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The Large Omnidirectional Child (LODC) developed by the National Highway Traffic Safety Administration (NHTSA) has an improved biofidelity over the currently available Hybrid III 10-year-old (HIII-10C) Anthropomorphic Test Device (ATD). The LODC design incorporates enhancements to many body region subassemblies, including a redesigned HIII-10C head with pediatric mass properties, and the neck, which produces head lag with Z-axis rotation at the atlanto-occipital joint, replicating the observations made from human specimens. The LODC also features a flexible thoracic spine, a multi-point thoracic deflection measurement system, skeletal anthropometry that simulates a child's sitting posture, and an abdomen that can measure belt loading directly. This study presents the development and validation of a dynamic nonlinear finite element model of the complete LODC dummy. Based on the three-dimensional CAD model, Hypermesh was used to generate a mesh of the finite element (FE) LODC model.

Deployment of autonomous vehicles requires an extensive evaluation of developed control, perception, and localization algorithms. Therefore, increasing the implemented SAE level of autonomy in road vehicles requires extensive simulations and verifications in a realistic simulation environment before proving ground and public road testing. The level of detail in the simulation environment helps ensure the safety of a real-world implementation and reduces algorithm development cost by allowing developers to complete most of the validation in the simulation environment. Considering sensors like camera, LiDAR, radar, and V2X used in autonomous vehicles, it is essential to create a simulation environment that can provide these sensor simulations as realistically as possible.

With the advent of increasing autonomous vehicles on public roads, the safety of vulnerable road users such as pedestrians, cyclists, etc. has never been more important. These especially include Blind or Visually Impaired (BVI) pedestrians who face difficulty in making confident decisions in road crossings without the help of accessible pedestrian signals (APS). This paper addresses some of the safety measures that can be taken to improve and assess the safety of BVI pedestrians in a controlled environment like a BVI school campus where autonomous vehicles are operated. The majority of research on autonomous vehicle safety does not consider the edge cases of encounters with BVI pedestrians. Based on this motivation, requirements and characteristics of Non-BVI and BVI pedestrians have been stated along with the motion models used to predict their future movements. Existing tools based on Bayesian multi-model filters were used for pedestrian tracking and motion predictions.

Automated driving systems (ADS) are one of the key modern technologies that are changing the way we perceive mobility and transportation. In addition to providing significant access to mobility, they can also be useful in decreasing the number of road accidents. For these benefits to be realized, candidate ADS need to be proven as safe, robust, and reliable; both by design and in the performance of navigating their operational design domain (ODD). This paper proposes a multi-pronged approach to evaluate the safety performance of a hypothetical candidate system. Safety performance is assessed through using a set of test cases/scenarios that provide substantial coverage of those potentially encountered in an ODD. This systematic process is used to create a library of scenarios, specific to a defined domain. Beginning with a system-specific ODD definition, a set of core competencies are identified.

With the enhancements in vehicle electrification and autonomous vehicles, Traffic systems are also being improved at an accelerated rate to aid the development of improving fuel economy standards. For this to be possible, it is essential that traffic can be accurately modeled and predicted. The existing toolsets are proprietary and expensive and traffic modeling is not a trivial task due to its dependence on various factors such as place, time, and weather. To address these issues, an entirely open-source Software-In-Loop (SIL) fleet-focused traffic modeling toolset has been developed with the ability to take environmental factors with powertrain-in-the-loop into account leveraging Simulation of Urban Mobility (SUMO) and python. The proposed SIL toolset encompasses the creation of a microscopic traffic distribution which accounts for the usual traffic trends of a typical day.

Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting.

The Elderly Female Dummy (EFD) is an omni-directional ATD developed to represent a vulnerable population. The EFD it is able to be 3D printed and quickly altered to meet design requirements. A recent side impact sled test series suggested that small, elderly females may be at risk of thoracic injuries in side impact crashes due to combined loading from the belt pre-tensioner and side airbag. The EFD was altered to add four IR-TRACCs to the thoracic region to allow both x-axis and y-axis displacement to be evaluated in a similar test. While the IR-TRACCs did record the displacement due to combined loading, the rate of displacement and timing of the peak displacements did not match external chestband outputs. The next step for the EFD is to revise the locations of IRTRACCs in the thorax and begin component testing in lateral and frontal directions to improve thoracic biofidelity.

With the emergence of Driving Automation Systems (SAE levels 1-5), the necessity arises for methods of evaluating these systems. However, these systems are much more challenging to evaluate than traditional safety features (SAE level 0). This is because an understanding of the Driving Automation system’s response in all possible scenarios is desired, but prohibitive to comprehensively test. Hence, this paper attempts to evaluate one such system, by modeling its behavior. The model generated parameters not only allow for objective comparison between vehicles, but also provide a more complete understanding of the system. The model can also be used to extrapolate results by simulating other scenarios without the need for conducting more tests. In this paper, low speed automated driving (also known as Traffic Jam Assist (TJA)) is studied. This study focused on the longitudinal behavior of automated vehicles while following a lead vehicle (LV) in traffic jam scenarios.

It has been discussed in numerous prior studies that in-neutral coasting, or sailing, can accomplish considerable amount of fuel saving when properly used. The driving maneuver basically makes the vehicle sail in neutral gear when propulsion is unnecessary. By disengaging a clutch or shifting the gear to neutral, the vehicle may better utilize its kinetic energy by avoiding dragging from the engine side. This strategy has been carried over to series production recently in some of the vehicles on the market and has become one of the eco-mode features available in current vehicles. However, the duration of coasting must be long enough to attain more fuel economy benefit than Deceleration Fuel Cut-Off (DFCO) - which exists in all current vehicle powertrain controllers - can bring. Also, the transients during shifting back to drive gear can result in a drivability concern.

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This paper concentrates on low speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of campus as an initial autonomous vehicle (AV) pilot test route for the deployment of low speed autonomous shuttles. This paper presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment.

An important part of automotive driving assistance systems and autonomous vehicles is speed optimization and traffic flow adaptation. Vehicle sensors and wireless communication with surrounding vehicles and road infrastructure allow for predictive control strategies taking near-future road and traffic information into consideration to improve fuel economy. For the development of autonomous vehicle speed control algorithms, it is imperative that the controller can be evaluated under different realistic driving and traffic conditions. Evaluation in real-life traffic situations is difficult and experimental methods are necessary where similar driving conditions can be reproduced to compare different control strategies. A traditional approach for evaluating vehicle performance, for example fuel consumption, is to use predefined driving cycles including a speed profile the vehicle should follow.

The objective of this study is to present a quantitative comparison of the biofidelity of the THOR and Hybrid III 50th percentile male ATDs. Quantitative biofidelity was assessed using NHTSA’s Biofidelity Ranking System in a total of 21 test conditions, including impacts to the head, face, neck, upper thorax, lower oblique thorax, upper abdomen, lower abdomen, femur, knee, lower leg, and whole-body sled tests to evaluate upper body kinematics and thoracic response under frontal and frontal oblique restraint loading. Biofidelity Ranking System scores for THOR were better (lower) than Hybrid III in 5 of 7 body regions for internal biofidelity and 6 of 7 body regions for external biofidelity. Nomenclature is presented to categorize the quantitative results, which show overall good internal and external biofidelity of the THOR compared to the good (internal) and marginal (external) biofidelity of the Hybrid III.

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.

Rotational motion of the head as a mechanism for brain injury was proposed back in the 1940s. Since then a multitude of research studies by various institutions were conducted to confirm/reject this hypothesis. Most of the studies were conducted on animals and concluded that rotational kinematics experienced by the animal's head may cause axonal deformations large enough to induce their functional deficit. Other studies utilized physical and mathematical models of human and animal heads to derive brain injury criteria based on deformation/pressure histories computed from their models.

Anthropomorphic test devices (ATDs) should accurately depict head kinematics in crash tests, and thoracic spine properties have been demonstrated to affect those kinematics. To investigate the relationships between thoracic spine system dynamics and upper thoracic kinematics in crash-level scenarios, three adult post-mortem human subjects (PMHS) were tested in both Isolated Segment Manipulation (ISM) and sled configurations. In frontal sled tests, the T6-T8 vertebrae of the PMHS were coupled through a novel fixation technique to a rigid seat to directly measure thoracic spine loading. Mid-thoracic spine and belt loads along with head, spine, and pectoral girdle (PG) displacements were measured in 12 sled tests conducted with the three PMHS (3-pt lap-shoulder belted/unbelted at velocities from 3.8 - 7.0 m/s applied directly through T6-T8).

The objectives of this study were to obtain biomechanical responses of post mortem human subjects (PMHS) by subjecting them to two moderate-speed rear impact sled test conditions (8.5g, 17 km/h; 10.5g, 24 km/h) while positioned in an experimental seat system, and to create biomechanical targets for internal and external biofidelity evaluation of rear impact ATDs. The experimental seat was designed to measure external loads on the head restraint (4 load cells), seat back (6 load cells), and seat pan (4 load cells) such that subject dynamic interaction with the seat could be evaluated. This seat system was capable of simulating the dynamic characteristics of modern vehicle seat backs by considering the moment-rotation properties of a typical passenger vehicle, thus providing a more realistic test environment than using a rigid seat with a non-rotating seat back as done in previous studies.

In ISO Technical Report 9790 (1999) normalized lateral and oblique thoracic force-time responses of PMHS subjected to blunt pendulum impacts at 4.3 m/s were deemed sufficiently similar to be grouped together in a single biomechanical response corridor. Shaw et al., (2006) presented results of paired oblique and lateral thoracic pneumatic ram impact tests to opposite sides of seven PMHS at sub-injurious speed (2.5 m/s). Normalized responses showed that oblique impacts resulted in more deflection and less force, whereas lateral impacts resulted in less deflection and more force. This study presents results of oblique and lateral thoracic impacts to PMHS at higher speeds (4.5 and 5.5 m/s) to assess whether lateral relative to oblique responses are different as observed by Shaw et al., or similar as observed by ISO.

This study evaluated the response of restrained post-mortem human subjects (PMHS) in 40 km/h frontal sled tests. Eight male PMHS were restrained on a rigid planar seat by a custom 3-point shoulder and lap belt. A video motion tracking system measured three-dimensional trajectories of multiple skeletal sites on the torso allowing quantification of ribcage deformation. Anterior and superior displacement of the lower ribcage may have contributed to sternal fractures occurring early in the event, at displacement levels below those typically considered injurious, suggesting that fracture risk is not fully described by traditional definitions of chest deformation. The methodology presented here produced novel kinematic data that will be useful in developing biofidelic human models.

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.

Markings or observable anomalies on vehicle seat belt restraint systems can be classified into two categories: (1) Those caused by collision forces, or “loading marks” and (2) those created by noncollision situations, or “normal usage marks” [1]. A survey was conducted of both crash tested and non-crash tested vehicles in order to collect data on both categories of markings. This paper examines and analyzes the markings caused by both collision and noncollision load scenarios in order to illustrate and evaluate their unique differences as well as provide a general pattern of severity relative to different loading conditions.