alex
The recent advancements in fields such as sensors, AI, and IoT are majorly impacting the automotive industry. Automated Driving Systems (ADS) are developing rapidly, meaning that SAE J3016 Level 3 and above vehicles are quickly becoming a reality. As a result, maintenance of such systems becomes essential to ensure their safe and efficient operation. Prognostic techniques in particular are crucial to monitor the state of health and predicting the end of life for components. Prognostics engineering is being applied in many industries and for conventional automotive applications, but ADS is new technology, and the prognostics for these systems are still being developed and adapted. In this paper, we first present a review of the most used prognostic techniques across different safety-critical domains such as aerospace, power, and manufacturing. Then, we summarize the main challenges that must be faced to successfully develop novel approaches for prognostics of ADS components and provide
Merola, FrancescoHanif, AtharLami, GiuseppeAhmed, QadeerMonohon, Mark
As longitudinal Automated Driving System (ADS) technologies, such as Adaptive Cruise Control (ACC), become more prevalent, robust testing frameworks that encompass both simulation and vehicle-in-the-loop (VIL) methodologies are essential to ensure system reliability, safety, and performance refinement. Although significant research has focused on ACC algorithm development and simulation testing, existing VIL dynamometer testing frameworks are typically tailored to specific vehicle models and sensor simulation tools. These highly customized approaches often fail to account for broader interoperability while overlooking energy consumption as a key performance metric. This paper presents a novel modular framework for ACC dynamometer testing, designed to enhance interoperability across a diverse range of vehicle platforms, simulation tools, and dynamometer facilities with a focus on evaluating impacts of automated longitudinal control on the overall energy consumption of the vehicle. The
Goberville, NicholasHamilton, KaylaDi Russo, MiriamJeong, JongryeolDas, DebashisOrd, DavidMisra, PriyashrabaCrain, Trevor
One of the major issues facing the automated driving system (ADS)-equipped vehicle (AV) industry is how to evaluate the performance of an AV as it navigates a given scenario. The development and validation of a sound, consistent, and transparent dynamic driving task (DDT) assessment (DA) methodology is a key component of the safety case framework (SCF) of the Automated Vehicle – Test and Evaluation Process (AV-TEP) Mission, a collaboration between Science Foundation Arizona and Arizona State University. The DA methodology was presented in earlier work and includes the DA metrics from the recently published SAE J3237 Recommended Practice. This work extends and implements the methodology with an AV developed by OEM May Mobility in four diverse, real-world scenarios: (1) an oncoming vehicle entering the AV’s lane, (2) vulnerable road user (VRU) crossing in front of the AV’s path, (3) a vehicle executing a three-point turn encroaches into the AV’s path, and (4) the AV exhibiting aggressive
Wishart, JeffreyRahimi, ShujauddinSwaminathan, SunderZhao, JunfengFrantz, MattSingh, SatvirComo, Steven Gerard
Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV
Wichner, DavidWishart, JeffreySergent, JasonSwaminathan, Sunder
As the autonomy of ADAS features are moving from SAE level 0 autonomy to SAE level 5 autonomy of operation, reliance on AI/ML based algorithms in ADAS critical functions like perception, fusion and path planning are increasing predominantly. AI/ML based algorithms offer exceptional performance of the ADAS features, at the same time these advanced algorithms also bring in safety challenges as well. This paper explores the functional safety aspects of AI/ML based systems in ADAS functions like perception, object fusion and path planning, by discussing the safety requirements development for AI/ML systems, dataset safety life cycle, verification and validation of AI systems, and safety analysis used for AI systems. Among all the safety aspects listed above, emphasis is put on dataset safety lifecycle as that is not only the most important element for training ML based algorithms for ADAS usage, but also the most cumbersome and expensive. The safety characteristics associated with dataset
Mudunuri, Venkateswara RajuAlmasri, HossamFan, Hsing-HuaChandrasekaran, Mukund
The rapid development of open-source Automated Driving System (ADS) stacks has created a pressing need for clear guidance on their evaluation and selection for specific use cases. This paper introduces a scenario-based evaluation framework combined with a modular simulation framework, offering a scalable methodology for assessing and benchmarking ADS solutions, including but not limited to off-the-shelf designs. The study highlights the lack of clear Operational Design Domain (ODD) descriptions in such systems. Without a common understanding, users must rely on subjective assumptions, which hinders the process of accurate system selection. To address this gap, the study proposes adopting a standardised ISO 34503 ODD description format within the ADS stacks. The application of the proposed framework is showcased through a case study evaluating two open-source systems, Autoware and Apollo. By first defining the assumed system’s ODD, then selecting a relevant scenario, and establishing
Chodowiec, EmilZhang, XizheMitchell, JoeBaker, PeterKhastgir, SiddarthaJennings, Paul
The rapid development of autonomous vehicles necessitates rigorous testing under diverse environmental conditions to ensure their reliability and safety. One of the most challenging scenarios for both human and machine vision is navigating through rain. This study introduces the Digitrans Rain Testbed, an innovative outdoor rain facility specifically designed to test and evaluate automotive sensors under realistic and controlled rain conditions. The rain plant features a wetted area of 600 square meters and a sprinkled rain volume of 600 cubic meters, providing a comprehensive environment to rigorously assess the performance of autonomous vehicle sensors. Rain poses a significant challenge due to the complex interaction of light with raindrops, leading to phenomena such as scattering, absorption, and reflection, which can severely impair sensor performance. Our facility replicates various rain intensities and conditions, enabling comprehensive testing of Radar, Lidar, and Camera
Feichtinger, Christoph Simon
A significant challenge to the scalability of automated driving systems is the potential unavailability of GPS information for localization. To address this issue, a methodology using a static 2D map of road and lane geometry and vehicle on board sensors data is proposed to ensure reliable localization and navigation for automated vehicles in GPS-denied situations. In this study, a dead reckoning system based on vehicle kinematics is implemented by using onboard sensor data from the vehicle's Controller Area Network (CAN). However, the kinematic dead reckoning estimate has error accumulation, the drift in the dead reckoning position estimate is eliminated by using an arc-length based map matching approach. This innovative approach was tested and validated at various safety-critical intersection scenarios, including four-way intersection, roundabout, slip-lane intersection, and curved road. This approach ensures the continuous and reliable localization of automated vehicles, thereby
Javed, Nur UddinSingh, YuvrajTan, ShengzheAhmed, Qadeer
Systematic testing of Automated Driving Systems (ADS) requires finding relevant test cases. The extraction of critical cases, also called edge or corner cases, from naturalistic driving data is a complex task and often prone to multiple errors. Large Language Models (LLMs) have been employed for virtual testing of ADS in recent years; however, quantitatively benchmarking LLMs’ performance in this task has been barely investigated. In this paper, based on the characteristics of different LLMs, six LLMs were selected for benchmarking the LLMs’ ability to understand ADS functional scenarios on motorways. A novel scenario classification model was introduced to enhance the granularity of data categorization for motorway driving scenarios. Different driving scenarios, described in natural language, were defined for testing the capability of these LLMs to understand various scenarios and convert them into standardized structured data. To perform the benchmarking in a standardized manner, the
Zhou, JiZhao, YongqiYang, AixiEichberger, Arno
Vehicles equipped with automated driving systems (ADS) may have non-traditional seating configurations, such as rear-facing for front-row occupants. The objectives of this study are (1) to generate biomechanical corridors from kinematic data obtained from postmortem human subjects (PMHS) sled tests and (2) to assess the biofidelity of the Global Human Body Models Consortium (GHBMC) 50th male (M50-O) v6.0 seated in an upright (25-deg recline) Honda Accord seat with a fixed D-ring (FDR) in a 56 km/h rear-facing frontal impact. A phase optimization technique was applied to mass-normalized PMHS data for generating corridors. After replicating the experimental boundary conditions in the computational finite element (FE) environment, the performance of the rigidized FE seat model obtained was validated using LSTC Hybrid III FE model simulations and comparison with experiments. The most recent National Highway Traffic Safety Administration (NHTSA) Biofidelity Ranking System (BRS) method was
Pradhan, VikramRamachandra, RakshitStammen, JasonKracht, CoreyMoorhouse, KevinBolte, John H.Kang, Yun-Seok
While weaponizing automated vehicles (AVs) seems unlikely, cybersecurity breaches may disrupt automated driving systems’ navigation, operation, and safety—especially with the proliferation of vehicle-to-everything (V2X) technologies. The design, maintenance, and management of digital infrastructure, including cloud computing, V2X, and communications, can make the difference in whether AVs can operate and gain consumer and regulator confidence more broadly. Effective cybersecurity standards, physical and digital security practices, and well-thought-out design can provide a layered approach to avoiding and mitigating cyber breaches for advanced driver assistance systems and AVs alike. Addressing cybersecurity may be key to unlocking benefits in safety, reduced emissions, operations, and navigation that rely on external communication with the vehicle. Automated Vehicles and Infrastructure Enablers: Cybersecurity focuses on considerations regarding cybersecurity and AVs from the
Coyner, KelleyBittner, Jason
Some challenges, such as reworking airbags to meet all seating scenarios, will be solved by the OEM as the final system integrator. Rearward-facing front seats have generally been limited to concept cars that explore a far-away world in which SAE Level 5 autonomous driving has been perfected. Magna has rewritten that playbook, winning a contract with a Chinese OEM for a reconfigurable seating system that includes fully rotating front seats on long rails, creating an unusually flexible cabin. Currently configured for vehicles with two rows of seating, the system features power-swivel seats along rails or tracks nearly two meters (6.6 ft) long. The front passenger and driver seats can rotate 270 degrees.
Clonts, Chris
In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm, which is further enhanced through the integration of wheel encoder and altitude data. Based on the generated map a LiDAR localization algorithm is used to determine the pose of the vehicle. The FAST-LIO based localization algorithm is enhanced by integrating relative LiDAR Odometry estimates and by using a simple yet effective delay compensation method to
Kramer, MarkusBeierlein, Georg
On-road vehicles equipped with driving automation features are entering the mainstream public space. This category of vehicles is now extending to include those where a human might not be needed for operation on board. Several pilot programs are underway, and the first permits for commercial usage of vehicles without an onboard operator are being issued. However, questions like “How safe is safe enough?” and “What to do if the system fails?” persist. This is where remote operation comes in, which is an additional layer to the automated driving system where a human assists the so-called “driverless” vehicle in certain situations. Such remote-operation solutions introduce additional challenges and potential risks as the entire chain of “automated vehicle, communication network, and human operator” now needs to work together safely, effectively, and practically. And as much as there are technical questions regarding network latency, bandwidth, cybersecurity, etc., aspects like human
Beiker, SvenMuelaner, Jody E.Razdan, Rahul
Artificial intelligence (AI)-based solutions are slowly making their way into mobile devices and other parts of our lives on a daily basis. By integrating AI into vehicles, many manufacturers are looking forward to developing autonomous cars. However, as of today, no existing autonomous vehicles (AVs) that are consumer ready have reached SAE Level 5 automation. To develop a consumer-ready AV, numerous problems need to be addressed. In this chapter we present a few of these unaddressed issues related to human-machine interaction design. They include interface implementation, speech interaction, emotion regulation, emotion detection, and driver trust. For each of these aspects, we present the subject in detail—including the area’s current state of research and development, its current challenges, and proposed solutions worth exploring.
Fang, ChenRazdan, rahulBeiker, SvenTaleb-Bendiab, Amine
The impending deployment of automated vehicles (AVs) represents a major shift in the traditional approach to ground transportation; its effects will inevitably be felt by parties directly involved with vehicle manufacturing and use (e.g., automotive original equipment manufacturers (OEMs), public transportation systems, heavy goods transportation providers) and those that play roles in the mobility ecosystem (e.g., aftermarket and maintenance industries, infrastructure and planning organizations, automotive insurance providers, marketers, telecommunication companies). The focus of this chapter is to address a topic overlooked by many who choose to view automated driving systems and AVs from a “10,000-foot perspective:” the topic of how AVs will communicate with other road users such as conventional (human-driven) vehicles, bicyclists, and pedestrians while in operation. This unsettled issue requires assessing the spectrum of existing modes of communication—both implicit and explicit
Beiker, SvenRazdan, RahulFavaro, FrancescaTaiber, JoachimSell, Raivo
On-road vehicles equipped with driving automation features are entering the mainstream public space. This category of vehicles is now extending to include those where a human might not be needed for operation on board. Several pilot programs are underway, and the first permits for commercial usage of vehicles without an onboard operator are being issued. However, questions like “How safe is safe enough?” and “What to do if the system fails?” persist. This is where remote operation comes in, which is an additional layer to the automated driving system where a human assists the so-called “driverless” vehicle in certain situations. Such remote-operation solutions introduce additional challenges and potential risks as the entire chain of “automated vehicle, communication network, and human operator” now needs to work together safely, effectively, and practically. And as much as there are technical questions regarding network latency, bandwidth, cybersecurity, etc., aspects like human
Beiker, SvenBock, ThomasTaiber, Joachim
A new revolution has taken place in the automobile industry in recent years, intelligent and connected vehicle (ICV) [1] has achieved a higher market share in recent years and relevant technologies have been quickly developed and widely accepted, so the auto industry needs to make regulations for automated driving system (ADS) on ICVs, mainly to assure the safety of ICV. To meet the requirements above, the definition of operational design domain (ODD) [2, 3] was put forward by the Society of Automotive Engineers (SAE) and International Organization for Standardization (ISO) a few years ago. ODD defines necessary external environment conditions for the ADS to operate, but the internal status of the vehicle is also a key part of judging whether ADS can operate safely. Based on that, we propose a novel definition named operational design condition (ODC), which can supersede ODD to play a bigger role in improving regulations and standards, and promoting vehicle safety and technological
Sun, HangWu, JiajieZhang, MiaoZhang, Hang
Effectively determining automated driving system (ADS)-equipped vehicle (AV) safety without relying on testing an infeasibly large number of driving scenarios is a challenge with wide recognition in industry and academia. The following paper builds on previous work by the Institute of Automated Mobility (IAM) and Science Foundation Arizona (SFAz), and proposes a test selection and scoring methodology (TSSM) as part of a safety case-based framework being developed by the SFAz to ensure the safety of AVs while addressing the scenario testing challenge. The TSSM is an AV verification and validation (V&V) process that relies, in part, on iterative, partially random generation of AV driving scenarios. These scenarios are generated using an operational design domain (ODD) and behavioral competency portfolio, which expresses the vehicle ODD and behavioral competencies in terms of quantifiable amounts or intensities of discrete components. Once generated, these scenarios are subjected to
O'Malley, GavinWishart, JeffreyZhao, JunfengRusso, Brendan
Most of the Automated Driving Systems (ADS) technology development is targeting urban areas; there is still much to learn about how ADS will impact rural transportation. The DriveOhio team deployed level-3 ADS-equipped prototype vehicles in rural Ohio with the goal of discovering technical challenges for ADS deployment in such environments. However, before the deployment on public roads, it was essential to test the ADS-equipped vehicle for their safety limitations. At Transportation Research Center Inc. (TRC Inc.) proving grounds, we tested one such prototype system on a closed test track with soft targets and robotic platforms as surrogates for other road users. This paper presents an approach to safely conduct testing for ADS prototype and assess its readiness for public road deployment. The main goal of this testing was to identify a safe Operational Design Domain (ODD) of this system by gaining better understanding of the limitations of the system. The prototype system uses Apollo
Rampilla, LokamanyaFreistuhler, CodyKaranjkar, SayaliSeitz, TimothyTulpule, Punit
With the development of vehicles equipped with automated driving systems, the need for systematic evaluation of AV performance has grown increasingly imperative. According to ISO 34502, one of the safety test objectives is to learn the minimum performance levels required for diverse scenarios. To address this need, this paper combines two essential methodologies - scenario-based testing procedures and scoring systems - to systematically evaluate the behavioral competence of AVs. In this study, we conduct comprehensive testing across diverse scenarios within a simulator environment following Mcity AV Driver Licensing Test procedure. These scenarios span several common real-world driving situations, including BV Cut-in, BV Lane Departure into VUT Path from Opposite Direction, BV Left Turn Across VUT Path, and BV Right Turn into VUT Path scenarios. Furthermore, the test cases are divided into different risk levels, allowing the AV to be tested in a variety of risk-level situations, with a
Wang, TinghanRahimi, ShujauddinSwaminathan, SunderZaidi, MohsinWishart, JeffreyLiu, Henry
Ensuring the safety of vulnerable road users (VRUs) such as pedestrians, users of micro-mobility vehicles, and cyclists is imperative for the commercialization of automated vehicles (AVs) in urban traffic scenarios. City traffic intersections are of particular concern due to the precarious situations VRUs often encounter when navigating these locations, primarily because of the unpredictable nature of urban traffic. Earlier work from the Institute of Automated Vehicles (IAM) has developed and evaluated Driving Assessment (DA) metrics for analyzing car following scenarios. In this work, we extend those evaluations to an urban traffic intersection testbed located in downtown Tempe, Arizona. A multimodal infrastructure sensor setup, comprising a high-density, 128-channel LiDAR and a 720p RGB camera, was employed to collect data during the dusk period, with the objective of capturing data during the transition from daylight to night. In this study, we present and empirically assess the
Rath, Prabin KumarHarrison, BlakeLu, DuoYang, YezhouWishart, JeffreyYu, Hongbin
Automated driving systems (ADS) are designed toward safely navigating the roadway environment, which also includes consideration of potential conflict with other road users. Of particular concern is understanding the cumulative risk associated with vulnerable road users (VRUs) conflicts and collisions. VRUs represent a population of road users that have limited protection compared to vehicle occupants. These severity distributions are particularly useful in evaluating ADS real-world performance with respect to the existing fleet of vehicles. The objective of this study was to present event severity distributions associated with vehicle-cyclist collisions within an urban naturalistic driving environment by leveraging data from third-party vehicles instrumented with forward-facing cameras and a sensor suite (accelerometer sampling at 20 Hz and GPS [variable sampling frequency]). From over 66 million miles of driving, 30 collision events were identified. A global optimization routine was
Campolettano, Eamon T.Scanlon, John M.Kusano, Kristofer D.
Modern advances in the technical developments of Advanced Driver Assistance Systems (ADAS) have elevated autonomous vehicle (AV) operations to a new height. Vehicles equipped with sensor based ADAS have been positively contributing to safer roads. As the automotive industry strives for SAE Level 5 full driving autonomy, challenges inevitably arise to ensure ADAS performance and reliability in all driving scenarios, especially in adverse weather conditions, during which ADAS sensors such as optical cameras and LiDARs suffer performance degradation, leading to inaccuracy and inability to provide crucial environmental information for object detection. Currently, the difficulty to simulate realistic and dynamic adverse weather scenarios experienced by vehicles in a controlled environment becomes one of the challenges that hinders further ADAS development. While outdoor testing encounters unpredictable environmental variables, indoor testing methods, such as using spray nozzles in a wind
Li, LongPao, Wing YiHoworth, JoshuaAgelin-chaab, MartinRoy, LangisKomar, JohnKnutzen, JulianBaltazar, AlexMuenker, Klaus
This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing. These sub-models were converted into Python then uploaded into the
Zeits, RogerGuenther, DennisCanova, MarcelloHeydinger, GarySundararaman Venkateshwara, KannaSalaani, KamelElsasser, Devin
This study assessed a driver’s ability to safely manage Super Cruise lane changes, both driver commanded (Lane Change on Demand, LCoD) and system triggered Automatic Lane Changes (ALC). Data was gathered under naturalistic conditions on public roads in the Washington, D.C. area with 12 drivers each of whom were provided with a Super Cruise equipped study vehicle over a 10-day exposure period. Drivers were shown how to operate Super Cruise (e.g., system displays, how to activate and disengage, etc.) and provided opportunities to initiate and experience commanded lane changes (LCoD), including how to override the system. Overall, drivers experienced 698 attempted Super Cruise lane changes, 510 Automatic and 188 commanded LCoD lane changes with drivers experiencing an average of 43 Automatic lane changes and 16 LCoD lane changes. Analyses characterized driver interactions during LCoD and ALC maneuvers exploring the extent to which drivers actively monitor the process and remain engaged
Llaneras, RobertGlaser, DanielGlaser, Yi
This SAE Recommended Practice provides guidance for test facilities in identifying potential hazards, and safety risks, along with requirements and recommendations related specifically to testing of automated driving systems (ADS) and ADS-operated vehicles. Herein after, for the purposes of this document, utilization of the term “test facilities” implies those conducting testing of ADS or ADS-operated vehicles, unless otherwise noted. References made to safety within this recommended practice apply only to test method safety and driving safety on and during testing at an ADS test facility and do not apply to vehicle design or safety performance. Safety practices for on-road testing, operation, and related deployment are not covered within this document.
On-Road Automated Driving (ORAD) Committee
Curbs are as key to automated driving system (ADS) navigation, operation, and safety as they are for human driven vehicles. The design, maintenance, and management of curbs and adjacent infrastructure can make the difference in whether ADS vehicles can pick up and deliver passengers and goods safely, efficiently, and effectively. Curbs may also be key to integrating ADS services with other forms of active and human-driven transportation. Benefits from accessibility, reduced emissions, and strong supply chains require that ADS vehicles be able to dock curbside in a manner that does not disrupt traffic or impede safe movement of people walking, biking, or using a mobility device. Automated Vehicles and Infrastructure Enablers: Curbs and Curbside Management addresses considerations regarding the curb with respect to pick up and drops for passengers and freight, as well as managing and designing both sides of the curb with respect to automated vehicles and other types of shared mobility
Coyner, KelleyBittner, Jason
Automated driving system is a multi-source sensor data fusion system. However different type sensor has different operating frequencies, different field of view, different detection capabilities and different sensor data transition delay. Aiming at these problems, this paper introduces the processing mechanism of out of sequence measurement data into the multi-target detection and tracking system based on millimeter wave radar and camera. After the comparison of ablation experiments, the longitudinal and lateral tracking performance of the fusion system is improved in different distance ranges.
Li, Fu-XiangZhu, Yuan
Radar is playing more and important role in multiple object detection and tracking system due to the fact that Radar can not only determine the velocity instantly but also it is less influenced by environment conditions. However, Radar faces the problem that it has many detection clutter,false alarms and detection results are easily affected by the reflected echoes of road boundary in traffic scenes. Besides this, With the increase of the number of targets and the number of effective echoes, the number of interconnection matrices increases exponentially in joint probability data association, which will seriously affect the real-time and accuracy of high-speed scene algorithms.in the tracking system. So, A method of using millimeter wave radar to detect and fit the boundary guardrail of high-speed road is proposed, and the fitting results are applied to the vehicle detection and tracking system to improve the tracking accuracy. Through the comparison and verification of ablation
Li, Fu-XiangZhu, Yuan
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.
Eley, T.C.King, John LeslieLyytinen, KalleNickerson, Jeffrey
Letter from the Special Issue Editor
Riehl, Jonathan
Recent rapid advancement in machine learning (ML) technologies have unlocked the potential for realizing advanced vehicle functions that were previously not feasible using traditional approaches to software development. One prominent example is the area of automated driving. However, there is much discussion regarding whether ML-based vehicle functions can be engineered to be acceptably safe, with concerns related to the inherent difficulty and ambiguity of the tasks to which the technology is applied. This leads to challenges in defining adequately safe responses for all possible situations and an acceptable level of residual risk, which is then compounded by the reliance on training data. The Path to Safe Machine Learning for Automotive Applications discusses the challenges involved in the application of ML to safety-critical vehicle functions and provides a set of recommendations within the context of current and upcoming safety standards. In summary, the potential of ML will only
Burton, Simon
In autonomous driving vehicles with an automation level greater than three, the autonomous system is responsible for safe driving, instead of the human driver. Hence, the driving safety of autonomous driving vehicles must be ensured before they are used on the road. Because it is not realistic to evaluate all test conditions in real traffic, computer simulation methods can be used. Since driving safety performance can be evaluated by simulating different driving scenarios and calculating the criticality metrics that represent dangerous collision risks, it is necessary to study and define the criticality metrics for the type of driving scenarios. This study focused on the risk of collisions in the confluence area because it was known that the accident rate in the confluence area is much higher than on the main roadway. There have been several experimental studies on safe driving behaviors in the confluence area; however, there has been little study logically exploring the merging
Imaseki, TakashiSugasawa, FukashiKawakami, ErikoMouri, Hiroshi
Connected autonomous vehicles that employ internet connectivity are technologically complex, which makes them vulnerable to cyberattacks. Many cybersecurity researchers, white hat hackers, and black hat hackers have discovered numerous exploitable vulnerabilities in connected vehicles. Several studies indicate consumers do not fully trust automated driving systems. This study expanded the technology acceptance model (TAM) to include cybersecurity and level of trust as determinants of technology acceptance. This study surveyed a diverse sample of 209 licensed US drivers over 18 years old. Results indicated that perceived ease of use positively influences perceived usefulness, perceived ease of usefulness negatively influences perceived cyber threats, and perceived cyber threats negatively influence the level of trust.
King, WarrenHalawi, Leila
Autonomous driving systems (ADS) have been widely tested in real-world environments with operators who must monitor and intervene due to remaining technical challenges. However, intervention methods that require operators to take over control of the vehicle involve many drawbacks related to human performance. ADS consist of recognition, decision, and control modules. The latter two phases are dependent on the recognition phase, which still struggles with tasks involving the prediction of human behavior, such as pedestrian risk prediction. As an alternative to full automation of the recognition task, cooperative recognition approaches utilize the human operator to assist the automated system in performing challenging recognition tasks, using a recognition assistance interface to realize human-machine cooperation. In this study, we propose a recognition assistance interface for cooperative recognition in order to achieve safer and more efficient driving through improved human-automation
Kuribayashi, AtsushiTakeuchi, EijiroCarballo, AlexanderIshiguro, YoshioTakeda, Kazuya
The latest developments in vehicle-to-infrastructure (V2I) and vehicle-to-anything (V2X) technologies enable all the entities in the transportation system to communicate and collaborate to optimize transportation safety, mobility, and equity at the system level. On the other hand, the community of researchers and developers is becoming aware of the critical role of roadway infrastructure in realizing automated driving. In particular, intelligent infrastructure systems, which leverage modern sensors, artificial intelligence, and communication capabilities, can provide critical information and control support to connected and/or automated vehicles to fulfill functions that are infeasible for automated vehicles alone due to technical or cost considerations. However, there is limited research on formulating and standardizing the intelligence levels of road infrastructure to facilitate the development, as the SAE automated driving levels have done for automated vehicles. This article
Ran, BinCheng, YangLi, ShenLi, HanchuParker, Steven
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, ScottNguyen, AnHeydinger, GaryAbbey, Howard
Although SAE level 5 autonomous vehicles are not yet commercially available, they will need to be the most intelligent, secure, and safe autonomous vehicles with the highest level of automation. The vehicle will be able to drive itself in all lighting and weather conditions, at all times of the day, on all types of roads and in any traffic scenario. The human intervention in level 5 vehicles will be limited to passenger voice commands, which means level 5 autonomous vehicles need to be safe and capable of recovering fail operational with no intervention from the driver to guarantee the maximum safety for the passengers. In this paper a LiDAR-based fail-safe emergency maneuver system is proposed to be implemented in the level 5 autonomous vehicle. This system is composed of an external redundant 3600 spinning LiDAR sensor and a redundant ECU that is running a single task to steer and fully stop the vehicle in emergency situations (e.g., vehicle crash, system failure, sensor failures
Alrousan, QusayAlzu'bi, HamzehTasky, TomVarasquim, Juliano
Simulation plays a central role in almost every aspect of automotive product development. And as this month's cover story explains, ‘sim’ is extending its reach in automated-driving R&D, bringing efficiency to human factors and critical but tedious component-verification work. Some argue that most AV development should - and thanks to contemporary sim technology, can - be conducted in the virtual world. It's hard for me to imagine getting to consumer-ready SAE Level 4 and 5 driving automation without eventual heavy reliance on simulation-based validation. That notion comes hard against what's played out with Tesla, however. The EV leader effectively has leveraged its customers' on-the-road experiences to incrementally “harden” its automated-driving software. It's not an entirely off-the-ranch idea; many AV developers have relied on some sort of crowdsourcing data acquisition to help their systems learn. The difference, however, is that Tesla consigned this role - and its genuine risks
Visnic, Bill
Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated. The method is evaluated
Weber, NicoThiem, ChristophKonigorski, Ulrich
Over the past decade there has been significant development in Automated Driving (AD) with continuous evolution towards higher levels of automation. Higher levels of autonomy increase the vehicle Dynamic Driving Task (DDT) responsibility under certain predefined Operational Design Domains (in SAE level 3, 4) to unlimited ODD (in SAE level 5). The AD system should not only be sophisticated enough to be operable at any given condition but also be reliable and safe. Hence, there is a need for Automated Vehicles (AV) to undergo extensive open road testing to traverse a wide variety of roadway features and challenging real-world scenarios. There is a serious need for accurate Ground Truth (GT) to locate the various roadway features which helps in evaluating the perception performance of the AV at any given condition. The results from open road testing provide a feedback loop to achieve a mature AD system. This paper presents an approach of using High Definition (HD) map data of various
Martens, JoshuaZhong, RenYedida, SiddharthaAlzu'bi, HamzehTasky, TomSirkar, Deshna
Artificial intelligence (AI)-based solutions are slowly making their way into our daily lives, integrating with our processes to enhance our lifestyles. This is major a technological component regarding the development of autonomous vehicles (AVs). However, as of today, no existing, consumer ready AV design has reached SAE Level 5 automation or fully integrates with the driver. Unsettled Issues in Vehicle Autonomy, AI and Human-Machine Interaction discusses vital issues related to AV interface design, diving into speech interaction, emotion detection and regulation, and driver trust. For each of these aspects, the report presents the current state of research and development, challenges, and solutions worth exploring. Click here to access the full SAE EDGETM Research Report portfolio.
Fang, Chen
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