Real-Time Applications

This page contains a limited bibliography of technical papers and presentations related to CarSim and TruckSim in real-time applications such as driving simulators and testing of hardware in the loop (HIL).

Rubanraj Sekar, Olivia Jacome, Jeffrey Chrstos, and Stephanie Stockar (Ohio State University), "Assessment of Driving Simulators for Use in Longitudinal Vehicle Dynamics Evaluation," SAE Paper 2022-01-0533. March 2022. Show summary

In the last decade, the use of Driver-in-the-Loop (DiL) simulators has significantly increased in research, product development, and motorsports. To be used as a verification tool in research, simulators must show a level of correlation with real-world driving for the chosen use case. This study aims to assess the validity of a low-cost, limited travel Vehicle Dynamics Driver-in-Loop (VDDiL) simulator by comparing on-road and simulated driving data using a statistical evaluation of longitudinal and lateral metrics. The process determines if the simulator is appropriate for verifying control strategies and optimization algorithms for longitudinal vehicle dynamics and evaluates consistency in the chosen metrics. A validation process explaining the experiments, choice of metrics, and analysis tools used to perform a validation study from the perspective of the longitudinal vehicle model is shown in this study.

Validity is measured statistically, where a simulator is said to have absolute validity if the chosen metrics are not statistically different between on-road and simulator driving. Metrics showing statistically significant differences but with a consistent trend imply relative validity. In this study, an instrumented vehicle is used to collect on-road driving data in a defined urban route with 14 drivers with no professional driving experience. The vehicle model used in the simulator was matched to the actual vehicle in the longitudinal dynamics using CarSim. The urban route used for on-road driving was replicated in the virtual world with geometric accuracy.

VDDiL was rated to have moderate consistency with the realworld driving experience implying medium physical fidelity with a combination of absolute and relative validity for longitudinal metrics. On average, the speed-based metrics in the simulator were approximately 5 mph higher, with acceleration and jerk-based metrics about 1.5 - 3 times of corresponding on-road metrics.

Joshi, A., "Powertrain and Chassis Hardware-in-the-Loop (HIL) Simulation of Autonomous Vehicle Platform," SAE Paper 2017-01-1991. September 2017. Show summary

Autonomous vehicle being developed by the automotive industry consists of two main components: the software which is responsible for the decision-making capabilities of the system, and the hardware which encompasses all aspects of the physical vehicle responsible for motion such as the engine, brakes and steering subsystems along with their corresponding controls. This component forms the basis of the autonomous vehicle platform.

For SAE Level 4 autonomous vehicles, where an automated driving system is responsible for all the dynamics driving tasks including the fallback driving performance in case of system faults, redundant mechanical systems and controls are required as part of the autonomous vehicle platform since the driver is completely out of the loop with respect to driving. Because testing autonomous vehicles is expensive, time-consuming, and unsafe due to the number of scenarios and driven kilometers required for validation, a simulation platform, which can provide a controlled and consistent testing environment, is required for rapid prototyping and testing of the hardware components of the autonomous vehicle.

This paper focuses on a powertrain and chassis hardware-in-the-loop (HIL) simulation of the autonomous vehicle platform and the correlation of the performance of the corresponding subsystems with those of the actual autonomous vehicle. This setup includes powertrain controllers and actuators, redundant brakes and steering controllers, alongside full brake hydraulics hardware. A 2017 Ford Fusion Hybrid was used as the vehicle platform for simulation. The simulation of other subsystem plants and controllers was achieved by using a real-time CarSim-Simulink co-simulation environment representative of the 2017 Ford Fusion Hybrid through a dSPACE HIL simulator.

Joshi, A., "Hardware-in-the-Loop (HIL) Implementation and Validation of SAE Level 2 Autonomous Vehicle with Subsystem Fault Tolerant Fallback Performance for Takeover Scenarios," SAE Paper 2017-01-1994. September 2017. Show summary

Most ADAS systems today fall under the classification of SAE Level 1 which is also referred to as the driver assistance level. The progression from SAE Level 1 to SAE Level 2 or partial automation involves the critical task of merging autonomous lateral control and autonomous longitudinal control such that the tasks of steering and acceleration/deceleration are not required to be handled by the driver under certain conditions. However, the driver is still required to monitor the driving environment and handle scenarios where control is handed over to the driver due to subsystem faults of the autonomous system. Since vehicle testing is expensive, time-consuming and hazardous an alternative method of development and validation is required.

The objectives of this research are two-fold. The first focuses on a real-time powertrain-based Hardware-in-the-Loop (HIL) implementation and validation of an SAE Level 2 autonomous vehicle. The second objective focuses on studying the performance of SAE Level 2 autonomous vehicles during takeover scenarios due to subsystem faults. To accomplish these objectives, an acceleration-based Adaptive Cruise Control (ACC) was combined with a path-following lateral control along with supervisory control for system mode transitions due to system deactivations and faults. This research presents system modes in which longitudinal control only and lateral control only are engaged as fallback states to the full autonomous system being faulted for lateral control and longitudinal control failures respectively. Simulations were conducted to evaluate the performance of the autonomous controls when subjected to these faults. A powertrain subsystem representative of the 2017 Ford Fusion Hybrid was used as the hardware simulation platform using a dSPACE HIL simulator and CarSim RT.

Joshi, A., "Real-Time Implementation and Validation for Automated Path Following Lateral Control Using Hardware-in-the-Loop (HIL) Simulation," SAE Technical Paper 2017-01-1683, 2017, doi:10.4271/2017-01-1683. SAE paper 2017-01-1683. March 2017. Show summary

Software for autonomous vehicles is highly complex and requires vast amount of vehicle testing to achieve a certain level of confidence in safety, quality and reliability. According to the RAND Corporation, a 100 vehicle fleet running 24 hours a day 365 days a year at a speed of 40 km/hr, would require 17 billion driven kilometers of testing and take 518 years to fully validate the software with 95% confidence such that its failure rate would be 20% better than the current human driver fatality rate [1]. In order to reduce cost and time to accelerate autonomous software development, Hardware-in-the-Loop (HIL) simulation is used to supplement vehicle testing. For autonomous vehicles, path following controls are an integral part for achieving lateral control. Combining the aforementioned concepts, this paper focuses on a real-time implementation of a path-following lateral controller, developed by Freund and Mayr [2]. The controller is implemented on a powertrain subsystem HIL simulation bench to enable lateral control of the longitudinal controlled HIL setup for automated driving applications. 2017 Ford Fusion Hybrid powertrain controllers and actuators were used as the hardware platform for the powertrain subsystem. The simulation of other subsystem plants and controllers was achieved by using a real-time CarSim-Simulink co-simulation environment representative of the 2017 Ford Fusion Hybrid through a dSPACE HIL simulator.The objectives of this research were three-fold. The first objective was to implement a real-time version of the path-following lateral controller to add lateral capability to a powertrain-based longitudinal controlled HIL setup. The second objective was to validate the path-following capability of the lateral controller. Lastly, the third objective was to quantitatively understand the real-time behavior and sensitivity of the lateral controller using simulations over varying vehicle inertial and environmental conditions such as speed, payload mass, payload position, surface type/friction, rapid acceleration/deceleration, and crosswinds.

Gary Bertollini, Linda Brainer, Jacqueline A. Chestnut, Steven Oja, and Joseph Szczerba (General Motors). "General Motors Driving Simulator and Applications to Human Machine Interface (HMI) Development." SAE paper 2010-01-1037. April 2010. Show summary

This report describes a new driving simulator capability at General Motors (GM) Research and Development's (R&D) Vehicle Development Research (VDR) Laboratory and its application in an iterative HMI development process. The paper also provides an overview of three recent simulator usability tests supporting HMI development.

Tomoya Toyohira (Honda). "The Validity of EPS Control System Development using HILS. SAE paper 2010-01-0008." April 2010. Show summary

In recent years, the increased use of electric power steering in vehicles has increased the importance of issues such as making systems more compact and lightweight, and dealing with increased development man-hours.

John Wilkinson, Thomas Klingler (General Motors), and Cedric W. Mousseau (Michelin Tire). "Brake Response Time Measurement for a HIL Vehicle Dynamics Simulator." SAE paper 2010-01-0079. April 2010. Show summary

Vehicle dynamics simulation with Hardware In the Loop (HIL) has been demonstrated to reduce development and validation time for dynamic control systems. For dynamic control systems such as Anti-lock Braking System (ABS) and Electronic Stability Control (ESC), an accurate vehicle dynamics performance simulation system requires the Electronic Brake Control Module (EBCM) coupled with the vehicles brake system hardware. This kind of HIL simulation-specific software tool can further increase efficiency by means of automation and optimization of the development and validation process. This paper presents a method for HIL vehicle dynamics simulator optimization through Brake Response Time (BRT) correlation. The paper discusses the differences between the physical vehicle and the HIL vehicle dynamics simulator. The differences between the physical and virtual systems are used as factors in the development of a Design Of Experiment (DOE) quantifying HIL simulator performance. Finally, the DOE results are used to drive the development of a tool to correlate the HIL system hardware to the physical vehicle BRT. This leads to the development of hardware with improved BRT, and to the design of new HIL simulators with improved brake response.

Yuuki Shiozawa, Masatsugu Yokote, Masaaki Nawano, Hiroshi Mouri (Nissan Motor Co., Ltd.), "Development of a Method for Controlling Unstable Vehicle Behavior." SAE paper 2007-01-0840, April 2007. Show summary

A model-based predictive controller is validated for a lane keeping assistant system using CarSim in a HIL system.

Goossens, P., "Model-based Design, Virtual Prototyping and Automated Testing of Electromechanical Subsystems for Automotive Applications," Opal-RT Technologies, July 2004. Click here for the PDF | Show summary

HIL testing of automotive subsystems using the Opal-RT platform at McGill University.

Watanabe, Y., Sayers , M.W., "Extending Vehicle Dynamics Software for Analysis, Design, Control, and Real-Time Testing," presented at the The 6th AVEC Symposium, Hiroshima, Japan, Sep 9-13, 2002. Show summary

An overview of CarSim and CarSim RT.

Chen, C. and Peng, H., "Rollover prevention for Sports Utility Vehicles With Human-in-the-Loop Evaluations," 5th Int'l Symposium on Advanced Vehicle Control, August 2000, Ann Arbor, Michigan. Click here for the PDF | Show summary

Rollover is studied using TruckSim for regular simulation and also a driving simulator.

Sayers, M.W., "Vehicle Models for RTS Applications." Vehicle System Dynamics, Vol. 32, No. 4-5, Nov. 1999, pp. 421-438. Show summary

This paper presents the modeling assumptions in CarSim RT.