Search Results

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.

Testing vision-based advanced driver assistance systems (ADAS) in a Camera-in-the-Loop (CiL) bench setup, where external visual inputs are used to stimulate the system, provides an opportunity to experiment with a wide variety of test scenarios, different types of vehicle actors, vulnerable road users, and weather conditions that may be difficult to replicate in the real world. In addition, once the CiL bench is setup and operating, experiments can be performed in less time when compared to track testing alternatives. In order to better quantify normal operating zones, track testing results were used to identify behavior corridors via a statistical methodology. After determining normal operational variability via track testing of baseline stationary surrogate vehicle and pedestrian scenarios, these operating zones were applied to screen-based testing in a CiL test setup to determine particularly challenging scenarios which might benefit from replication in a track testing environment.

In order to validate the operation of advanced driver assistance systems (ADAS), tests must be performed that assess the performance of the system in response to different scenarios. Some of these systems are designed for crash-imminent situations, and safely testing them requires large stretches of controlled pavement, expensive surrogate targets, and a fully functional vehicle. As a possible more-manageable alternative to testing the full vehicle in these situations, this study sought to explore whether these systems could be isolated, and tests could be performed on a bench via a hardware-in-the-loop methodology. For camera systems, these benches are called Camera-in-the-Loop (CiL) systems and involve presenting visual stimuli to the device via an external input.

Advanced Driver Assistance System (ADAS) has become a common standard feature assisting greater safety and fuel efficiency in the latest automobiles. Yet some ADAS systems fail to improve driving comfort for vehicle occupants who expect human-like driving. One of the more difficult situations in ADAS-assisted driving involves instances with cut-in vehicles. In vehicle control, determining the moment at which the system recognizes a cut-in vehicle as an active target is a challenging task. A well-designed comprehensive threat assessment developed for cut-in vehicle driving scenarios should eliminate abrupt and excessive deceleration of the vehicle and produce a smooth and safe driving experience. This paper proposes a novel methodology for threat assessment for driving instances involving a cut-in vehicle. The methodology takes into consideration kinematics, vehicle dynamics, vehicle stability, road condition, and driving comfort.

Intersections have a high risk of vehicle-to-vehicle conflicts because of the overlapping traffic flow from multiple roads. To understand the factors contributing to the crashes, this study examines the common characteristics in intersection-related crash and near- crash events, such as the existence of traffic control devices, the driver at fault, and occurrence of visual obstructions. The descriptive data of the crash and near-crash events recorded in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) database is used in categorization and statistical analysis in this study. First, the events are divided into seven categories based on trajectories of the conflicting vehicles. The categorization provides the basis for in-depth analysis of crash-contributing factors in specific confliction patterns. Subsequently, descriptive statistics are used to portray each of the categories.

A single-vehicle crash involving an SUV led to the study of the failure of the anti-sway bar linkage and tire pressure and their relative effects on the handling characteristics of the vehicle. The SUV, having been involved in a rollover, was found with the anti-sway bar drop link disconnected from the suspension lower A-arm assembly. Also, after the crash, the tire pressure in the front tires on the subject vehicle was measured to be above the value specified by the SUV manufacturer; however, the pressure for one of the rear tires was measured to be roughly half of the SUV manufacturer’s recommended pressure. The other rear tire was deflated. The testing described herein addresses the question of what effects the anti-sway bar drop link disconnection or reduced rear axle tire pressure would have on the SUV’s pre-accident handling and driveability.

A machine has been developed to measure the complete inertia matrix; mass, center of gravity (CG) location, and all moments and products of inertia. Among other things these quantities are useful in studying engine vibrations, calculation of the torque roll axis, and in the placement of engine mounts. While the machine was developed primarily for engines it can be used for other objects of similar size and weight, and even smaller objects such as tires and wheels/rims. A key feature of the device is that the object, once placed on the test table, is never reoriented during the test cycle. This reduces the testing time to an hour or less, with the setup time being a few minutes to a few hours depending on the complexity of the shape of the object. Other inertia test methods can require up to five reorientations, separate CG measurement, and up to several days for a complete test.

Evaluating the safety of Autonomous Vehicles (AV) is a challenging problem, especially in traffic conditions involving dynamic interactions. A thorough evaluation of the vehicle’s decisions at all possible critical scenarios is necessary for estimating and validating its safety. However, predicting the response of the vehicle to dynamic traffic conditions can be the first step in the complex problem of understanding vehicle’s behavior. This predicted response of the vehicle can be used in validating vehicle’s safety. In this paper, models based on Machine Learning were explored for predicting and classifying driver’s response. The Naturalistic Driving Study dataset (NDS), which is part of the Strategic Highway Research Program-2 (SHRP2) was used for training and validating these Machine Learning models.

This paper presents the development of a CarSim model of an All-Terrain Vehicle (ATV) that can be used to predict the handling and stability characteristic of the vehicle. The inertia and suspension characteristics of a subject ATV are measured and a model of the ATV is built in CarSim based on the measurements. A simplified suspension model is developed to convert the suspension compliance measurements into parameters suitable to a CarSim model. Procedures used to apply vehicle mass, inertia and suspension kinematics data in CarSim are also shown. The model is evaluated using predictions of vehicle response during a constant radius circle test. The simulation results of the maneuver are compared with the field test results shown in a recent CPSC report on ATV’s. Similar cornering characteristics are found in both results. Modifications are made to the model to study how changes to the ATV affect performance.

The rapid development of driver assistance systems, such as lane-departure warning (LDW) and lane-keeping support (LKS), along with widely publicized reports of automated vehicle testing, have created the expectation for an increasing amount of vehicle automation in the near future. As these systems are being phased in, the coexistence of automated vehicles and human-driven vehicles on roadways will be inevitable and necessary. In order to develop automated vehicles that integrate well with those that are operated in traditional ways, an appropriate understanding of human driver behavior in normal traffic situations would be beneficial. Unlike many research studies that have focused on collision-avoidance maneuvering, this paper analyzes the behavior of human drivers in response to cut-in vehicles moving at similar speeds. Both automated and human-driven vehicles are likely to encounter this scenario in daily highway driving.

This study was performed to showcase the possible applications of the Hardware-in-the-loop (HIL) simulation environment developed by the National Highway Traffic Safety Administration (NHTSA), to test heavy truck crash avoidance safety systems. In this study, the HIL simulation environment was used to recreate a simulation of an actual accident scenario involving a single tractor semi-trailer combination. The scenario was then simulated with and without an antilock brake system (ABS) and electronic stability control (ESC) system to investigate the crash avoidance potential afforded by the tractor equipped with the safety systems. The crash scenario was interpreted as a path-following problem, and three possible driver intended paths were developed from the accident scene data.

Typically, when one thinks of advanced driver assistance systems (ADAS), systems such as Forward Collision Warning (FCW) and Collision Imminent Braking (CIB) come to mind. In these systems driver assistance is provided based on knowledge about the subject vehicle and surrounding objects. A new class of these systems is being implemented. These systems not only use information on the surrounding objects but also use information on the driver's response to an event, to determine if intervention is necessary. As a result of this trend, an advanced level of understanding of driver braking behavior is necessary. This paper presents an alternate method of analyzing driver braking behavior. This method uses a frequency content based approach to study driver braking and allows for the extraction of significantly more data from driver profiles than traditionally would have been done.

Dynamic Brake Support (DBS) is a safety system that has been applied to various passenger cars and has been shown to be effective at assisting drivers in avoiding or mitigating rear-end collisions. The objective of a DBS system is to ensure that the brake system is applied quickly and at sufficient pressure when a driver responds to a collision imminent situation. DBS is capable of improving braking response due to a passenger car driver's tendency to utilize multi-stage braking. Interest is developing in using DBS on commercial vehicles. In order to evaluate the possible improvement in safety that could be realized through the use of DBS, driver braking behavior must first be analyzed to confirm that improvement is possible and necessary. To determine if this is the case, a study of the response of truck drivers' braking behavior in collision imminent situations is conducted. This paper presents the method of evaluation and results.

This research was to model a 6×4 tractor-trailer rig using TruckSim and simulate severe braking maneuvers with hardware in the loop and software in the loop simulations. For the hardware in the loop simulation (HIL), the tractor model was integrated with a 4s4m anti-lock braking system (ABS) and straight line braking tests were conducted. In developing the model, over 100 vehicle parameters were acquired from a real production tractor and entered into TruckSim. For the HIL simulation, the hardware consisted of a 4s4m ABS braking system with six brake chambers, four modulators, a treadle and an electronic control unit (ECU). A dSPACE simulator was used as the “interface” between the TruckSim computer model and the hardware.

The (Vehicle Inertia Parameter Evaluation Rig) VIPER II is a full vehicle mass and inertia parameter measurement machine. The VIPER II expands upon the capabilities of its predecessor and is capable of measuring vehicles with a mass of up to 45,360 kg (100,000 lb), an increase in capacity of 18,100 kg (40,000 lb). The VIPER II also exceeds its predecessor in both the length and width of vehicles it can measure. The VIPER II's maximum vehicle width is 381 cm (150 in) an increase of 76 cm (30 in) and maximum distance from the vehicle CG to the outer most axle is 648 cm (255 in) an additional 152 cm (60 in) The VIPER II is capable of performing measurements including vehicle CG height, pitch, roll, and yaw moments of inertia and the roll/yaw cross product of inertia. While being able to measure both heavier and larger vehicles, the VIPER II is designed to maintain a maximum error of 3% for all inertia measurements and 1% for CG height.

A Software-in-the-Loop (SIL) simulation is presented here wherein control algorithms for the Anti-lock Braking System (ABS) and Roll Stability Control (RSC) system were developed in Simulink. Vehicle dynamics models of a 6×4 cab-over tractor and two trailer combinations were developed in TruckSim and were used for control system design. Model validation was performed by doing various dynamic maneuvers like J-Turn, double lane change, decreasing radius curve, high dynamic steer input and constant radius test with increasing speed and comparing the vehicle responses obtained from TruckSim against field test data. A commercial ESC ECU contains two modules: Roll Stability Control (RSC) and Yaw Stability Control (YSC). In this research, only the RSC has been modeled. The ABS system was developed based on the results obtained from a HIL setup that was developed as a part of this research.

This paper presents a feedback linearization control technique as applied to a Roll Simulator. The purpose of the Roll Simulator is to reproduce in-field rollovers of ROVs and study occupant kinematics in a laboratory setting. For a system with known parameters, non-linear dynamics and trajectories, the feedback linearization algorithm cancels out the non-linearities such that the closed-loop dynamics behave in a linear fashion. The control inputs are computed values that are needed to attain certain desired motions. The computed values are a form of inverse dynamics or feed-forward calculation. With increasing system eigenvalue, the controller exhibits greater response time. This, however, puts a greater demand on the translational actuator. The controller also demonstrates that it is able to compensate for and reject a disturbance in force level.

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.

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.

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.