Browse Topic: Highly automated vehicles

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Trajectory Tracking Control and State Estimation for Highly Automated Vehicles: A System Framework Implemented on U-Shift II2026-01-07797/1/2026
Trajectory tracking control and vehicle state estimation are core functionalities of highly automated vehicles and must operate reliably under strict real-time constraints as well as in the presence of model uncertainties and limited sensor availability. This paper presents an integrated, real-time capable framework for trajectory tracking control and vehicle state estimation, developed within the UShift II research project and implemented on the highly automated vehicle platform. The framework combines nonlinear model predictive control (NMPC) for trajectory tracking with an extended Kalman filter (EKF) for multi-sensor state estimation within a modular system architecture. The NMPC is based on a vehicle model designed for low-speed automated driving maneuvers and explicitly accounts for actuator constraints. Trajectories are tracked based on local planned reference trajectories while ensuring smooth and physically feasible control inputs for underlying control. The EKF fuses measurements from global navigation satellite system (GNSS), inertial sensors, and wheel-speed-based odometry, providing consistent estimates of the vehicle states under varying sensor availability. Particular emphasis is placed on robustness and computational efficiency in order to meet the real-time execution requirements on the target hardware. The complete framework is implemented on automotive-grade real-time hardware and validated on the U-Shift II vehicle platform. Experimental results demonstrate reliable localization performance, smooth and accurate trajectory tracking, and deterministic real-time execution, confirming the suitability of the proposed approach for practical low-speed automated driving applications.
Fuchs, Sören, Neubeck, Jens, Wagner, Andreas
A Self-Learning Framework for NVH CAE Analysis2026-01-07186/20/2026
The vibro-acoustic performance of a vehicle is a critical factor in customer perception of quality and comfort, yet optimizing for Noise, Vibration, and Harshness (NVH)—specifically road noise—presents a persistent challenge in the modern automotive development cycle. While advanced Finite Element Method (FEM) analysis is essential, the increasing complexity and volume of CAE simulation data often overwhelm manual interpretation, potentially leading to prolonged development times or compromises in final comfort quality. To address these challenges, this paper introduces the application of CDH/ACE (Autonomous Computational Experiments), a framework that integrates conventional CAE simulation workflows with advanced machine learning in an iterative, cyclic process. This creates an exceptionally user-friendly and self-correcting system that autonomously defines, performs, and learns from computational experiments. By leveraging machine learning algorithms to build robust predictive models from simulation data, the framework intelligently guides design exploration to achieve complex engineering objectives such as design of experiments, multi-objective optimization, and robustness analysis. We demonstrate this methodology through a comprehensive full-vehicle road noise optimization study, detailing the process of defining experiment parameters and configuring acoustic targets within the autonomous learning cycle. The results highlight the effectiveness of this highly automated and intuitive workflow, showing significant reductions in road noise and vehicle mass alongside a substantial decrease in manual engineering effort. Finally, the paper presents the tangible benefits of this approach, assessing current advantages and limitations while providing an outlook on the future application of autonomous, machine-learning-driven methodologies in accelerating modern vehicle development.
Visser, Rene
Integrating Human factors and Predictive Modeling for Enhanced Aviation Safety: A Data - Driven Approach to Risk Mitigation and System Design2026-26-07956/1/2026
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence modeling, model training, and interpretability analysis. The study demonstrates how this pipeline uncovers hidden relationships among operational errors, environmental influences, maintenance actions, design considerations, and crew behavior. The findings show that the integrated approach improves the accuracy and stability of risk prediction and highlights specific human factors patterns that consistently contribute to elevated risk levels. These insights support targeted mitigation strategies, inform design improvements, and help prioritize safety interventions. The work concludes that a combined human factors and predictive modeling framework enhances the ability of organizations to identify vulnerabilities earlier, allocate resources more effectively, and strengthen system resilience. This approach is adaptable to diverse aviation contexts and offers a practical path for transforming human factors data into actionable safety intelligence.
Valiyaparambil, Praveen
Effects of Child Anthropometry, Seatback Angle, Seat Rotation Angle, Seatbelt Force Limiting, and Collision Type on Injury Risk of Child Occupants in Highly Automated Vehicles2026-01-05704/7/2026
This study aimed to evaluate the influence of child anthropometry, seating postures (recline and rotation), seatbelt force limiting, and frontal collision scenarios on the kinematic response and injury risk in highly automated vehicles. The TUST IBMs 6YO-O model was conducted the frontal collisions in sled tests. This simulation matrix includes five percentiles six-year-old occupants (P3, P25, P50, P75, and P97), three seatback angles (20°, 30°, and 45°), four seat rotation angles (0°, 90°, 180°, and 270°), three seatbelt force limiting (2.6 kN, 3.6 kN, and 4.6 kN), and three frontal collision types. Injury risks were assessed including the child occupant's head, neck, chest/abdomen, and lumbar region in each simulation (n=540). The results indicate that the child anthropometry, the seatback angle, and the seat rotation angle have a significant influence on the motion responses. Statistically significant differences between all the groups within each independent variable category were observed based on the analysis of variance. As the child dimension increases, the risk of head injury decreases showing by HIC15, while the risk of neck and lumbar injuries increases. As the seatback angle increases, biomechanical parameters of the head show an increasing trend. The risk of upper neck injury decreases, while the risk of lumbar injury decreases and then increases. As the seat rotation angle increases, the risks of head, neck, and chest injuries initially rise and subsequently decrease, while the risk of lumbar injury demonstrates a downward trend. Seatbelt force limiting exhibited a positive correlation with head, neck, and lumbar injury risks. Consequently, small percentile child experiences higher head loads in smart cockpits, with seatback angle and seat rotation angle being key factors contributing to child injuries. These findings highlight the critical need to address the vulnerability of smaller children in smart cockpits by adapting integrated active and passive safety systems to mitigate their injury risk.
Wang, Yanxin, Zhao, Hongqian, Li, Haiyan, He, Lijuan, Cui, Shihai, Lv, Wenle
Sensitivity Analysis toward Differential Braking as Backup Steering Functionality2026-01-50132/20/2026
This paper presents a novel sensitivity analysis framework for differential braking as a backup steering solution in fail-operational Steer-by-Wire systems. The fault-tolerant design approach of Steer-by-Wire and steering systems for highly automated driving relies on the availability of road wheel actuators (RWA). Redundancies are therefore commonly used to ensure fail-operationality. Since its widespread implementation in production vehicles through electronic stability control, the use of differential braking as a cost-effective measure is desirable to increase functional diversity. However, feasible lateral accelerations through this backup solution are limited compared to conventional steering systems and lie close to ordinary driving scenarios. To address this limitation, this work investigates the influence of chassis parameters on differential braking performance. After defining characteristic values and a simulation test plan, a preliminary analysis using a linear single-track model incorporating differential braking is conducted. The most influential vehicle parameters are identified, including the newly derived effect of rear axle cornering stiffness—an aspect not previously quantified in this context. A second, more detailed sensitivity analysis is performed using a characteristic-based dual-track model equipped with representative brake, steering, and chassis subsystems. Parameter variation ranges are derived from distributed data-based sources. The resulting total effects reveal new insights into the sensitivity of chassis design parameters, including scrub radius, steering friction, rear axle cornering stiffness, and coupling effects of suspension and kinematics. The findings provide valuable guidance for future chassis design in fail-operational steering systems and highlight the importance of underexplored parameters in achieving reliable lateral dynamics through differential braking.
Salzwedel, Leon, Iatropoulos, Jannes, Heise, Cedric, Frohn, Christian, Henze, Roman
CARLA Hardware-in-the-Loop Simulation Environment for Autonomous Driving12-09-02-001511/20/2025
This article suggests a validation methodology for autonomous driving. The goal is to validate front camera sensors in advanced driver-assist systems (ADAS) based on virtually generated scenarios. The outcome is the CARLA-based hardware-in-the-loop (HIL) simulation environment (CHASE). It allows the rapid prototyping and validation of the ADAS software. We tested this general approach on a specific experimental application/setup for a vehicle front camera sensor. The setup results were then proven to be comparable to real-world sensor performance. The CARLA simulation environment was used in tandem with a vehicle CAN bus interface. This introduced a significantly improved realism to user-defined test scenarios and their results. The approach benefits from almost unlimited variability of traffic scenarios and the cost-efficient generation of massive testing data.
Cardozo, Shawn Moses, Hlavác, Václav
Hybrid Safety Framework for Autonomous Off-Highway Vehicles2025-28-032511/6/2025
The evolution of Autonomous off-highway vehicles (OHVs) has transformed mining, construction, and agriculture industries by significantly improving efficiency and safety. These vehicles operate in high dust, uneven terrain, and potential communication failures, where safety is challenged. To guarantee vehicle safety in such situations, a robust architecture that combines AI-driven perception, fail-safe mechanisms, and conformance to many ISO standards is required. In unstructured environments, AI-driven perception, decision-making, and fail-safe mechanisms are not fully addressed by traditional safety standards like ISO26262 (road vehicles), ISO19014 (earth-moving machinery and it is replacing withdrawn ISO 15998), ISO12100 (Safety of machinery) and ISO25119 (agriculture), ISO 18497 (safety of highly automated agricultural machinery), and ISO/CD 24882 (cybersecurity for machinery).These standards mainly concentrate on the reliability of mechanical and electric/electronic systems. Additionally, emerging standards such as ISO21448 (SOTIF) used to detect and mitigate unsafe AI outputs, and ISO8800(AI safety) which focus on safety assurance for AI-based systems, offer valuable insights but requires additional adaptation. AI-driven systems are vulnerable to cyber threats that can endanger the vehicle safety. Integration of ISO21434 (cybersecurity for vehicles) and EU Cyber Resilience Act (CRA) has been added as a key regulatory framework with safety standards is highly needed to address safety failures induced by cyber threats. This paper introduces a hybrid safety framework that integrates ISO safety standards ISO26262, ISO19014, ISO12100, ISO25119, ISO21448, ISO21434, ISO 18497, ISO/CD 24882, EU Cyber Resilience Act and ISO8800 with AI innovations enhance the safety and reliability of autonomous OHVs. The proposed framework makes use of sensor fusion, explainable AI for transparent decision-making, especially in safety-critical scenarios, and a real-time fail-safe mechanism to manage critical failure scenarios such as power failures, communication loss, and sensor degradation by switching to a safe state. To confirm the effectiveness of this hybrid approach, digital twin simulation software along with additional technologies in OHV applications are used. The results demonstrate significant improvements in more accurate fault detection, efficient responses, and overall system resilience, highlighting the benefits of merging AI safety techniques with established ISO standards.
Muthusamy, Sugantha
Conception and Experience of Non-Driving Related Activities in Semi-Autonomous Vehicles2025-01-02607/2/2025
While semi-autonomous driving (SAE level 3 & 4) is already partially a reality, the driver still needs to take over driving upon notice. Hence, the cockpit cannot be designed freely to accommodate spaces for non-driving related activities. In the following use case, a mobile workplace is created by integrating a translucent acrylic glass pane into the cockpit and introducing joystick steering of the car. By using the technology Virtual Desktop 1, which is a software layer, any desktop application can be represented freely transformable on arbitrary physical and virtual surfaces. Thus, a complete Windows environment can be distributed across all curved and flat surfaces of an interior. The concept is further enhanced by a voice-driven generative AI which helps to summarize documents. A physical and a virtual demonstrator are created to experience and assess the mobile workspace, the well-being of the driver, external influences, and psychological aspects. The physical demonstrator is a 1:1 partial interior mockup with projection-based interactive surfaces. The virtual demonstrator represents the same interior model using the simulation technology TRONIS® and is perceptible through virtual reality (Apple Vision Pro). The demonstrators enable a user-centered design process and facilitate the creation of innovative designs that can be experienced realistically. The technological concepts can also be adapted for other non-driving related activities such as relaxation and entertainment, allowing for the application of many use cases and a broad variety of potential users.
Beutenmüller, Frank, Reining, Nine, Rosenstiel, Reto, Schmidt, Maximilian, Layer, Selina, Bues, Matthias, Mendonca, Daisy
Flight Test Evaluation of Autonomous Descending-Decelerating Precision Point-in-Space Approach to the GroundF-0081-2025-01235/20/2025
New forms of highly automated Advanced Air Mobility (AAM) aircraft, such as electric vertical take-off and landing (eVTOL) vehicles, could transform transportation, cargo delivery, and a variety of public services. The National Aeronautics and Space Administration (NASA) conducted a series of flight demonstrations in collaboration with the Defense Advanced Research Projects Agency (DARPA) and Sikorsky Aircraft (a Lockheed Martin company) to progressively evaluate autonomous technologies. The autoland flight test research is a first in series for investigating the world’s first procedural descending-decelerating automated landing with vertical guidance Instrument Flight Procedures (IFP). The Sikorsky Optionally Piloted Vehicle (OPV) experimental UH-60 Black Hawk was used to evaluate a flight path’s four-dimensional trajectory (4DT) management into primitive commands and then follow those commands to a Point-in-Space (PinS) landing to the ground. All flight procedures were manually flown to the ground at 12 degrees with a 20-knot tail wind to ensure flight safety before automation was engaged. New and novel high precision approach procedures could pave the way for all future VTOL operations.
Zahn, David, Patterson, Gayle, Williams, Ethan, Eggum, Sarah, Fettrow, Tyler
Infrastructure Enablers for Automation: Vol. 3EPRCOMPV0420244/30/2025
Coyner, Kelley, Bittner, Jason
Integrated Multimodal System for Real-Time Driver Fatigue Detection and Cognitive Load Assessment2025-01-80764/1/2025
As human drivers' roles diminish with higher levels of driving automation (SAE L2-L4), understanding driver engagement and fatigue is crucial for improving safety. We developed an integrated hardware and software system to analyze driver interaction with automated vehicles, with a particular focus on cognitive load and fatigue assessment. The system includes three submodules; namely the Driver Behavior Measurement (DBM), Vehicle Dynamics Measurement (VDM), and the Driver Physiological Measurement (DPM). The DBM module uses electro-optical (EO) and infrared (IR) camera to track a number of facial features such as eye aspect ratio (EAR), mouth aspect ratio (MAR), pupil circularity (PUC), and mouth to eye aspect ratio (MOE). Although determining these metrics from images of the driver’s face in conditions such as low light or with sunglasses is challenging, the paper showed that fusion of EO and IR image analysis produces robust performance. The VDM module utilizes an Inertial Measurement Unit (IMU) to provide vehicular motion data such as speed, acceleration, braking and yaw rate to aid detection of fatigue-related irregularities. A wearable heart rate monitor was used in the DPM module to track driver heart rate as an indicator of stress and fatigue. Data from these modules is fused and processed using a previously published CNN-LSTM model, achieving 90.1% accuracy in detecting fatigue in preliminary tests performed with one driver. The test results show that the system is robust, scalable, and suitable for large-scale studies on driver engagement with highly automated vehicles.
Jirjees, Abdullah, Rahman, Taufiq, Farhani, Ghazal, Singh, Daniel, Charlebois, Dominique
Data Driven Quantitative Evaluation of Safety Controllability for Automated Vehicle2025-01-82814/1/2025
With the development of automated vehicle (AV), it is essential to ensure their safety even in the presence of system faults or function inefficiency. Safety controllability refers to the ability to manage and control the vehicle, ensuring that it remains safe even in the presence of faults with unexpected conditions. This study proposed a data driven method to evaluate quantitatively safety controllability for AVs. Safety analysis is conducted to identify the potential hazard events. Taking system function and architecture into consideration, the failure modes of the vehicle hazards are identified with hazardous driving situation. Based on the identified failure modes, fault injection tests are conducted with critical scenarios. According to the vehicle dynamic performance, the improved analytic hierarchy process (AHP) can be explored to quantitatively evaluate the safety controllability based on fault injection test results. In particular, this study focuses on the case study to forecast the effect of faults on safety controllability with vehicle dynamic performance. The purposed method can quantitatively evaluate the controllability of the AVs with critical system failure in critical scenario, which can provide the guideline for autonomous driving system safety design.
Ye, Xiaoming, Yang, Yanding, Li, Lingyang, Zhang, Yaguo, Wang, Yongliang
Improving Safety of Autonomous Vehicles: A Verifiable Method for Graceful Degradation of Decision and Control Responsibilities12-08-02-00213/24/2025
Developing safe and reliable autonomous vehicles is crucial for addressing contemporary mobility challenges. While the goal of autonomous vehicle development is full autonomy, up to SAE Level 4 and beyond, human intervention remains necessary in critical or unfamiliar driving scenarios. This article introduces a method for gracefully degrading system functionality and seamlessly transferring decision-making and control between the autonomous system and a remote safety operator when needed. This transfer is enabled by an onboard dependability cage, which continuously monitors the vehicle’s performance during its operation. The cage communicates with a remote command control center, allowing for remote supervision and intervention by a safety driver. We assess this methodology in both lab and test field settings in a case study of last-mile parcel delivery logistics and discuss the insights and results obtained from these evaluations.
Aniculaesei, Adina, Aslam, Iqra, Zhang, Meng, Buragohain, Abhishek, Vorwald, Andreas, Rausch, Andreas
An Improved Vehicle Path-Tracking Model Based on Adaptive Nonlinear Model Predictive Control via Online Big Bang—Big Crunch Algorithm and Artificial Neural Network10-08-04-003210/25/2024
In this article, a novel tuning approach is proposed to obtain the best weights of the discrete-time adaptive nonlinear model predictive controller (AN-MPC) with consideration of improved path-following performance of a vehicle at different speeds in the NATO double lane change (DLC) maneuvers. The proposed approach combines artificial neural network (ANN) and Big Bang–Big Crunch (BB–BC) algorithm in two stages. Initially, ANN is used to tune all AN-MPC weights online. Vehicle speed, lateral position, and yaw angle outputs from many simulations, performed with different AN-MPC weights, are used to train the ANN structure. In addition, set-point signals are used as inputs to the ANN. Later, the BB–BC algorithm is implemented to enhance the path-tracking performance. ANN outputs are selected as the initial center of mass in the first iteration of the BB–BC algorithm. To prevent control signal fluctuations, control and prediction horizons are kept constant during the simulations. The results showed that all AN-MPC weights are successfully tuned online and updated during the maneuvers, and the path-following performance of the ego vehicle is improved at different NATO DLC speeds using the proposed ANN + BB–BC, compared to the method where ANN is used only.
Yangin, Volkan Bekir, Yalçın, Yaprak, Akalin, Ozgen
Systematic Selective Limits Application Using Decision-Making Engines to Enhance Safety in Highly Automated Vehicles12-08-01-00058/1/2024
The traditional approach to applying safety limits in electromechanical systems across various industries, including automated vehicles, robotics, and aerospace, involves hard-coding control and safety limits into production firmware, which remains fixed throughout the product life cycle. However, with the evolving needs of automated systems such as automated vehicles and robots, this approach falls short in addressing all use cases and scenarios to ensure safe operation. Particularly for data-driven machine learning applications that continuously evolve, there is a need for a more flexible and adaptable safety limits application strategy based on different operational design domains (ODDs) and scenarios. The ITSC conference paper [1] introduced the dynamic control limits application (DCLA) strategy, supporting the flexible application of diverse limits profiles based on dynamic scenario parameters across different layers of the Autonomy software stack. This article extends the DCLA strategy by outlining a methodology for safety limits application based on ODD elements, scenario identification, and classification using decision-making (DM) engines. It also utilizes a layered architecture and cloud infrastructure based on vehicle-to-infrastructure (V2I) technology to store scenarios and limits mapping as a ground truth or backup mechanism for the DM engine. Additionally, the article focuses on providing a subset of driving scenarios as case studies that correspond to a subset of the ODD elements, which forms the baseline to derive the safety limits and create four different application profiles or classes of limits. Finally, the real-world examples of “driving-in-rain” scenario variations have been considered to apply DM engines and classify them into the previously identified limits application profiles or classes. This example can be further compared with different DM engines as a future work potential that offers a scalable solution for automated vehicles and systems up to Level 5 Autonomy within the industry.
Garikapati, Divya, Liu, Yiting, Huo, Zhaoyuan
FMCW Lidar Simulation with Ray Tracing and Standardized Interfaces2024-01-29777/2/2024
In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment. For this purpose, an ASAM Open Simulation Interface (OSI) object list referencing virtual 3D objects provides the input for the ray tracer. The divergence of the continuous laser beam is approximated by super-sampling the beam with multiple rays, the calculation of the received power is supported by the future ASAM OpenMATERIAL standard. Subsequently, the output of the ray tracer serves as the input of the signal processing that adapts the so-called Fourier tracing from the field of radar sensor simulation. This approach uses the range and velocity information of the individual rays to estimate the frequency spectrum of the intermediate frequency signal. A subsequent peak detection algorithm determines the output of the model, which is provided in the form of OSI lidar detections. Verification scenarios are tested to check the plausibility of the output and the source code of the signal processing is made available as open source.
Hofrichter, Kristof, Linnhoff, Clemens, Elster, Lukas, Peters, Steven
Lighting and Visual Information for Vulnerable Road User (VRU) Safety: An Introductory ReviewVRUSC-002-20245/3/2024
Injuries and fatalities among pedestrians, cyclists, scooterists, highway road workers, and safety and emergency personnel—often referred to as vulnerable road users (VRUs)—continue to rise at alarming rates worldwide. Emphasizing the urgent need for enhanced safety measures, this study, commissioned by the Vulnerable Road User Safety Consortium™ (VRUSC) and conducted by the Light and Health Research Center at the Icahn School of Medicine at Mount Sinai, evaluates the potential effectiveness of lighting and visual information systems in improving VRU safety. The white paper presents a synthesis of published research on lighting and markings from the perspective of both human drivers and machine vision systems. It identifies potential preliminary guidelines for the intensity, color, temporal, and spatial characteristics of lighting and visual information that can help prevent crashes involving VRUs. The study also highlights knowledge gaps that require further research, including the development of standardized lighting specifications tailored to the unique needs of VRUs.
Vulnerable Road Users Safety Consortium
SAE TOMORROW TODAY: Former Patagonian Rancher Advancing Ag Tech134474/15/2024
As a former rancher in Patagonia, Alex Foessel understands the challenges facing the ag market and is working to advance safety and integrity in off-road autonomy. As Managing Partner of Balanced Engineering, Alex and his team support the production and commercialization of highly automated off-road equipment though a proven safety case framework and certification process -- getting great ideas to the ag market sooner. With experts in Iowa, Pennsylvania and Brazil, the company is maximizing productivity in agriculture through the integration of automation and AI. To learn more, we sat down with Alex to discuss the challenges facing the ag market, the importance of safety, and how off-road ag technology will evolve over the next decade.
Hineman, Marcie
Dummy Positioning at Reclined Seating Position before Impact Testing2024-01-24904/9/2024
Alongside advancements in automated vehicle technologies, occupants within vehicle compartments are enjoying increased freedom to relax and enjoy their journeys. For instance, reclined seating postures have become more prevalent and comfortable compared to upright seating when Highly Automated Vehicles (HAVs) are introduced. Unfortunately, most Anthropomorphic Testing Devices (ATD) do not support reclined postures. THOR-AV 50M is a specially designed dummy for reclined postures. As a crucial tool for developing safety restraint systems to protect reclined occupants, the first question is how to position it correctly on a reclined seat before impact testing. In this study, classical zero gravity seats were selected. H-point coordinators of selected seat at 25°, 40° and 60° seatback angle were measured and compared by using H-point machine (HPM) even though current HPM was not designed for reclined seat. THOR-AV 50M with loosened joints, served to simulate human relaxation fully when seating on a seat, to explore the most comfortable position, there were two ways of seat adjusting from 25° to 40° and 60°, H-point coordinators of THOR-AV 50M at 40° and 60° were measured, related H-point could be regarded as the most comfortable position. By comparing the measured H-point coordinators of THOR -AV 50M and HPM at 40° and 60°, authors found that deviations were within current regulatory requirement. This finding indicated that the target of H-point of THOR-AV 50M at reclined seating position could be pre-defined by HPM. Therefore, a procedure for positioning the dummy in a reclined seat was developed. This paper provides an explanation of the rationale behind the method by using both physical and virtual dummy finite element models. It serves as a valuable reference for potential updates to related standards and protocols to incorporate reclined seating positions in the future, makes up for blanks in automotive industry.
Liu, Chongqing, Wang, Zhenwen
Cybersecurity in the Context of Fail-Operational Systems2024-01-28084/9/2024
The development of highly automated driving functions (AD) recently rises the demand for so called Fail-Operational systems for native driving functions like steering and braking of vehicles. Fail-Operational systems shall guarantee the availability of driving functions even in presence of failures. This can also mean a degradation of system performance or limiting a system’s remaining operating period. In either case, the goal is independency from a human driver as a permanently situation-aware safety fallback solution to provide a certain level of autonomy. In parallel, the connectivity of modern vehicles is increasing rapidly and especially in vehicles with highly automated functions, there is a high demand for connected functions, Infotainment (web conference, Internet, Shopping) and Entertainment (Streaming, Gaming) to entertain the passengers, who should no longer occupied with driving tasks. But the connectivity is accompanied by potential cyber security risks, eventually compromising a vehicles safety. Therefore, mitigating such risks by appropriate security measures is mandatory. Unfortunately, the combination of functional safety and cyber security requirements aiming on the same target often contains a considerable potential for conflict, as they may be contradicting. Especially in Fail-Operational systems, where system availability is a major safety goal, matching of both fields is quite a challenge. This paper depicts contradictions, raises related open question, offers possible answers and tries to encourage an industry-wide discussion of the stakeholders in the related fields.
Schmidt, Karsten, Dannebaum, Udo, Schneider, Rolf, Ambekar, Abhijit
Methodology to Estimate Load Spectra of Autonomous and Highly Automated Vehicles2024-01-23264/9/2024
The knowledge of representative load collectives and duty cycles is crucial for designing and dimensioning vehicles and their components. For human driven vehicles, various methods are known for deriving these load spectra directly or indirectly from fleet measurement data of the customer vehicle operation. Due to the lack of market penetration of highly automated and autonomous vehicles, there is no sufficient fleet data available to utilize these methods. As a result of increased demand for ride comfort compared to human driven vehicles, autonomous vehicle operation promises reduced driving speeds as well as reduced lateral and longitudinal accelerations. This can consequently lead to decreasing operation loads, thus enabling potentially more light-weight, cost-effective, resource-saving and energy-efficient vehicle components. In order to unlock this potential of dedicatedly dimensioned components for autonomous vehicles, a methodology for quantifying the loads in customer operation is required. Therefore, this paper proposes a novel methodology to quantify operation loads of highly automated and autonomous vehicles based on statistical long-term simulation, in which route characteristics, surrounding traffic and vehicle control algorithms are taken into account. The statistical synthesis of driving routes as the basis for further long-term simulation is addressed in detail in this paper. Furthermore, the impact of different lateral and longitudinal control strategies on drivetrain loads of an autonomous vehicle is showcased as an early result of the proposed methodology. Future work required to complete the proposed methodology is addressed in the outlook of this paper. Additional utilization of the driving route synthesis for the validation of autonomous driving functions is pointed out.
Brandes, Gerrit, Rebesberger, Ron, Sander, Marcel, Erxleben, Lars, Henze, Roman, Küçükay, Ferit
FMCW Lidar Simulation with Ray Tracing and Standardized InterfacesSAE-PP-003874/5/2024
In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment. For this purpose, an ASAM Open Simulation Interface (OSI) object list referencing virtual 3D objects provides the input for the ray tracer. The divergence of the continuous laser beam is approximated by super-sampling the beam with multiple rays, the calculation of the received power is supported by the future ASAM OpenMATERIAL standard. Subsequently, the output of the ray tracer serves as the input of the signal processing that adapts the so-called Fourier tracing from the field of radar sensor simulation. This approach uses the range and velocity information of the individual rays to estimate the frequency spectrum of the intermediate frequency signal. A subsequent peak detection algorithm determines the output of the model, which is provided in the form of OSI lidar detections. Verification scenarios are tested to check the plausibility of the output and the source code of the signal processing is made available as open source.
Hofrichter, Kristof, Linnhoff, Clemens, Elster, Lukas, Peters, Steven
AVSC Best Practice for First Responder Interactions with Fleet-Managed Automated Driving System-Dedicated Vehicles (ADS-DVs)AVSC-I-01-20244/4/2024
This AVSC best practice was first published in 2020 and has been revised to cast expanded role definitions, rearrangement of use cases in Section 4 based on severity of risk, re-creation of Table 4 in 5.4 as a checklist and moved to Appendix B, as well as clarification of sections, examples, and terms throughout the document. This document outlines interactions between first responders and ADS-DVs (SAE level 4 and level 5). It builds on earlier work done by the Crash Avoidance Metrics Partners (CAMP), detailing three types of interactions first responders may encounter: direct, indirect, and informational. In addition, a standardized framework with recommendations for an interaction plan is laid out for ADS developers, manufacturers, and fleet operators which may assist first responders in both emergency and non-emergency situations involving ADS-DVs.
Automated Vehicle Safety Consortium
Simulation-Based Application of Safety of The Intended Functionality to Mitigate Foreseeable Misuse in Automated Driving SystemsSAE-PP-0037111/28/2023
The development of Automated Driving Systems (ADS) has the potential to revolutionize the transportation industry, but it also presents significant safety challenges. One of the key challenges is ensuring that the ADS is safe in the event of Foreseeable Misuse (FM) by the human driver. To address this challenge, a case study on simulation-based testing to mitigate FM by the driver using the driving simulator is presented. FM by the human driver refers to potential driving scenarios where the driver misinterprets the intended functionality of ADS, leading to hazardous behavior. Safety of the Intended Functionality (SOTIF) focuses on ensuring the absence of unreasonable risk resulting from hazardous behaviors related to functional insufficiencies caused by FM and performance limitations of sensors and machine learning-based algorithms for ADS. The simulation-based application of SOTIF to mitigate FM in ADS entails determining potential misuse scenarios, conducting simulation-based testing, and evaluating the effectiveness of measures dedicated to preventing or mitigating FM. The major contribution includes defining (i) test requirements for performing simulation-based testing of a potential misuse scenario, (ii) evaluation criteria in accordance with SOTIF requirements for implementing measures dedicated to preventing or mitigating FM, and (iii) approach to evaluate the effectiveness of the measures dedicated to preventing or mitigating FM. In conclusion, an exemplary case study incorporating driver-vehicle interface and driver interactions with ADS forming the basis for understanding the factors and causes contributing to FM is investigated. Furthermore, the test procedure for evaluating the effectiveness of the measures dedicated to preventing or mitigating FM by the driver is developed in this work.
patel, milin, Jung, Rolf
Comparison of the Knee–Thigh–Hip Response in Small Female ATDs with Female PMHS09-11-02-00139/20/2023
Bilateral knee impacts were conducted on Hybrid III and THOR 5th percentile female anthropomorphic test devices (ATDs), and the results were compared to previously reported female PMHS data. Each ATD was impacted at velocities of 2.5, 3.5, and 4.9 m/s. Knee–thigh–hip (KTH) loading data, obtained either via direct measurement or through exercising a one-dimensional lumped parameter model (LPM), was analyzed for differences in loading characteristics including the maximum force, time to maximum force, loading rate, and loading duration. In general, the Hybrid III had the highest loading rate and maximum force, and the lowest loading duration and time to peak force for each point along KTH. Conversely, the PMHS generally had the lowest loading rate and maximum force, and the highest loading duration and time to peak force for each point along KTH. The force transfer from the knee to the femur was 79.2 ± 0.3% for the Hybrid III 5th female, 82.7 ± 0.4% for the THOR-05F, and 70.6 ± 1.7% for the PMHS. The force transfer from the knee to the hip was 60.6 ± 0.5% for the Hybrid III 5th female, 41.4 ± 0.4% for the THOR-05F, and 57.0 ± 3.0% for the PMHS. While the Hybrid III aligned more with the PMHS force transfer ratios, the loading characteristics of the THOR-05F were more similar to the PMHS.
Carpenter, Randolff L., Berthelson, Parker R., Donlon, John-Paul, Forman, Jason L.
Letter from the Special Issue Editors10-07-03-00169/15/2023
Letter from the Special Issue Editors
Ivanov, Valentin, Savitski, Dzmitry
Simulation-Based Testing and Performance Evaluation of Vehicle Safety Functions12-07-01-00069/7/2023
The progressive development toward highly automated driving poses major challenges for the release and validation process in the automotive industry, because the immense number of test kilometers that have to be covered with the vehicle cannot be tackled to any extent with established test methods, which are highly focused on the real vehicle. For this reason, new methodologies are required. Simulation-based testing and, in particular, virtual driving tests will play an important role in this context. A basic prerequisite for achieving a significant reduction in the test effort with the real vehicle through these simulations are realistic test scenarios. For this reason, this article presents a novel approach for generating relevant traffic situations based on a traffic flow simulation in SUMO and a vehicle dynamics simulation in CarMaker. The procedure is shown schematically for an emergency braking function. A driving function under test faces the major challenges when the other road users commit driving errors. Therefore, the driving behavior models in this traffic flow simulation are modified in such a way that critical scenarios can arise because of these driving errors. In order to be able to make a statement about the correct behavior of the driving function under test in these traffic situations, objective criteria are necessary to evaluate the triggering behavior and the handling of the traffic situations. Based on the performance evaluation of the driving function under test, characteristic test scenarios are then identified that evenly cover the test space. The comparison of the deviations in covering this test space with full and the reduced dataset is small except in areas where there are no scenarios in both datasets. Finally, these selected scenarios are used to perform an application of the driving function under test. The procedure is exemplified for the triggering time and the maximum deceleration of an emergency braking function. When comparing the distributions, it is shown that the performance in both datasets improves in the same way when parameters are optimized. For example, the mean performance of the driving function increases by more than 0.3 in each case when optimizing the triggering time. Thus, it is no longer necessary to use all scenarios for parameterization in virtual driving tests.
Riegl, Peter, Gaull, Andreas, Beitelschmidt, Michael
A Review of Intelligence-Based Vehicles Path Planning02-16-04-00227/28/2023
Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm. Then, the path planning method based on deep RL is introduced based on the shortcomings of RL in intelligence-based vehicle path planning. Finally, we discuss the trend of path planning for vehicles.
Hao, Bing, Zhao, JianShuo, Wang, Qi
Design of a Robust Optimal Multivariable Control for a Steer-by-Wire System2023-01-12186/26/2023
On the way to highly automated and autonomous driving, a robustly designed steering system is a key component. Therefore, this article presents a new control approach for modern steer-by-wire systems. The approach consists of a multivariable control for the driver´s steering torque and the rack position simultaneously, using the requested torques of the downstream and upstream motor as control variables. The plant model used in this approach is a detailed model of a steer-by-wire system with nine degrees of freedom. For the control design, an optimal reduced model is derived. The reduced plant model is linearized, and it is augmented by linear models for the reference and disturbance environment of the steer-by-wire system and by a linearized model for the feeling generator that computes the requested steering torque. For this augmented model, a multivariable linear optimal static state space controller is designed. Hence, the whole environment of the real steering system is considered in the control design. Due to the multivariable approach and the augmented model that contains all subsystems and dominant characteristics of the real system, the resulting control system shows excellent robustness characteristics. Therefore, the presented control fulfills all the requirements of a modern steering system regarding robustness and can be adapted to different driving situations.
Irmer, Marcus, Thomas PhD, Karin, Ruschitzka PhD, Margot, Henrichfreise PhD, Hermann
Automated Vehicles and Infrastructure Enablers: ConnectivityEPR20230136/13/2023
Do connected vehicle (CV) technologies encourage or dampen progress toward widespread deployment of automated vehicles? Would digital infrastructure components be a better investment for safety, mobility, and the environment? Can CVs, coupled with smart infrastructure, provide an effective pathway to further automation? Highly automated vehicles are being developed (albeit slower than predicted) alongside varied, disruptive connected vehicle technology. Automated Vehicles and Infrastructure Enablers: Connectivity looks at the status of CV technology, examines the concerns of automated driving system (ADS) developers and infrastructure owners and operators (IOOs) in relying on connected infrastructure, and assesses lessons learned from the growth of CV applications and improved vehicle-based technology. IOOs and ADS developers agree that cost, communications, interoperability, cybersecurity, operation, maintenance, and other issues undercut efforts to deploy a comprehensive connected infrastructure. Click here to access The Mobility Frontier: Accelerating Infrastructure Readiness for Autonomy Click here to access the full SAE EDGETM Research Report portfolio.
Coyner, Kelley, Bittner, Jason
Classification of Roadway Infrastructure and Collaborative Automated Driving System12-06-04-00265/9/2023
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 proposes a five-level intelligence definition for intelligent roadway infrastructure, namely, connected and automated highway (CAH). The CAH is a subsystem of the more extensive collaborative automated driving system (CADS), along with the connected automated vehicle (CAV) subsystem. Leveraging the intelligence definition of CAH, the intelligence definition for the CADS is also defined. Examples of how the CAH at different levels operates with the CAV in the CADS are also introduced to demonstrate the dynamic allocation of various automated driving tasks between different entities in the CADS.
Ran, Bin, Cheng, Yang, Li, Shen, Li, Hanchu, Parker, Steven
AVSC Information Report for Change Risk ManagementAVSC000102023044/12/2023
AVSC Information Report for Change Risk Management AVSC00010202304 provides a process for change risk management for fleet-operated ADS-DVs using level 4 or 5 automation. The document addresses risks resulting from planned and unplanned changes in an ADS-DV design and/or operation. This information report is based on the concept of risk-informed decision-making. Making risk management decisions such as safety and change management, safety analysis, and safety assurance are especially applicable when moving from concept to production intent for the ADS-DV. Change Risk Management (CRM) does not replace best practices or other methods for managing safety anomalies or change management processes. It may instead be viewed as an additional resource that elaborates on how safety anomaly management and change management can be performed.
Automated Vehicle Safety Consortium
Estimation of the Tire-Road Interaction Forces by using Pacejka’s Formulas with Combined Slips and Camber Angles2023-01-06464/11/2023
The growing market demand for highly automated and autonomous vehicles and the need to equip vehicles with ever higher standards of comfort, safety and performance requires knowledge of physical quantities that are often difficult or expensive to measure directly. The absence of direct sensors, the difficulty of implementation, and their cost have led researchers to identify alternative solutions that allow estimating the physical quantity of interest by aggregating other available information. The interaction forces between tire and road are among the most significant. Given that the dynamics of a vehicle are strongly linked to the forces exchanged between the tire and the road, their knowledge is fundamental in the development of control systems aimed at improving performance in terms of handling, road holding or comfort. This paper presents a new technique for the estimation of tire-road interaction forces based on the integration of models and measures. A Central Difference Kalman filter was applied to a Double Track Model. The non-linear Kalman filter allowed us to handle the non-linearity of the system. The tire-road interaction was modelled through Pacejka's magic formulas that into account the combined longitudinal and lateral slips and the camber angle. This version made it possible to carry out complex and realistic manoeuvres. The realized estimator also considers the influence of lateral and longitudinal load transfers and aerodynamic forces in the three spatial directions. The Camber angle used in this observer was estimated through neural networks. The measures used are longitudinal velocity, yaw rate, longitudinal slip and wheel steering angles.
Marotta, Raffaele, Ivanov, Valentin, Strano, Salvatore, Terzo, Mario, Tordela, Ciro
A Method for Building Vehicle Trajectory Data Sets Based on Drone Videos2023-01-07144/11/2023
The research and development of data-driven highly automated driving system components such as trajectory prediction, motion planning, driving test scenario generation, and safety validation all require large amounts of naturalistic vehicle trajectory data. Therefore, a variety of data collection methods have emerged to meet the growing demand. Among these, camera-equipped drones are gaining more and more attention because of their obvious advantages. Specifically, compared to others, drones have a wider field of bird's eye view, which is less likely to be blocked, and they could collect more complete and natural vehicle trajectory data. Besides, they are not easily observed by traffic participants and ensure that the human driver behavior data collected is realistic and natural. In this paper, we present a complete vehicle trajectory data extraction framework based on aerial videos. It consists of three parts: 1) objects detection, 2) data association, and 3) data cleaning. In particular, considering that the hovering drone can be approximated as a fixed camera, we propose an improved object detection algorithm based on classical image processing algorithms. It overcomes the shake effects of drone-based aerial videos and can be directly applied to the automatic detection of moving vehicles without manual annotation data. The output of the algorithm is the vehicle rotated bounding box information with high accuracy, including vehicle center position, vehicle heading, and vehicle dimension. In addition, the improved detection algorithm can be used for vehicle object automatic annotation.
Wang, Zhenyu, Yu, Zhuoping, Tian, Wei, Xiong, Lu, Tang, Chen
An ODD-Based Scalable Assurance Framework for Automated Driving Systems2023-01-05744/11/2023
Due to the increasing complexities, the safety assurances for Automated Driving Systems (ADSs) and Advanced Driver Assistance Systems (ADASs) pose challenges. Recent development within the industry and academia suggests a scenario-based approach underpinned by the system’s Operational Design Domain (ODD) for its safety assurance. In such framework, the ODD defines the safe operating boundary, whereas the scenarios set out individual test conditions. To assess the behavior of the system, a critical element for road safety is the ability to respect the rules of the road. This paper joins together ODDs, scenarios, and rules of the road to form a scalable ODD-based safety assurance framework. The backbone of the framework contains a coherent and common taxonomy to describe the ODDs and behavior library, the scenario tagging structure from the ASAM OpenLABEL standard has been used in the example use case. The workflow utilizes the system’s ODD and behavior library as input to perform filtering and matching activities over a set of testing scenarios and the rules of the road library. Firstly, the ODD and behavior input are used to filter the applicable scenarios within the initial scenario set. At the same time the ODD and behavior input can also be used to filter the applicable rules within the rules of the road library. By further utilizing the ODD and behavior tags covered by the applicable scenarios, and applying them to the rules of the road library, the applicable scenarios related rules can be identified; similarly using the ODD and behavior tags covered by the applicable rules, and applying them to the initial scenario set, the applicable rules related scenarios can be obtained. Such process allows the most relevant rules and scenarios to be used when testing a target system. Furthermore, by comparing the applicable scenarios related rules with the applicable rules, and comparing the applicable rules related scenarios with the applicable scenarios, one can gain an understanding on the effectiveness and the efficiency of the test. When combined with the wider scenario-based evaluation criteria, the framework illustrated within this paper provides a novel and effective way to conduct and evaluate tests.
Zhang, Xizhe, Khastgir, Siddartha, Jennings, Paul
Design of a Secure Automated Driving Systems Test Data Interface2023-01-08284/11/2023
Vehicles equipped with Level 4 and 5 autonomy will need to be tested according to regulatory standards (or future revisions thereof) that vehicles with lower levels of autonomy are currently subject to. Today, dynamic Federal Motor Vehicle Safety Standards (FMVSS) tests are performed with human drivers and driving robots controlling the test vehicle’s steering wheel, throttle pedal, and brake pedal. However, many Level 4 and 5 vehicles will lack these traditional driver controls, so it will be impossible to control these vehicles using human drivers or traditional driving robots. Therefore, there is a need for an electronic interface that will allow engineers to send dynamic steering, speed, and brake commands to a vehicle. This paper describes the design and implementation of a market-ready Automated Driving Systems (ADS) Test Data Interface (TDI), a secure electronic control interface which aims to solve the challenges outlined above. The interface consists of a communication port integrated into automobiles which lack traditional manual controls. Via this interface dynamic test scenarios can be executed by plugging in a Vehicle Control Unit (VCU), which is an element of the TDI hardware/firmware intended to be available only to authorized users. Physically, the VCU can interface with the On-board Diagnostics (OBD) OBD-II connector that is present on essentially all modern automobiles. This paper also describes a demonstration of the complete VCU with the secure TDI using a vehicle equipped with a Dataspeed drive-by-wire kit, with its steering, throttle, and brake control systems mimicking the behavior of an autonomous subject vehicle.
Zagorski, Scott, Nguyen, An, Heydinger, Gary, Abbey, Howard
Injury Assessment in Non-Standard Seating Configurations in Highly Automated Vehicles Using Digital Twin and Active Learning2023-01-00064/11/2023
Human-driven vehicles are going to be replaced by highly automated vehicles as one of the future mobility trends. Even though highly automated vehicles’ active safety systems can protect against vehicle-to-vehicle accidents, the traffic mix between human-driven vehicles and highly automated vehicles is still a potential source of vehicle collisions. Additionally, occupants in highly automated vehicles will be passengers not necessarily dealing with driving anymore, so there will be a considerable number of non-standard seating configurations. Those configurations are not able to be assessed for safety by hardware testing due to their number, variability and complexity. The objective of the paper is the development of a fast virtual approach to identify the passengers’ injury risk in non-standard seating configurations under multi-directional impact scenarios and severity. We deploy the concept of surrogate modeling, where we introduce a digital twin for the expected automated vehicle interiors. Non-standard seating configurations are represented by a simplified model of four seats located in the vehicle. These seats are occupied by a previously developed scalable human body model representing passengers of variable anthropometry. Thanks to the vehicle interior simplification and the hybrid human body model, thousands of simulations describing the impacts identified can be run. Based on the numerical simulations describing impact scenarios, a fast and lean artificial intelligence (AI) model actively learns a digital twin to approximate injury risk predictions for a huge number of possible crash scenarios fast. An impact scenario concerns a seating configuration occupied by up to 4 passengers (a seat can be empty) of variable height, weight, age and gender and crash direction and severity (velocity). Behind AI, the machine learning method uses supervised classifiers that are trained to predict injury severity based on the given input. There are 8 trained classifiers per passenger, each one for a body segment, where multi (predicting 4 injury severity levels) and binary (predicting injury or non-injury levels) classifications are compared. The machine learning accuracy is compared by the Mattheus correlation coefficient, where the presented digital twin AI approach reasonably approximates the numerical solution.
Hyncik, Ludek, Talimian, Abbas, Vychytil, Jan, Kleindienst, Jan, Gharbi, Slim, Ziazopoulos, Pantelis
Research on the Development Path and Policy Recommendations of Vehicle Infrastructure Cooperation2022-01-706512/22/2022
By looking into the vehicle-infrastructure cooperation (VIC) which is oriented towards intelligent, networked and integrated development, this paper analyzes and proposes the essence and development direction of Intelligent Vehicle Infrastructure Cooperation Systems (I-VICS). With an in-depth analysis of technologies of core importance to VIC and influence factors that constrain VIC development as a whole, the paper comes up with a technological route for VIC, and identifies a direction for vehicle-infrastructure cooperative development that progresses from primary to intermediate cooperation, then to advanced cooperation, and finally to full-fledged cooperation. Policy recommendations aiming at strengthening top-level design, building an integrated vehicle-infrastructure-cloud platform, expediting independence of key techs, building robust standards and regulations for VIC, enhancing workforce development as well as greater efforts at market promotion are put forward.
Lei, Chun, Zeng, Jiao, Jiang, Yuan, Li, Zhe
SAE TOMORROW TODAY: Simulation Tool Chain for AVs Achieves SAE Level 31337212/16/2022
The race is on to develop an autonomous vehicle (AV) that is safer than a human driver -- and one automaker is coming closer. By joining forces with Ansys, the global leader in engineering simulation, the BMW Group has developed the first-ever end-to-end tool chain specifically guided by safety principles to develop and validate ADAS and automated/autonomous driving functions. Through this collaboration, the BMW Group is leveraging Ansys' unique simulation capabilities to become one of the first automotive manufacturers to offer SAE Level 3 highly automated driving to consumers. The collaboration is key to quickly addressing advanced driver-assistance systems (ADAS) and AV system reliability and significantly speed time to market. As shown in SAE J3016, the transition from SAE Level 2 to Level 3 is significant. With state-of-the-art software solutions that are cloud-native, scalable, fit for massive data, open and extensible, the partnership between BMW and Ansys has resulted in an automated simulation tool chain that supports the mass generation of safety relevant scenarios and related analytics to validate AV system performance. We sat down with Nicolas Orand, Senior R&D Director of Autonomous Product Line at Ansys, and Rene Grosspietsch, Business Development Autonomous Driving at BMW Group, to discuss their ground-breaking partnership, the critical role of simulation in achieving SAE Level 3 autonomy, and the path to fully autonomous driving.
Hineman, Marcie
Automated Vehicles and Infrastructure Enablers: ElectrificationEPR202202912/15/2022
Highly automated vehicles are being developed alongside a variety of novel, disruptive technologies and a global focus on reducing greenhouse gas emissions from transportation. ADS can reduce emissions and improve fuel efficiency for vehicles powered by traditional internal combustion engines. Electric motors can further raise the bar for both those areas, especially if the power used to charge an electric vehicle is generated from renewable sources. However, implementing electrified AVs requires a viable charging infrastructure. Automated Vehicles and Infrastructure Enablers: Electrification covers issues concerning infrastructure and the electrification of all forms of vehicles: heavy-duty vehicles like trucks and buses, light-duty vehicles like cars and vans, micro-mobility, and new form factors. Click here to access The Mobility Frontier: Accelerating Infrastructure Readiness for Autonomy Click here to access the full SAE EDGETM Research Report portfolio.
Coyner, Kelley, Bittner, Jason
Vertiport Assessment and Mobility Operations SystemTBMG-4712012/1/2022
It is widely believed that Advanced Air Mobility (AAM) is poised to have a significant societal impact in the coming years to move people and cargo more rapidly and efficiently. AAM refers to a new mode of transportation utilizing highly automated airborne vehicles for transporting goods and/or people. The main goals of AAM vehicles are to reduce emissions, to increase connectivity and speed, while helping to reduce traffic congestion. These vehicles can take off and land vertically in designated urban locations called vertiports.
An Interface Approach for Safety and Cybersecurity Management Systems in Highly Automated Driving VehiclesSAE-PP-0030410/28/2022
To ensure safety and security of highly automated driving systems one shall make sure all risks are reduced to a reasonable level and an all potential cyberattacks are addressed with necessary protection. Because of the complexity of such vehicle systems, systematic and structured management approaches are vital to maintaining safety via cybersecurity (CS). The interface of Safety Management System (SMS) with Cybersecurity Management System (CSMS) is one of the key aspects to ensuring that potential safety issues are addressed. Both management systems include planning, concepts, and process development, with significant areas of overlapping management systems is required. Regarding the management systems interface and distribution, it is still a challenge that Highly Automated Driving (HAD) vehicles needs to overcome by means of effective implementation and strategies with continuous improvement and a reduction of miscommunication. From that motivation, a set of engineering risk management framework are proposed in this paper. Subsequently, introducing the interface areas between the safety and the cybersecurity domain is one of the focus areas of this paper, together with the representation of the interface management activities with exemplary interaction template. Additionally, mapping in between safety and cybersecurity related standards in terms of evidence and management systems is represented partially to support both safety case and security assurance.
Khatun, Marzana, Wagner, Florence, Jung, Rolf, Glaß, Michael
An Adaptable Security by Design Approach for Ensuring a Secured Remote Monitoring Teleoperation (RMTO) of an Autonomous VehicleSAE-PP-0030210/25/2022
Remote Monitoring and Teleoperation (RMTO) of Autonomous Vehicles (AV) is advancing in pace in the industry. Researchers and industrial partners explore the role RMTO plays in helping AV navigate through complicated situations among many others. At the heart of this, lies the problem of potential pathways and attack vectors or threat surfaces by which a malicious attack can be carried out on an RMTO and on an AV itself. The separation of cybersecurity considerations in RMTO is barely considered, as so far the majority of available research and activities mainly focused on AV. The main focus of this paper is addressing RMTO cybersecurity utilizing an adaptable security-by-design approach, although security-by-design is still in the infant state within automotive cybersecurity. An adaptable security-by-design approach for RMTO covers Security Engineering Life-cycle, Logical Security Layered Concept, and Security Architecture. Based on the international automotive cybersecurity standards - ISO/SAE 21434, a Threat Analysis and Risk Assessment (TARA) with a formalisation of the highest level of threats identified from the TARA of the RMTO system is carried out, with corresponding mitigation actions as per UNECE WP29. The adaptable security-by-design approach has been then applied to a prototype RMTO system, developed by an industrial partner. Finally, penetration testing has been carried out where the results verify the capability of the adoptable security-by-design to reinforce the security of the RMTO systems against some of the identified risks and threats.
Iyieke, Victormills, Jadidbonab PhD, Hesamaldin, Bryans PhD, Jeremy, Robinson, Tom
Autonomous Field RoboticsEPR202202310/17/2022
The market penetration of highly automated agricultural vehicles in crop farming and arable environments is still very low. However, the unsettled issues and market barriers stem from three main topics. The first is the technical development and appropriate framework conditions for hardware and software required for autonomous field vehicles. The second is the regulatory framework needed to facilitate investment by manufacturers and users. Finally, the third topic is the willingness of the user to accept the non-deterministic systems that are common in agricultural practices today. Autonomous Field Robotics is a joint report between SAE International and the German Institute for Standardization (DIN) developed to enable relevant stakeholders—including users, regulators, researchers, and manufacturers, among others—to discuss the barriers facing automated agricultural vehicles. The report includes a cross-industry and cross-sector exchange on the three central aspects, a prioritization of the unsettled questions to be addressed, as well as a pragmatic framework to enable the use of autonomous field robotics beyond the scientific context in agricultural practice. Click here to access the full SAE EDGETM Research Report portfolio.
Lehmann, Johannes, Dwerlkotte, Nina, Saxe, Friederike
Advanced Continuous Sensing Technology for Hydraulic Brake Fluid2022-01-11859/19/2022
The Continuous Fluid Level and Quality Indicator (CFLQI) technology is focused on increasing the sampling frequency of brake fluid reservoir volume and detecting specific brake fluid contaminants. CFLQI targets to improve diagnostics detection range and resulting degraded vehicle operation strategies by increasing sensitivity to brake fluid loss and the addition of a fluid quality feature. The theory of CFLQI is to improve future autonomous and highly automated vehicle performance, both of which will have reduced driver input and service schedules, by providing earlier fluid level and fluid health warnings. The two technologies selected to prove theory of operation were ultra-sonic sensor and capacitive sense element technology. Both technologies show initial capability to meet fluid sensing targets with system level ASIL D ASIC design. The CFLQI compliments and improves upon current technology of brake pad wear sensors, leak detection diagnostics and brake fluid level monitoring. The increased data will be used in conjunction with current technology to define if a system fluid weep or contamination exists and the rate in which degraded vehicle operating states should be enacted. The methodology for the design began with defining the necessary diagnostics capabilities and fluid level sensor output strategy during dynamic and static vehicle states. Leading to need of continuous fluid level readouts to provide earlier warnings for previously undetectably low brake fluid leak rates, and ability to use sensor hardware to perform reference checks for monitoring changes in brake fluid composition.
Leether, Cole, Nguyen, Hung, Weber, Steven
DEVELOPING A MODEL OF DRIVER PERFORMANCE, SITUATION AWARENESS, AND COGNITIVE LOAD CONSIDERING DIFFERENT LEVELS OF PARTIAL VEHICLE AUTONOMY2024-01-39618/10/2022
ABSTRACT To optimize the use of partially autonomous vehicles, it is necessary to develop an understanding of the interactions between these vehicles and their operators. This research investigates the relationship between level of partial autonomy and operator abilities using a web-based virtual reality study. In this study participants took part in a virtual drive where they were required to perform all or part of the driving task in one of five possible autonomy conditions while responding to sudden emergency road events. Participants also took part in a simultaneous communications console task to include an element of multitasking. Situation awareness was measured using real-time probes based on the Situation Awareness Global Assessment Technique (SAGAT) as well as the Situation Awareness Rating Technique (SART). Cognitive Load was measured using the NASA Task Load Index (NASA-TLX) and an adapted version of the SOS Scale. Other measured factors included multiple indicators of driving performance and secondary task performance. Results indicate a relationship between performance and autonomy level. Citation: J. E. Cossitt, V. R. Patel, D. W. Carruth, V. J. Paul, C. L. Bethel, “Developing a Model of Driver Performance, Situation Awareness, and Cognitive Load Considering Different Levels of Partial Vehicle Autonomy,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 16-18, 2022.
Cossitt, Jessie E., Patel, Viraj R., Carruth, Daniel W., Paul, Victor J., Bethel, Cindy L.
EDITORIAL: Platooning still promising for trucking22TOFHP08_048/1/2022
There is “no business case” for platooning, or the electronic coupling of two or more trucks in close formation. That was the assessment of Daimler Trucks in 2019 when it decided to pause its years-long platooning development activities. The OEM determined that for U.S. long-distance applications, where conditions were expected to be ideal, the fuel savings were less than stellar and diminished further when the platoon got “disconnected” and trucks had to accelerate to reconnect. Instead, the company turned its full attention to developing highly automated (SAE Level 4) trucks. The fate of Peloton Technology, a company all-in on platooning but that ceased operations in 2021, is another indicator that perhaps platooning's promise has faded.
Gehm, Ryan
Toward an Automated Scenario-Based X-in-the-Loop Testing Framework for Connected and Automated Vehicles12-05-04-00306/27/2022
Emerging technologies for connected and automated vehicles (CAVs) are rapidly advancing, and there is an incremental adoption of partial automation systems in existing vehicles. Nevertheless, there are still significant barriers before fully or highly automated vehicles can enter mass production and appear on public roads. These are not only associated with the need to ensure their safe and efficient operation but also with cost and delivery time constraints. A key challenge lies in the testing and validation (T&V) requirements of CAVs, which are expected to be significantly higher than those of traditional and partially automated vehicles. Promising methodologies that can be used toward this goal are scenario-based (SBT) and X-in-the-Loop (XiL) testing. At the same time, complex techniques such as co-simulation and mixed-reality simulation could also provide significant benefits. Nevertheless, the benefits of individual solutions are likely to be significantly smaller, if considered in isolation without any supporting test automation methods. This article attempts to combine existing knowledge and state of the art to explore the development of a framework for automating the T&V needs of CAVs. To this end, the integration of the VeriCAV framework for automating SBT with the Digital CAV Proving Ground Feasibility Study (DigiCAV) XiL mixed-reality CAV development and evaluation platform has been explored. The goal of the new framework is to enable an iterative and incremental approach across all stages of CAV development through the combination of optimal scenario generation and a comprehensive XiL scenario execution environment. This article presents an overview of the new framework as well as preliminary proof of concept results.
Kyriakopoulos, Ioannis, Jaworski, Pawel, Edwards, Tim
Generic Crash Pulses Representing Future Accident Scenarios of Highly Automated Vehicles09-10-02-00104/1/2022
Assessment of crashworthiness of autonomous vehicles (AVs) must be carried out for future crash scenarios, as not all crashes will be avoidable. Representative crash pulses for AVs are needed to evaluate conceptual design restraint systems of those vehicles. Within this study, generic crash pulses for crash scenarios expected to be relevant for AVs were generated based on a set of vehicle-to-vehicle structure simulations with current European sedan cars. These crash scenarios included one Straight Crossing Path (SCP) and two Left Turn Across Path Opposite Direction (LTAP OD) scenarios with varying initial velocities and weight ratios of the crash opponents to obtain different crash configurations. Additionally, full-width frontal simulations with 40 kph and 56 kph were included as a reference. The acceleration signals obtained from the individual simulations were approximated by Legendre polynomials. A prediction model was created for each crash configuration based on a set of three to four pulses to provide a generic crash pulse for each investigated crash configuration. The prediction quality of the model was tested against simulations with freely available finite element (FE) models. A number of 20 or 60 basis functions of Legendre polynomials was needed to approximate the obtained velocity or acceleration signals with a correlation of at least 0.9. The obtained prediction model for each crash configuration was able to provide good predictions for heading direction when simulations were within the training data. Approximating velocities and differentiation to derive acceleration turned out to be the better option than approximating the acceleration directly. Eventually, five crash configurations were proposed for future investigations. The derived generic crash pulses capture a range of loading severities, directions, and stiffness of current heavy sedan cars. The pulses can be used for sled simulations to evaluate restraint systems in conceptual studies when no vehicle-specific crash pulses are available.
Höschele, Patrick, Smit, Stefan, Tomasch, Ernst, Östling, Martin, Mroz, Krystoffer, Klug, Corina
DRSPI - A Framework for Preserving Automated Vehicle Safety Claims by Unknown Unknowns Recognition and Dynamic Runtime Safety Performance Indicator Improvement2022-01-08133/29/2022
A safe automated vehicle must “know when it doesn’t know.” Automated vehicles cannot depend on the traditional drive-fail-fix cycle due to heavy tail problem distributions supplying virtually infinite problems. In order to be safe, automated vehicles require the ability to handle unforeseen untested “unknown unknown” situations. Safety Performance Indicators (SPIs) at deep-enough sub-claim levels can uncover safety case claim violations in a ‘leading’ fashion - prior to safety events. This paper introduces Dynamic Realtime SPIs (SPIs calculated at runtime) at sufficiently low safety case claim levels which yield runtime recognition of safety case claim violations and can be used by the ADS to infer that it is encountering an “unknown unknown” situation. Then, because “knowing when an ADS doesn’t know” is insufficient to ensure AV safety, we introduce the Dynamic Realtime SPI (DRSPI) framework, for handling such occurrences. The DRSPI framework includes methodical assignment of one or more SPI improvement mechanisms (IMs) to each SPI such that the ADS can dynamically adjust its performance in response to unknown situations as witnessed by leading SPIs monitored in real-time. As a result, unknown unknowns are recognized, control is adjusted, safety performance is brought back up, and the integrity of the safety case sub-claim(s) are re-established in the face of unknown unknown situations. An example application of the Dynamic Realtime SPI Improvement framework, including Realtime SPIs attached to safety case sub-claims, is also presented.
Diaz, Michael, Woon, Michael
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