Browse Topic: Hardware-in-the-loop (HIL)
ABSTRACT To realize the full potential of simulation-based evaluation and validation of autonomous ground vehicle systems, the next generation of modeling and simulation (M&S) solutions must provide real-time closed-loop environments that feature the latest physics-based modeling approaches and simulation solvers. Real-time capabilities enable seamless integration of human-in/on-the-loop training and hardware-in-the-loop evaluation and validation studies. Using an open modular architecture to close the loop between the physics-based solvers and autonomy stack components allows for full simulation of unmanned ground vehicles (UGVs) for comprehensive development, training, and testing of artificial intelligence vehicle-based agents and their human team members. This paper presents an introduction to a Proof of Concept for such a UGV M&S solution for severe terrain environments with a discussion of simulation results and future research directions. This conceptual approach features: 1
Abstract This paper presents the development of a transmission-in-the-loop (TiL) experimentation system. In this TiL experimental setup, the input side of the transmission is controlled by a dynamometer emulating the engine, while the output sides of the transmission are controlled by two dynamometers emulating the wheels and vehicle. The models emulating these vehicle components are required to possess sufficient fidelity to simulate engine torque pulse (ETP) and wheel slip dynamics while being computationally efficient to run in real-time. While complex engine and tire models exist in the literature that accurately capture these dynamics, they are often too numerically stiff for real-time simulation. This paper presents the system level details of such a TiL setup, and the modeling concepts for the development of high fidelity real-time models of the engine and tire dynamics for use in this experiment. Parameters of the engine model are identified using experimental data. Vehicle
ABSTRACT The age of large autonomous ground vehicles has arrived. Wherever vehicles are used, autonomy is desired and, in most cases, being studied and developed. The last barrier is to prove to decision makers (and the general public) that these autonomous systems are safe. This paper describes a rigorous safety testing environment for large autonomous vehicles. Our approach to this borrows elements from game theory, where multiple competing players each attempt to maximize their payout. With this construct, we can model an environment that as an agent that seeks poor performance in an effort to find the rare corner cases that can lead to automation failure
ABSTRACT Hardware/software integrated system ensures a system will operate as intended in the same configuration it will be used in the field. Manual system testing can be a very slow and error prone process, as well as being incapable of testing interfaces that humans cannot interact with. Many existing solutions exist to introduce test hardware into the loop for verifying systems, but most of these solutions provide a separate component for each hardware interface. This paper presents an approach for a single integrated system that can test all hardware interfaces of a system under test, managed by a single controller. This test system provides the capability to abstract away the hardware being tested so a test developer can develop tests while only understanding the manual interfaces of the system being tested. We show that this approach can provide a significant acceleration to the time to execute tests, as well as improving the reliability, and consistency of the tests. Citation
ABSTRACT This paper will discuss a hybrid approach for antenna placement optimization on tactical vehicles. Tactical vehicles tend to have collocated antennas that operate in adjacent frequency bands. It may be required that two antennas operate simultaneously to satisfy a wide range of voice and data capabilities. The current process to optimize the location of antennas on platforms involves longer test times, complicated logistics, high costs, and is usually performed in an uncontrolled environment. In order to optimize the placement location and minimize the cosite interference between these antennas with consideration to the top deck obstructions, it is advantageous to use a hybrid method. The hybrid method presented here is the combination of Electromagnetic (EM) Modeling and Simulation (M&S) and Laboratory Hardware in the Loop (HWIL) testing. This paper presents the benefits of using this hybrid method in the areas of test time reduction, lessening costs, easing logistics, and
ABSTRACT Autonomous vehicles provide a unique challenge for simulation to effectively and performantly model due to their system level complexity and the inclusion of autonomy software. This environment is made even more challenging when looking at the interactions of humans in-the-loop with the vehicles and autonomy software and also how to include more simulation in the testing process for Autonomous Vehicles. With the use of a software framework built from a Commercial off the Shelf (COTS) game engine the Ground Vehicle Systems Laboratory demonstrated the feasibility of real-time human, software and hardware in the loop testing of autonomous systems. This approach facilitated the execution of two major events which are described herein. Citation: John Brabbs, Benjamin Haynes, Thomas Stanko, “Using A Gaming Engine for Autonomous Vehicle Modeling and Simulation”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13
ABSTRACT This paper describes the role of Modeling and Simulation (M&S) as a critical tool which must be necessarily used for the development, acquisition and testing of autonomous systems. To be used effectively key aspects of development, acquisition and testing must adapt and change to derive the maximum benefit from M&S. We describe how development, acquisition and testing should leverage and use M&S. We furthermore introduce and explain the idea of testable autonomy and conclude with a discussion of the qualities and requirements that M&S needs to have to effectively function in the role that we envision. Citation: J. Brabbs, S. Lohrer, P. Kwashnak, P. Bounker, M. Brudnak, “M&S as the Key Enabler for Autonomy Development, Acquisition and Testing”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019
ABSTRACT Due to the high complexity of modern internal combustion engines and powertrain systems, the proper calibration of the electronic control unit’s (ECU) parameters has a strong impact on project targets like fuel consumption, emissions and drivability, as well as development costs and project duration. Simulation methods representing the system behavior with a model can support the calibration process considerably. However, standard physics-based models are often not able to describe all effects with sufficient accuracy, or the effort to set up a detailed model is too high. Physics-based models can also have a high computational demand, so that their simulation is not real-time capable. More suited for ECU calibration are data-driven models, combined with Design of Experiment (DoE). The system to be calibrated is identified with few specific test bench or vehicle measurements. From these measurements, a mathematical regression model is built. This paper describes recently
ABSTRACT The cannon Concept Technology Demonstrator is a U.S. military proof of concept 155 mm self-propelled howitzer platform. It demonstrated fully automated ammunition handling, weapon stabilization, and mobility in a 24-ton test platform. The next generation Concept Technology Demonstrator served as a transfer mechanism of capabilities from a heavyweight howitzer platform to a notional future lightweight self-propelled howitzer. Simulation model data of the demonstration platform vehicle response during weapon firing was contrasted with the initial notional lightweight system’s firing stability analysis. The results of this comparison stimulated an updated correlation effort. This correlation effort utilized test firings without chassis stabilizing spades to reveal physics-based simulation model fidelity requirements for future programs. Observations of simulation and system performance were used to define a systematic approach to simulation model fidelity improvements and
ABSTRACT BAE Systems Combat Simulation and Integration Labs (CSIL) are a culmination of more than 14 years of operational experience at our SIL facility in Santa Clara. The SIL provides primary integration and test functions over the entire life cycle of a combat vehicle’s development. The backbone of the SIL operation is the Simulation-Emulation-Stimulation (SES) process. The SES process has successfully supported BAE Systems US Combat Systems (USCS) SIL activities for many government vehicle development programs. The process enables SIL activities in vehicle design review, 3D virtual prototyping, human factor engineering, and system & subsystem integration and test. This paper describes how CSIL applies the models, software, and hardware components in a hardware-in-the-loop environment to support USCS combat vehicle development in the system integration lab
ABSTRACT This work presents the development of a high fidelity Simulation In the Loop/Hardware In the Loop simulation environment using add-ons to Autonomous Navigation Virtual Environment Laboratory (ANVEL) and a navigation unit developed by Auburn University’s GPS and Vehicle Dynamics Lab (GAVLAB) in support of the United States Army’s Autonomous Ground Resupply Science Technology Objective. The developed add-ons include a real time interface for ANVEL, Inertial Measurement Unit module, Wheel Speed Sensor module, and a GPS module that allows simulated signals or generated Radio Frequency signals. The developed add-ons allow for faster development of navigation algorithms and controllers due to a readily available, highly accurate truth from ANVEL and can be configured to introduce realistic errors from sensors, hardware, and GPS signals such that algorithm and controller robustness can be easily examined
In non-cooperative environments, unmanned aerial vehicles (UAVs) have to land without artificial markers, which is a key step towards achieving full autonomy. However, the existing vision-based schemes have the common problems of poor robustness and generalization, and the LiDAR-based schemes have the disadvantages of low resolution, high power consumption and high weight. In this paper, we propose an UAV landing system equipped with a binocular camera to preform 3D reconstruction and select the safe landing zone. The whole system only consists of a stereo camera, and the innovation of the solution is fusing the stereo matching algorithm and monocular depth estimation(MDE) model to get a robust prediction on the metric depth. The whole landing system consists of a stereo matching module, a monocular depth estimation (MDE) module, a depth fusion module, and a safe landing zone selection module. The stereo matching module uses Semi-Global Matching (SGM) algorithm to calculate the
Summary This paper discusses the latest techniques in vehicle modeling and simulation to support ground vehicle performance and fuel economy studies, enable system design optimization, and facilitate detailed control system design. The Autonomie software package, developed at Argonne National Laboratory, is described with emphasis on its capabilities to support Model-in-the-Loop, Software-in-the-Loop (SIL), Component-in-the-Loop (CIL), and Hardware-in-the-Loop simulations. Autonomie supports Model-Based Systems Engineering, which is growing in use as ground vehicles become more sophisticated and complex, with many more subsystems interacting within the vehicle and the environmental conditions in which the vehicles operate becoming more challenging and varied. With the advent of hybrid powertrains, the additional dimension of vehicle architecture has become one of the design variables that must be considered. This complexity results in the need for a simulation tool that is capable of
Proprietary, black box, and other hard-to-model subsystems are a leading source of schedule and labor cost across simulation supported analysis and lifecycle management. Using AI/ML technologies to rapidly develop and deploy digital twins of Hardware in the Loop (HWIL) and software systems reduces the Non-Recurring Engineering (NRE) in Modeling and Simulation (M&S) and supports validation of existing software digital twins. This approach also allows for portability of obsolete or proprietary components into a broader range of simulations or applications without exposing critical technologies. We present results of multiple case studies applying AI to black box components of interest to the ground vehicle community
Traditional live testing of autonomous ground vehicles can be augmented through use of digital twins of the test environment, the vehicle mobility models, and the vehicle sensors. These digital twins combined with the autonomous software under test allow testers to inject faults, weather, obstacles, find edge case scenarios, and collect information to understand the decision making of the autonomous software under test. With this new capability, autonomous ground vehicles can now be tested in four stages. The first stage is testing the autonomous software using digital twins. In this stage with the help of a High-Performance Computer thousands of scenarios can be run. Once issues are communicated and addressed, stage two, hardware in the loop testing can begin. Hardware in the loop uses simulators that already exist to test systems such as autonomous convoys with a virtual leader and a live follower. Stage three employs a live virtual constructive approach by using one vehicle to test
Validation plays a crucial role in any Electronic Development process. This is true in the development of any automotive Electronic Control Unit (ECU) that utilizes the Automotive V process. From Research and Development (R&D) to End of Line (EOL), every automotive module goes through a plethora of Hardware (HW) and Software (SW) testing. This testing is tedious, time consuming, and inefficient. The purpose of this paper is to show a way to streamline validation in any part of the automotive V process using Python as a driving force to automate and control Hardware-in-the-loop (HIL) / Model-in-the-loop (MIL) / Software-in-the-loop (SIL) validation. The paper will propose and outline a framework to control test equipment, such as power supplies and oscilloscopes, load boxes, and external HW. The framework includes the ability to control CAN communication signals and messages. A visual Graphical User Interface (GUI) has also been created to provide simplified operation to the user
Hardware-in-the-loop (HIL) testing is part of automotive V-design which is commonly used in automotive industries for the development of Electronic Control Unit (ECU). HIL test platform provides the capacity to test the ECU in a controlled environment even with scenarios that would be too dangerous or impractical to test on real situation, also the ECU can be tested even before the actual plant under building. This paper presents a HIL test platform for the validation of a seat ECU. The HIL platform can also be used for control and diagnostics algorithm development. The HIL test platform consists of three parts: a real time target machine (dSPACE SCALEXIO AutoBox), an ECU (Magna Seating M12 Module), and a signal conditioning unit (Load Box). The ECU produces the control commands to the real-time target machine through load box. The real time target machine hosts the plant model of the power seat which includes the kinematics and dynamics of the seat movements. The virtual model within
Automotives are provided with a lot of intelligence that monitors, controls, actuates, and diagnose the various aspects of vehicle functionalities. One of the critical parameters required to monitor is Vehicle fuel level. Fuel level in the vehicle is a key input for engine performance, drivability, and fuel level indication in Instrumentation cluster for customer. Most economic and reliable fuel level sensor is resistive sensor with float. The purpose of this paper is to address the wrong fuel level indication in Vehicle level. Wrong fuel level indication may be due to malfunction of Instrumentation cluster signal input or Fuel level sensor function. To verify this, Instrumentation cluster is tested with HIL system instead of real time Fuel level sensor. By configuring the HIL module to analogue resistance channel, cluster is tested for fuel level bar indication. Fuel level sensor is tested by Vehicle level fuel calibration and exact issue is simulated. The failed fuel level sensor is
The advent of BS6 coupled with RDE emission norms has increased the development efforts and costs due to the shear amount of testing and validation on real engines and vehicles which are necessitated by these stringent norms. Front-loading of tasks by moving actual vehicle and engine tasks on to virtual setup, will reduce the development efforts and costs significantly. This front-loading of tasks on to a LABCAR would need real time and highly accurate plant models, tools to parameterize these plant models and accurate data driven models to predict dynamic parameters like emissions. In this collaborative work between Maruti Suzuki India Ltd and ETAS India, ETAS VVTB and ICE plant models were parameterized with the data generated on engine test with ASCMO Global DoE test plan by using ASCMO MOCA. The ASCMO Global test plan also ensures the coverage of data points across the entire engine operating space. These plants models were optimized to an accuracy level of more than 95%. The
Autonomous Emergency Braking (AEB) systems play a critical role in ensuring vehicle safety by detecting potential rear-end collisions and automatically applying brakes to mitigate or prevent accidents. This paper focuses on establishing a framework for the Verification & Validation (V&V) of Advanced Driver Assistance Systems (ADAS) by testing & verifying the functionality of a RADAR-based AEB ECU. A comprehensive V&V approach was adopted, incorporating both virtual and physical testing. For virtual testing, closed-loop Hardware-in-Loop (HIL) simulation technique was employed. The AEB ECU was interfaced with the real-time hardware via CAN. Data for the relevant target such as the target position, velocity etc. was calculated using an ideal RADAR sensor model running on the real-time hardware. The methodology involved conducting a series of test scenarios, including various driving speeds, obstacle types, and braking distances. Automation was leveraged to perform automated testing and
The evolution of automotive Electronic Control Unit (ECU) technology brings the additional safety, comfort, and control to the vehicle. With an exponential increase in the complexity involved in modern-day ECU, it is very important to verify and validate robustness, functionality, and reliability of ECU software [1]. As of now, Hardware in loop [HIL] and Vehicle in Loop validations are well known software functional validation methods. However, these methods require physical setup, which can incur more cost and time during the development phase. In recent years, ECU virtualization gained attention for development and validation of automotive ECUs [2]. The goal is to minimize the effort on software testing. This paper focuses on virtualization of Electric Vehicle (EV) powertrain system using SIL approach. The objective is to provide an adaptable EV-virtualization environment for virtual-ECU (vECU) verification and validation. This paper focuses on standardization of SIL simulation setup
System engineering-based approach is now ubiquitous in the automotive industry. It is a disciplined approach that ensures that targets are clearly defined and met through a structured and holistic approach. In this paper, we report an application of a systems engineering-based methodology for developing seating system features. It starts with a Business Requirement Document (BRD), which enlists the business requirements of a feature. We then developed a Logical Architecture Diagram (LAD) on a Simulink environment, which is an initial proposal for designing the logic to realize the desired functionality. As a next step, we perform Functional Failure Analysis (FFA) on the LAD to identify potential failure modes. We propose a few ways to mitigate the identified failures or modify the design so that these failures are rendered inconsequential to the end user. Based on the updated LAD, a System Requirement Document (SRD) is created, which contains all the requirements corresponding to the
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