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In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy.

Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model.

Due to the lack of biofidelity seen in GHBMC M50-O in rear-facing impact simulations involving interaction with the seat back in an OEM seat, it is important to explore how the boundary conditions might be affecting the biofidelity and potentially formulate methods to improve biofidelity of different occupant models in the future while also maintaining seat validity. This study investigated the influence of one such boundary condition, which is the seat back foam material properties, on the thorax and pelvis kinematics and injury outcomes of the GHBMC 50th M50-O model in a high-speed rear-facing frontal impact scenario, which involves severe occupant loading of the seat back. Two different seat back foam materials were used – a stiff foam with high densification and a soft foam with low densification. The peak magnitudes of the T-spine resultant accelerations of the GHBMC M50-O increased with the use of soft foam as compared to stiff foam.

Backlash is the movement between the gear teeth that allows them to mate without binding. Backlash can cause large torque fluctuations in vehicle powertrains when the input torque changes direction. These fluctuations cause a jerk and shuddering, which negatively affects drive quality. Input torque frequently changes direction in electric vehicles due to regenerative braking. Limiting zero crossings is an option for better drive quality; however, this leads to decreased vehicle efficiency. Because of this, modulating the torque through the backlash region is preferred, yet, if done poorly, it can result in sluggish torque response. This paper proposes a torque-shaping algorithm for an electric motor and gear/differential system to reduce backlash in electric vehicles. The control algorithm modulates the commanded torque’s rate of change based on the vehicle speed and zero-crossing torque.

The National Highway Traffic Safety Administration (NHTSA) has developed the Large Omnidirectional Child (LODC) Anthropomorphic Test Device (ATD) to improve the biofidelity of the currently available Hybrid III 10-year-old (HIII-10C) ATD. The improvements of the LODC over the HIII-10C include changes in sub-assemblies such as the head and neck, where the LODC head is a redesigned HIII-10C head with pediatric mass properties and the neck has a modified atlanto-occipital joint to replicate observations made from human specimens. The current study focuses on developing a dynamic, nonlinear finite element (FE) model of the LODC ATD head and neck complex. The FE mesh is generated using HyperMesh based on the three-dimensional CAD model. The material data, contact definitions and initial conditions are defined in LS-PrePost and converted to LS-Dyna solver input format. The initial and boundary conditions are defined to replicate the neck flexion experimental tests.

In the last decade, the accelerated advancements in manufacturing techniques and material science enabled the automotive industry to manufacture commercial vehicles at more affordable rates. This, however, brought about roadways having to accommodate an ever-increasing number of vehicles every day. However, some roadways, during specific hours of the day, had already been on the brink of reaching their capacity to withstand the number of vehicles travelling on them. Hence, overcrowded roadways create slow traffic, and sometimes, bottlenecks. In this paper, a Dynamic Speed Harmonization (DSH) algorithm that regulates the speed of a vehicle to prevent it from being affected by bottlenecks has been presented. First, co-simulations were run between MATLAB Simulink and CarSim to test different deceleration profiles.

Child occupants have not been studied in far-side impacts as thoroughly as frontal or near side crash modes. The objective is to determine whether the installation method of child restraint systems (CRS) affects far-side crash performance. Twenty far-side impact sled tests were conducted with rear-facing (RF) CRS, forward-facing (FF) CRS, high-back boosters, and belt only. Each was installed on second row captain’s chairs from a recent model year minivan. Common CRS installation errors were tested, including using the seat belt in Emergency Locking Mode (ELR) instead of Automatic Locking Mode (ALR), not attaching the top tether, and using both the lower anchors (LA) and seat belt together. Correct installations were also tested as a baseline comparison. Q3s and Hybrid III 6-year-old (6yo) anthropomorphic test devices (ATDs) were used. Lateral displacements of the CRS and head were examined as well as injury metrics in the head, spine, and torso.

Many vehicles allow consumers to adapt the vehicle environment to their families’ needs by folding or removing one or more rear row seats. It is currently unclear how different seat configurations affect child restraint systems (CRS) installed in adjacent seats. The objective is to quantify CRS performance in far-side impacts when the seating position adjacent to the CRS is in its normal upright position, folded in half, or removed. Twelve tests were conducted. Second row seats from a recent model year minivan were obtained, including full size captain’s chairs from the outboard positions and narrow seats from the center position. Rear-facing (RF) and forward-facing (FF) CRS were installed one at a time in either the outboard or center position. The seating position adjacent to the CRS was set in either the standard upright position, folded in half, or removed. Far-side impacts were conducted at 10° anterior of pure lateral at 24.8 ± 0.2 g. The Q3s ATD was used for all tests.

By utilizing the vehicle to infrastructure communication, the conventional Green Light Optimized Speed Advisory (GLOSA) applications give speed advisory range for drivers to travel to pass at the green light. However, these systems do not consider the traffic between the ego vehicle and the traffic light location, resulting in inaccurate speed advisories. Therefore, the driver needs to intuitively adjust the vehicle's speed to pass at the green light and avoid traffic in these scenarios. Furthermore, inaccurate speed advisories may result in unnecessary acceleration and deceleration, resulting in poor fuel efficiency and comfort. To address these shortcomings of conventional GLOSA, in this study, we proposed the utilization of collaborative perception messages shared by smart infrastructures to create an enhanced speed advisory for the connected vehicle drivers and automated vehicles.

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.

To improve the biofidelity of the currently available Hybrid III 10-year-old (HIII-10C) Anthropomorphic Test Device (ATD), the National Highway Traffic Safety Administration (NHTSA) has developed the Large Omnidirectional Child (LODC) ATD. The LODC head is a redesigned HIII-10C head with mass properties and modified skin material required to match pediatric biomechanical impact response targets from the literature. A dynamic, nonlinear finite element (FE) model of the LODC head has been developed using the mesh generating tool Hypermesh based on the three-dimensional CAD model. The material data, contact definitions, and initial conditions are defined in LS-PrePost and converted to LS-Dyna solver input format. The aluminum head skull is stiff relative to head flesh material and was thus modeled as a rigid material. For the actual LODC, the head flesh is form fit onto the skull and held in place through contact friction.

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.

This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping.

With the current drive of automotive and technology companies towards producing vehicles with higher levels of autonomy, it is inevitable that there will be an increasing number of SAE level L4-L5 autonomous vehicles (AVs) on roadways in the near future. Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways. The roadways used in these simulations were replicas of real roadways in and around Columbus, Ohio, including an AV shuttle routes in operation.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

This paper and the associated lecture present an overview of technology trends and of market and business opportunities created by technology, as well as of the challenges posed by environmental and economic considerations. Commercial vehicles are one of the engines of our economy. Moving goods and people efficiently and economically is a key to continued industrial development and to strong employment. Trucks are responsible for nearly 70% of the movement of goods in the USA (by value) and represent approximately 300 billion of the 3.21 trillion annual vehicle miles travelled by all vehicles in the USA while public transit enables mobility and access to jobs for millions of people, with over 10 billion trips annually in the USA creating and sustaining employment opportunities.

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.

The rise of automation in the automotive industry has ensured significant progress in vehicle safety and infrastructure. During the transition to full autonomy, the driver is often the redundancy and safety feature in the event of a hazard or automation error. Understanding driver behavior in the transition from non-driver to driver is important for safety. Proper handling of transitions will be more critical as these events become less common and users trust automated driving systems. This research investigates the case of SAE level-3 automated driving systems, where the driver need not constantly pay attention but is responsible for reaction during hazards. Findings include quantitative and qualitative assessment of various warning modes for a distracted driver responding to an automated driving failure situation. Driver response time and behavior for these events are compared to instances with minimal warning systems.

With the advancement of the autonomous vehicle development, the different possibilities of improving fuel economy have increased significantly by changing the driver or powertrain response under different traffic conditions. Development of new fuel-efficient driving strategies requires extensive experiments and simulations in traffic. In this paper, a fuel efficiency simulator environment with existing simulator software such as Simulink, Vissim, Sumo, and CarSim is developed in order to reduce the overall effort required for developing new fuel-efficient algorithms. The simulation environment is created by combining a mid-sized sedan MATLAB-Simulink powertrain model with a realistic microscopic traffic simulation program. To simulate the traffic realistically, real roads from urban and highway sections are modeled in the simulator with different traffic densities.

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.