Search Results

Modern passenger cars are being equipped with advanced driver assistance systems such as lane departure warning, collision avoidance systems, adaptive cruise control, etc. Testing for operation and effectiveness of these warning systems involves interaction between vehicles. While dealing with multiple moving vehicles, obtaining discriminatory results is difficult due to the difficulty in minimizing variations in vehicle separation and other parameters. This paper describes test strategies involving an automated test driver interacting with another moving vehicle. The autonomous vehicle controls its state (including position and speed) with respect to the target vehicle. Choreographed maneuvers such as chasing and overtaking can be performed with high accuracy and repeatability that even professional drivers have difficulty achieving. The system is also demonstrated to be usable in crash testing.

Steering activity in response to deviations from the intended straight-ahead vehicle path is an important factor of vehicle-driver on-center performance. Traditionally, setting the parameters of vehicle models for stability analysis does not involve vehicle properties that make the vehicle deviate from its intended path with no steering input. However, for closed loop simulations where driver steering activity is considered a fundamental human performance measure, the ability of the vehicle model to faithfully generate path deviations in response to external forces is required for steering performance measures fidelity. Path deviations cause the driver to make small steering angle corrections to keep the vehicle on its intended straight path. It is in fact a natural driving load that should be included in every closed loop simulation before considering any study involving driving loads and driver distractions.

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.

The tractor trailer models discussed in this paper were for a real-time hardware-in-the-loop (HIL) simulation to test heavy truck electronic stability control (ESC) systems [1]. The accuracy of the simulation results relies on the fidelity and accuracy of the vehicle parameters used. However in this case where hardware components are part of the simulation, their accuracy also affects the proper working of the simulation and ESC unit. Hence both the software and hardware components have to be validated. The validation process discussed in this paper is divided into two sections. The first section deals with the validation of the TruckSim vehicle model, where experimental data is compared with simulation results from TruckSim. Once the vehicle models are validated, they are incorporated in the HIL simulation and the second section discusses the validation of the whole HIL system with ESC.

Validation was performed on an existing heavy truck vehicle dynamics computer model with roll stability control (RSC). The first stage in this validation was to compare the response of the simulated tractor to that of the experimental tractor. By looking at the steady-state gains of the tractor, adjustments were made to the model to more closely match the experimental results. These adjustments included suspension and steering compliances, as well as auxiliary roll moment modifications. Once the validation of the truck tractor was completed for the current configuration, the existing 53-foot box trailer model was added to the vehicle model. The next stage in experimental validation for the current tractor-trailer model was to incorporate suspension compliances and modify the auxiliary roll stiffness to more closely model the experimental response of the vehicle. The final validation stage was to implement some minor modifications to the existing RSC model.

This paper documents the vehicle response of a tractor-semitrailer following a sudden air loss (Blowout) in a steer axle tire while traveling at highway speeds. The study seeks to compare full-scale test data to predicted response from detailed heavy truck computer vehicle dynamics simulation models. Full-scale testing of a tractor-semitrailer experiencing a sudden failure of a steer axle tire was conducted. Vehicle handling parameters were recorded by on-board computers leading up to and immediately following the sudden air loss. Inertial parameters (roll, yaw, pitch, and accelerations) were measured and recorded for the tractor and semitrailer, along with lateral and longitudinal speeds. Steering wheel angle was also recorded. These data are presented and also compared to the results of computer simulation models. The first simulation model, SImulation MOdel Non-linear (SIMON), is a vehicle dynamic simulation model within the Human Vehicle Environment (HVE) software environment.

This paper examines variability of two static rollover metrics, Static Stability Factor (SSF) and Tilt Table Ratio (TTR), due to vehicle loading and vehicle-to-vehicle variation. Variability due to loading was determined by measuring SSF and TTR for 14 vehicles/configurations at multiple loadings. Up to five loadings were used per vehicle/configuration tested. Vehicle-to-vehicle variability was studied by measuring SSF and TTR for ten unmodified vehicles of each of four make/models. Five baseline vehicles, as similar as was feasible, were tested. The other five test vehicles spanned the range of submodels and options available. In general, both SSF and TTR decreased as occupants were added to a vehicle. The change in SSF and TTR per occupant was fairly consistent, with changes in TTR being more consistent. Placing ballast on the floor of the cargo compartment had a mixed effect on SSF, raising it for some vehicles and lowering it for others.

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 paper describes the theory and design of an apparatus, the Suspension Parameter Measurement Device (SPMD), which has been developed to measure the displacements and forces which occur at the road wheels of a vehicle as the body moves, or as lateral and/or longitudinal forces are applied at the tire/road interface. Wheel movements resulting from the bounce, pitch, or roll motions of the vehicle body in the absence of lateral and longitudinal forces at the tire/road interface are the kinematic characteristics of the suspension. Wheel displacements caused by the application of forces in the plane of the road are defined as the compliance characteristics, while those resulting from motions of the steering wheel are the steering characteristics. The purpose of the SPMD is to measure all of these characteristics, thereby providing data for use in the simulation of the performance of cars and light trucks.

This paper describes the mechanical design of a Suspension Parameter Identification Device and Evaluation Rig (SPIDER) for wheeled military vehicles. This is a facility used to measure quasi-static suspension and steering system properties as well as tire vertical static stiffness. The machine operates by holding the vehicle body nominally fixed while hydraulic cylinders move an “axle frame” in bounce or roll under each axle being tested. The axle frame holds wheel pads (representing the ground plane) for each wheel. Specific design considerations are presented on the wheel pads and the measurement system used to measure wheel center motion. The constraints on the axle frames are in the form of a simple mechanism that allows roll and bounce motion while constraining all other motions. An overview of the design is presented along with typical results.

This paper describes a new laboratory test facility for measuring suspension parameters that affect rollover. The Side-Pull mechanism rolls the test vehicle through a cable attached rigidly at its center of gravity (CG). Changes in wheel camber and wheel steer angles are measured as a function of body roll angle. The roll test simulates a steady-state cornering. Thus, both compliance and kinematic forces are fed simultaneously to the vehicle as they would be applied in a real cornering situation. The lateral load transfer, and roll angle as a function of simulated lateral acceleration is determined. The Side-Pull Roll Measurement has advantages over the conventional roll tests where the rolling force couple is applied vertically. The Side-Pull mechanism rolls the vehicle in a unrestricted way with horizontal forces applied at the tire / pad contact and the CG location. Thus, the measurements take into account coupling of compliance with roll.

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.

In 2007, a software model of a Roll Stability Control (RSC) system was developed based on test data for a Volvo tractor at NHTSA's Vehicle Research and Test Center (VRTC). This model was designed to simulate the RSC performance of a commercially available Electronic Stability Control (ESC) system. The RSC model was developed in Simulink and integrated with the available braking model (TruckSim) for the truck. The Simulink models were run in parallel with the vehicle dynamics model of a truck in TruckSim. The complete vehicle model including the RSC system model is used to simulate the behavior of the actual truck and determine the capability of the RSC system in preventing rollovers under different conditions. Several simulations were performed to study the behavior of the model developed and to compare its performance with that of an actual test vehicle equipped with RSC.

Vehicle dynamics models employed in heavy truck simulation often treat the semitrailer as a torsionally rigid member, assuming zero deflection along its longitudinal axis as a moment is applied to its frame. Experimental testing, however, reveals that semitrailers do twist, sometimes enough to precipitate rollover when a rigid trailer may have remained upright. Improving the model by incorporating realistic trailer roll stiffness values can improve assessment of heavy truck dynamics, as well as an increased understanding of the effectiveness of stability control systems in limit handling maneuvers. Torsional stiffness measurements were conducted by the National Highway Traffic Safety Administration (NHTSA) for eight semitrailers of different types, including different length box vans, traditional and spread axle flat beds, and a tanker.

This paper summarizes the results of test maneuvers devised to measure on-road, untripped, rollover propensity. Complete findings from this research are contained in [1]. Twelve test vehicles, representing a wide range of vehicle types and classes were used. Three vehicles from each of four categories: passenger cars, light trucks, vans, and sport utility vehicles, were tested. The vehicles were tested with vehicle characterization and untripped rollover propensity maneuvers. The vehicle characterization maneuvers were designed to determine fundamental vehicle handling properties while the untripped rollover propensity maneuvers were designed to produce two-wheel lift for vehicles with relatively higher rollover propensity potential. The vehicle characterization maneuvers were Pulse Steer, Sinusoidal Sweep, Slowly Increasing Steer, and Slowly Increasing Speed. The rollover propensity maneuvers were J-Turn, J-Turn with Pulse Braking, Fishhook #1 and #2, and Resonant Steer.

In 2004, a model of a 6s6m ABS controller was developed in order to support NHTSA's efforts in the study of heavy truck braking performance. This model was developed using Simulink and interfaced with TruckSim, a vehicle dynamics software package, in order to create an accurate braking simulation of a 6×4 Peterbilt straight truck. For this study, the vehicle model braking dynamics were improved and the ABS controller model was refined. Also, the controller was made adaptable to ABS configurations other than 6s6m, such as 4s4m and 4s3m. Controller models were finally validated to experimental data from the Peterbilt truck, gathered at NHTSA's Vehicle Research and Test Center (VRTC).

This paper introduces a new nonlinear model for simulating the dynamics of pneumatic-over-mechanical commercial vehicle braking systems. The model employs an effective systems approach to accurately reproduce forcing functions experienced at the hubs of heavy commercial vehicles under braking. The model, which includes an on-off type ABS controller, was developed to accurately simulate the steer, drive, and trailer axle drum (or disc) brakes on modern heavy commercial vehicles. This model includes parameters for the pneumatic brake control and operating systems, a 4s/4m (four sensor, four modulator) ABS controller for the tractor, and a 2s/2m ABS controller for the trailer. The dynamics of the pneumatic control (treadle system) are also modeled. Finally, simulation results are compared to experimental data for a variety of conditions.

According to NHTSA's 2011 Traffic Safety Facts [1], passenger vehicle occupant fatalities continued the strong decline that has been occurring recently. In 2011, there were 21,253 passenger vehicles fatalities compared to 22,273 in 2010, and that was a 4.6% decrease. However; large-truck occupant fatalities increased from 530 in 2010 to 635 in 2011, which is a 20% increase. This was a second consecutive year in which large truck fatalities have increased (9% increase from 2009 to 2010). There was also a 15% increase in large truck occupant injuries from 2010. Moreover, the fatal crashes involving large trucks increased by 1.9%, in contrast to other-vehicle-occupant fatalities that declined by 3.6% from 2010. The 2010 accident statistics NHTSA's report reveals that large trucks have a fatal accident involvement rate of 1.22 vehicles per 100 million vehicle miles traveled compared to 1.53 for light trucks and 1.18 for passenger cars.

This paper presents a real-time steering system torque feedback model used in the National Advanced Driving Simulator (NADS). The vehicle model is based on real-time recursive multi-body dynamics augmented with vehicle subsystems models including tires, power train, brakes, aerodynamics and steering. The steering system feel is of paramount importance for the fidelity of the simulator. The driver has to feel the appropriate torque as he/she steers the vehicle. This paper presents a detailed mathematical model of the steering physics from low-speed stick-slip to high-speed states. On-center steering weave handling and aggressive lane change inputs are used to validate the basic mathematical predictions. This validation is objective and open loop, and was done using field experiments.

This paper presents an evaluation of a complete vehicle dynamics model for a 1998 Chevrolet Malibu to be used for the National Advanced Driving Simulator. Vehicle handling, braking and powertrain dynamics are evaluated and simulation results are compared with experimental field-testing. NADSdyna, the National Advanced Driving Simulator vehicle dynamics software, is used. The Malibu evaluation covers vehicle directional dynamics that include steady state, transient frequency response, and vehicle longitudinal dynamics composed of acceleration and braking. Also, analyses of the effects of modified tire parameters on vehicle dynamics response is performed. The effects of wind gusts generated by a tractor-trailer and a bus on the Malibu vehicle directional dynamics are analyzed. For the steering system feel, we compare the handwheel torque feedback with the measured data during both high-speed dynamics and in the very low speed tire stick-slip regime.