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This paper presents the details of the model for the physical steering system used on the National Advanced Driving Simulator. The system is basically a hardware-in-the-loop (steering feedback motor and controls) steering system coupled with the core vehicle dynamics of the simulator. The system's torque control uses cascaded position and velocity feedback and is controlled to provide steering feedback with variable stiffness and dynamic properties. The reference model, which calculates the desired value of the torque, is made of power steering torque, damping function torque, torque from tires, locking limit torque, and driver input torque. The model also provides a unique steering dead-band function that is important for on-center feel. A Simulink model of the hardware/software is presented and analysis of the simulator steering system is provided.

This paper presents an overview of work performed by the National Highway Traffic Safety Administration's (NHTSA) Vehicle Research and Test Center (VRTC) to test, validate, and improve the planned National Advanced Driving Simulator's (NADS) vehicle dynamics simulation. This vehicle dynamics simulation, called NADSdyna, was developed by the University of Iowa's Center for Computer-Aided Design (CCAD) NADSdyna is based upon CCAD's general purpose, real-time, multi-body dynamics software, referred to as the Real-Time Recursive Dynamics (RTRD), supplemented by vehicle dynamics specific submodules VRTC has “beta tested” NADSdyna, making certain that the software both works as computer code and that it correctly models vehicle dynamics. This paper gives an overview of VRTC's beta test work with NADSdyna. The paper explains the methodology used by VRTC to validate NADSdyna.

The paper discusses the development of a model for the 2006 BMW 330i for the National Advanced Driving Simulator's (NADS) vehicle dynamics simulation, NADSdyna. The front and rear suspensions are independent strut and link type suspensions modeled using recursive rigid-body dynamics formulations. The suspension springs and shock absorbers are modeled as force elements. The paper includes parameters for front and rear semi-empirical tire models used with NADSdyna. Longitudinal and lateral tire force plots are also included. The NADSdyna model provides state-of-the-art high-fidelity handling dynamics for real-time hardware-in-the-loop simulation. The realism of a particular model depends heavily on how the parameters are obtained from the actual physical system. Complex models do not guarantee high fidelity if the parameters used were not properly measured. Methodologies for determining the parameters are detailed in this paper.

The paper discusses the development of a model for a 1998 Chevrolet Malibu for the National Advanced Driving Simulator’s (NADS) vehicle dynamics simulation, NADSdyna. The Malibu is the third vehicle modeled for the NADS, and this is the third paper dealing with model development. SAE Paper 970564 contains details of the model for the 1994 Ford Taurus and SAE Paper 1999–01-0121 contains details of the model for the 1997 Jeep Cherokee. The front and rear suspensions are independent strut and link type suspensions modeled using recursive rigid body dynamics formulations. The suspension springs and shock absorbers are modeled as elements in the rigid body formulation. To complement the vehicle dynamics for the NADS application, subsystem models that include tire forces, braking, powertrain, aerodynamics, and steering are added to the rigid body dynamics model. The models provide state-of-the-art high fidelity vehicle handling dynamics for real-time simulation.

In crashes between heavy trucks and light vehicles, most of the fatalities are the occupants of the light vehicle. A reduction in heavy truck stopping distance should lead to a reduction in the number of crashes, the severity of crashes, and consequently the numbers of fatalities and injuries. This study made use of the National Advanced Driving Simulator (NADS). NADS is a full immersion driving simulator used to study driver behavior as well as driver-vehicle reactions and responses. The vehicle dynamics model of the existing heavy truck on NADS had been modified with the creation of two additional brake models. The first was a modified S-cam (larger drums and shoes) and the second was an air-actuated disc brake system. A sample of 108 CDL-licensed drivers was split evenly among the simulations using each of the three braking systems. The drivers were presented with four different emergency stopping situations.

This paper discusses the improvement of a heavy truck anti-lock brake system (ABS) model currently used by the National Highway Traffic Safety Administration (NHTSA) in conjunction with multibody vehicle dynamics software. Accurate modeling of this complex system is paramount in predicting real-world dynamics, and significant improvements in model accuracy are now possible due to recent access to ABS system data during on-track experimental testing. This paper focuses on improving an existing ABS model to accurately simulate braking under limit braking maneuvers on high and low-coefficient surfaces. To accomplish this, an ABS controller model with slip ratio and wheel acceleration thresholds was developed to handle these scenarios. The model was verified through testing of a Class VIII 6×4 straight truck. The Simulink brake system and ABS model both run simultaneously with TruckSim, with the initialization and results being acquired through Matlab.

This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.