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A framework is proposed for model-based misfire detection in gasoline engines with dynamic skip fire by employing a novel control oriented engine model. The model-based techniques form compact description of plant behavior and have a number of well known benefits. The performance requirements and environment legislation resulted in a rigorous research on misfire detection due to which an extensive literature can be found for the problem of misfire detection in all-cylinder firing gasoline engines. Since there is no fix cylinder activation/de-activation sequence in dynamic skip fire engines. So, the problem of misfire detection in dynamic skip fire engines departs from its trivial nature. In the proposed framework, ‘cylinder skip sequence’ is also fed to the engine model along-with conventional engine inputs. The First Principle based Engine Model constructs the crankshaft angular speed fluctuation pattern for a given cylinder skip sequence.

As the technology in autonomous vehicle and smart city infrastructure is developing fast, the idea of smart city and automated driving has become a present and near future reality. Both Highway Chauffeur and low speed shuttle applications are tested recently in different research to test the feasibility of autonomous vehicles and automated driving. Based on examples available in the literature and the past experience of the authors, this paper proposes the use of a unified computing, sensing, communication and actuation architecture for connected and automated driving. It is postulated that this unified architecture will also lead to a scalable and replicable approach. Two vehicles representing a passenger car and a small electric shuttle for smart mobility in a smart city are chosen as the two examples for demonstrating scalability and replicability.

This research leverages publicly available crash data to construct safety-critical scenarios focusing primarily on Level 3 Automated Driving Systems (ADS) safety assessment under highway driving conditions. NHTSA’s Crashworthiness Data System (CDS) has a rich dataset of representative crashes sampled from numerous Primary Sampling Units (PSUs) across the country. Each of these datasets includes the storyline, road geometry information, detailed description of actors involved in the crash, weather information, scene diagrams, crash images, and a myriad of other crash-specific details. The methodology adopted aims to generate critical scenarios from real-world driving to complement the existent regulatory tests for the validation of L3 ADS. For this work, a four-step approach was adopted to extract safety-critical scenarios from crash data.

Photogrammetry is widely used in the accident reconstruction community to extract three-dimensional information from photographs. This article extends a prior study conducted by the authors, whereby model accuracy was assessed for a technique that exploited vehicle edges and epipolar line projections to construct 3D vehicle models, by examining 3D roadway and site features. To do so, artificial images were generated using an ideal computer-generated camera within a computer-assisted drawing environment to allow for a known reference model to compare with results produced using photogrammetry. A systematic study was undertaken by modeling the curvature, elevation, and super-elevation of a roadway and associated markings, sidewalks, and buildings, either by relying on discrete points or utilizing epipolar lines. The models were assessed for accuracy, and the sensitivity of the accuracy to camera elevation was considered.

Photogrammetry is widely used in the automotive and accident reconstruction communities to extract three-dimensional information from photographs. Prior studies in the literature have demonstrated the accuracy of such methods when photographs contain easily-identifiable, distinct points; however, it is often desirable to determine measurements for locations where a seam, edge, or contour line is available. To exploit such details, an analyst can control the direction that the epipolar line is projected onto the camera plane by strategic selection of photographs. This process constrains the search for the corresponding 3D point to a straight line that can be projected perpendicular to the seam, edge, or contour line. Thus, the goal of this study was to evaluate the modeling accuracy for cases in which an analyst uses epipolar lines in a workflow.

This research focuses on the development and performance of analytical models to simulate a tractor-semitrailer in straight-ahead braking. The simulations were modified and tuned to simulate full-treadle braking with all brakes functioning correctly, as well as the behavior of the tractor-semitrailer rig under full braking with selected brakes disabled. The models were constructed in TruckSim and based on a tractor-semitrailer used in dry braking performance testing. The full-scale vehicle braking research was designed to define limits for engineering estimates on stopping distance when Class 8 air-braked vehicles experience partial degradation of the foundation brake system. In the full scale testing, stops were conducted from 30 mph and 60 mph, with the combination loaded to 80,000 lbs (gross combined weight or GCW), half payload, and with the tractor-semitrailer unladen (lightly loaded vehicle weight, or LLVW).

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.

Topology optimization is a branch of structural optimization which solves an optimal material distribution problem. The resulting structural topology, for a given set of boundary conditions and constraints, has an optimal performance (e.g. minimum compliance). Conventional 3D topology optimization algorithms achieve quality optimized results; however, it is an extremely computationally intensive task which is, in general, impractical and computationally unachievable for real-world structural optimal design processes. Therefore, the current development of rapid topology optimization technology is experiencing a major drawback. To address the issues, a new approach is presented to utilize the powerful abilities of large deep learning models to replicate this design process for 3D structures. Adversarial models, primarily Wasserstein Generative Adversarial Networks (WGAN), are constructed which consist of 2 deep convolutional neural networks (CNN) namely, a discriminator and a generator.

Advanced Driver Assistance Systems (ADAS) are designed and calibrated rigorously to provide them with the robustness against highly uncertain environments that they usually operate in. Typical calibration procedures for such systems rely extensively on track (controlled environment) testing, which is time-consuming, expensive, and sometimes cannot cover all the critical test scenarios that could be encountered by ADAS in the real world. Therefore, virtual (simulation-based) testing and validation has been gaining more prominence and emphasis for ensuring high coverage along with easier scalability and usage. This paper attempts to provide an alternative approach for calibrating ADAS in the controller validation phase by the aid of simulated test case scenarios. The study executes characterization of the uncertainty in the position and heading of the ego and the obstacle vehicles.

Thoracic injury criteria have been previously developed to predict thoracic injury for vehicle occupants as a function of biomechanical response. Historically, biomechanical testing of post-mortem human surrogates (PMHS) for injury criteria development has primarily been focused on mid-sized males. Response targets and injury criteria for other demographics, including small females, have been determined by scaling values from mid-sized males. The objective of this study was to explore the applicability of scaled injury criteria to their representative population. Two PMHS were subjected to a side-impact loading condition which replicates a near-side, MDB-to-vehicle impact for the driver. This was accomplished using the Advanced Side Impact System, or ASIS, on a HYGE sled. The sled acceleration matched the acceleration profile of an impacted vehicle, while the four pneumatic cylinders of the ASIS produced realistic door intrusion.

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.

The design and development of new biologically inspired technologies based on intelligent materials that are capable of sensing the levels of target biomolecules and, if needed, trigger appropriate countermeasures to regulate biological processes and rhythms of the astronauts is being undertaken in our laboratories. This is accomplished by coupling biologically inspired sensors that monitor the levels of the target biomolecules with intelligent polymeric materials that can regulate the release of a countermeasure. The technology developed here integrates sensors and artificial muscle material into a self-regulating device that can perform with minimal crew intervention. Further, it takes advantage of microfabrication technology to construct lightweight and robust responsive delivery systems. These “intelligent” devices address the need for the control and regulation of biological processes and rhythms under spaceflight conditions.

Side impacts are disproportionately injurious for children compared to other crash directions. Far side impacts allow for substantial translation and rotation of child restraint systems (CRS) because the CRS does not typically interact with any adjacent structures. The goal of this study is to determine whether minor installation incompatibilities between CRS and vehicle seats cause safety issues in far side crashes. Four non-ideal CRS installation conditions were compared against control conditions having good fit. Two repetitions of each condition were run. The conditions tested were: 1) rear-facing (RF) CRS installed with a pool noodle to create proper recline angle, 2) RF CRS with narrow base, 3) forward-facing (FF) CRS with gap behind back near seat bight (i.e., vehicle seat angle too acute for CRS), 4) FF CRS with gap behind back near top of CRS (i.e., vehicle seat angle too obtuse for CRS). Second row captain’s chairs were set up at 10° anterior of lateral.

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.

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.

RESULTS of tests performed on a modified type F-4 CFR engine show that precombustion reactions in both the fired and motored engine gave the same carbonyl products. The maximum specific yields of these carbonyls were similar for a given fuel compressed with comparable pressure-time-temperature histories in both motored- and fired-engine tests. As the motored engine seems to duplicate precombustion reactions occurring in a fired engine under normal operating conditions, the authors of this paper conclude that the motored engine, offering ease of control and sampling, is a convenient and valid tool for combustion research.

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.

Connected vehicle (CV) technology is among the most heavily researched areas in both the academia and industry. The vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to pedestrian (V2P) communication capabilities enable critical situational awareness. In some cases, these vehicle communication safety capabilities can overcome the shortcomings of other sensor safety capabilities because of external conditions such as 'No Line of Sight' (NLOS) or very harsh weather conditions. Connected vehicles will help cities and states reduce traffic congestion, improve fuel efficiency and improve the safety of the vehicles and pedestrians. On the road, cars will be able to communicate with one another, automatically transmitting data such as speed, position, and direction, and send alerts to each other if a crash seems imminent. The main focus of this paper is the implementation of Cooperative Collision Avoidance (CCA) for connected vehicles.

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.

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.