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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.

Battery packs used in automotive application experience high-power demands, fast charging, and varied operating conditions, resulting in temperature imbalances that hasten degradation, reduce cycle life, and pose safety risks. The development of proper simulation tools capable of capturing both the cell electrical and thermal response including, predicting the cell’s temperature rise and distribution, is critical to design efficient and reliable battery packs. This paper presents a co-simulation model framework capable of predicting voltage, 2-D heat generation and temperature distribution throughout a cell. To capture the terminal voltage and 2-D heat generation across the cell, the simulation framework employs a high-fidelity electrical model paired with a charge balance model based on the Poisson equation. The 2-D volumetric heat generation provided by the charge balance model is used to predict the temperature distribution across the cell surface using CFD software.

A new optical array imaging probe, called the 1D2D probe, has been developed by Science Engineering Associates, with features added to improve the real-time and post-analysis measurements of particle spectra, particularly in the Supercooled Large Droplet size range. The probe uses optical fibers and avalanche photodiodes to achieve a very high frequency response, and a Field-Programmable Gate Array that performs real-time particle rejection and processing of accepted particles with negligible inter-particle dead time. The probe records monochromatic two-dimensional images, while also recording the number of individual particle pixels at a second grey scale level. The probe implements flexible features to filter recording of highly out of focus particles to improve the accuracy of particle size determination, or to reject small particles to improve the statistics of measurements of larger particles.

1 Autonomous Driving Systems (ADS) are developing rapidly. As vehicle technology advances to SAE level 3 and above (L4, L5), there is a need to maximize and verify safety and operational benefits. As a result, maintenance of these ADS systems is essential which includes scheduled, condition-based, risk-based, and predictive maintenance. A lot of techniques and methods have been developed and are being used in the maintenance of conventional vehicles as well as other industries, but ADS is new technology and several of these maintenance types are still being developed as well as adapted for ADS. In this work, we are presenting a systematic literature review of the “State of the Art” knowledge for the maintenance of a fleet of ADS which includes fault diagnostics, prognostics, predictive maintenance, and preventive maintenance.

For realistic traffic modeling, real-world traffic calibration data is needed. These data include a representative road network, road users count by type, traffic lights information, infrastructure, etc. In most cases, this data is not readily available due to cost, time, and confidentiality constraints. Some open-source data are accessible and provide this information for specific geographical locations, however, it is often insufficient for realistic calibration. Moreover, the publicly available data may have errors, for example, the Open Street Maps (OSM) does not always correlate with physical roads. The scarcity, incompleteness, and inaccuracies of the data pose challenges to the realistic calibration of traffic models. Hence, in this study, we propose an approach based on spatial interpolation for addressing sparsity in vehicle count data that can augment existing data to make traffic model calibrations more accurate.

Accurately predicting the future behavior of the surrounding traffic, especially the velocity of the lead vehicle is important for optimizing the energy consumption and improve the safety of Connected and Automated Vehicles (CAVs). Several studies report methods to predict short-to-mid-length lead vehicle velocity using stochastic models or other data-driven techniques, which require availability of extensive data and/or Vehicle-to-Vehicle (V2V) communication. In the absence of connectivity, or in data-restricted cases, the prediction must rely only on the measured position and relative velocity of the lead vehicle at the current time. This paper proposes two velocity predictors to predict short-to-mid-length lead vehicle velocity. The first predictor is based on a Constant Acceleration (CA) with an augmented stop mode. The second one is based on a modified Enhanced Driver Model (EDM-LOS) with line-of-sight feature.

Testing any new safety technology of Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) requires simulation-based validation and verification. The specific scenarios used for testing, outline incidences of accidents or near-miss events. In order to simulate these scenarios, specific values for all the above parameters are required including the ego vehicle model. The ‘criticality’ of a scenario is defined in terms of the difficulty level of the safety maneuver. A scenario could be over-critical, critical, or under-critical. In over-critical scenarios, it is impossible to avoid a crash whereas, for under-critical scenarios, no action may be required to avoid a crash. The criticality of the scenario depends on various parameters e.g. speeds, distances, road/tire parameters, etc. In this paper, we propose a definition of criticality metric and identify the parameters such that a scenario becomes critical.

In the last decade, the use of Driver-in-the-Loop (DiL) simulators has significantly increased in research, product development, and motorsports. To be used as a verification tool in research, simulators must show a level of correlation with real-world driving for the chosen use case. This study aims to assess the validity of a low-cost, limited travel Vehicle Dynamics Driver-in-Loop (VDDiL) simulator by comparing on-road and simulated driving data using a statistical evaluation of longitudinal and lateral metrics. The process determines if the simulator is appropriate for verifying control strategies and optimization algorithms for longitudinal vehicle dynamics and evaluates consistency in the chosen metrics. A validation process explaining the experiments, choice of metrics, and analysis tools used to perform a validation study from the perspective of the longitudinal vehicle model is shown in this study.

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.

Due to the infeasibility of exhaustive on-road testing of Automated Vehicles (AVs) and vehicles with Advanced Driver Assistance Systems (ADAS), virtual methods for verification and validation of such vehicles have gained prominence. In order to incorporate the variability in the characteristics of test scenarios such as surrounding traffic, weather, obstacles, road network, infrastructure features, etc., as well as provide the option of varying the fidelities of subsystem models, this study discusses a modular software block-set for virtual testing of AV/ADAS controllers based on open source tools. The core concept is to co-simulate the traffic, vehicle dynamics, sensors, and the 3D scenes required for perception. This is achieved using SUMO (Simulation of Urban MObility, a microscopic road-network-based traffic generation tool) and Unreal Engine (for 3D traffic flow generation).

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.

Second-life batteries (SLBs, automotive batteries that have lost their usefulness for vehicular applications) can provide low-cost environment-friendly solutions for grid-connected systems. The estimation of the remaining useful life (RUL) of SLBs is a fundamental step for the development of appropriate business models. This paper aims at unveiling correlations between the SLB's power profile and aging performance by defining appropriate metrics. A widely accepted empirical degradation model, that can predict calendar and cycling aging, is considered for this study. Several grid-connected power profiles are analyzed, such as peak shaving for DC-fast charge stations and frequency regulation. The results of this analysis show a correlation between the SLB's replacement rate with the minimum daily SoC.

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.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

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.

Simulation-based design of connected and automated hybrid-electric vehicles is a challenging problem. The design space is large, the systems are complex, and the influence of connected and autonomous technology on the process is a new area of research. The Ohio State University EcoCAR Mobility Challenge team developed a comprehensive design and simulation approach as a solution. This paper covers the detailed simulation work conducted after initial design space reduction was performed to arrive at a P0-P4 hybrid vehicle with a gasoline engine. Two simulation environments were deployed in this strategy, each with unique advantages. The first was Autonomie, which is a commercial software tool that is well-validated through peer-reviewed studies. This allowed the team to evaluate a wide range of components in a robust simulation framework.

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.