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Journal Article

Toward Improving Vehicle Fuel Economy with ADAS

2018-10-29
Abstract Modern vehicles have incorporated numerous safety-focused advanced driver-assistance systems (ADAS) in the last decade including smart cruise control and object avoidance. In this article, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy (FE). We combine ADAS sensor data with a previously developed prediction model, dynamic programming (DP) optimal energy management control, and a validated model of a 2010 Toyota Prius to explore FE. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results.
Journal Article

Theory of Collision Avoidance Capability in Automated Driving Technologies

2018-10-29
Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.
Journal Article

Localization and Perception for Control and Decision-Making of a Low-Speed Autonomous Shuttle in a Campus Pilot Deployment

2018-11-12
Abstract Future SAE Level 4 and Level 5 autonomous vehicles (AV) 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 article 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 the campus as an initial AV pilot test route for the deployment of low-speed autonomous shuttles. This article presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment.
Journal Article

Hardware-in-the-Loop (HIL) Implementation and Validation of SAE Level 2 Automated Vehicle with Subsystem Fault Tolerant Fallback Performance for Takeover Scenarios

2018-07-27
Abstract The advancement towards development of autonomy follows either the bottom-up approach of gradually improving and expanding existing Advanced Driver Assist Systems (ADAS) technology where the driver is present in the control loop or the top-down approach of directly developing autonomous vehicle hardware and software using alternative approaches without the driver present in the control loop. Most ADAS systems today fall under the classification of SAE Level 1 which is also referred to as the driver assistance level. The progression from SAE Level 1 to SAE Level 2 or partial automation involves the critical task of merging automated lateral control and automated longitudinal control such that the tasks of steering and acceleration/deceleration are not required to be handled by the driver under certain conditions [1].
Journal Article

Numerical and Experimental Investigation of the Optimization of Vehicle Speed and Inter-Vehicle Distance in an Automated Highway Car Platoon to Minimize Fuel Consumption

2018-06-22
Abstract The development of the technology of automated highways promises the opportunity for the vehicles to travel safely at a closer distance concerning each other. As such, vehicles moving in the wake of others experience a reduction in fuel consumption. This article investigates the effect of longitudinal distance between two passenger cars on drag coefficients numerically and experimentally. For the numerical analysis, the fluid flow at car speeds of 70, 90 and 110 km/h were examined. The Artificial Intelligence coding was applied to train an Artificial Neural Network to extend the calculated data. The optimum values for the inter-vehicle distance and the vehicle speed to assure the least drag coefficient are obtained. To support the numerical results an instrument designed and built particularly to accurately measure the fuel consumption was installed on a midsize sedan car and some field tests were carried out.
Journal Article

Power Quality Test Data Analysis for Aircraft Subsystem

2018-12-21
Abstract Aircraft subsystem development involves various combinations of testing and qualification activities to realize a flight-worthy system. The subsystem needs to be verified for a massive number of customer requirements. Power quality (PQ) testing is also an important testing activity carried out as part of the environmental qualification test. It is intended to verify the functionality of subsystems with various kinds of power disturbances and to determine the ability of a subsystem to withstand PQ disturbances. The subsystem being designed should be reliable enough to handle PQ anomalies. A PQ test results in an enormous amount of data for analysis with millions of data samples depending on the test and can be identified as big data. The engineer needs to analyze each set of test data as part of post-processing to ensure the power disturbances during testing are as per the standard requirements and that the functional performance of the subsystem is met.
Journal Article

Investigations on Spark and Corona Ignition of Oxymethylene Ether-1 and Dimethyl Carbonate Blends with Gasoline by High-Speed Evaluation of OH* Chemiluminescence

2018-03-01
Abstract Bio-fuels of the 2nd generation constitute a key approach to tackle both Greenhouse Gas (GHG) and air quality challenges associated with combustion emissions of the transport sector. Since these fuels are obtained of residual materials of the agricultural industry, well-to-tank CO2 emissions can be significantly lowered by a closed-cycle of formation and absorption of CO2. Furthermore, studies of bio-fuels have shown reduced formation of particulate matter on account of the fuels’ high oxygen content therefore addressing air quality issues. However, due to the high oxygen content and other physical parameters these fuels are expected to exhibit different ignition behaviour. Moreover, the question is whether there is a positive superimposition of the fuels ignition behaviour with the benefits of an alternative ignition system, such as a corona ignition.
Journal Article

Literature Review on the Effects of Organometallic Fuel Additives in Gasoline and Diesel Fuels

2018-04-18
Abstract A literature review was conducted and fuel survey data were obtained to identify the use of metallic fuel additives (MFAs) within market fuels and determine their effects on engines, exhaust systems, and vehicle performance. The primary focus was on modern vehicles equipped with on-board diagnostic (OBD) systems and advanced emissions control systems. For gasoline, this includes vehicles categorized as National Low Emission Vehicles (NLEV) and Tier 2 or beyond in the U.S., and Euro-3 through Euro-6 in the EU. For diesel, this includes engines/vehicles with original equipment manufacturer (OEM)-equipped oxidation catalysts and diesel particulate filters. The literature search of peer-reviewed papers and other publicly available articles returned over 100 items relevant to the use of organometallic fuel additives, but did not provide significant evidence of widespread use of MFAs in either gasoline or diesel fuels.
Journal Article

The Impacts of Pd in BEA Zeolite on Decreasing Cold-Start NMOG Emission of an E85 Fuel Vehicle

2018-10-25
Abstract In the development of hydrocarbon (HC) traps for E85 fuel vehicle emission control, the addition of palladium (Pd) to BEA zeolite was studied for trapping and decreasing cold-start ethanol emissions. BEA zeolite after a laboratory aging at 750°C for 25 hours released nearly all of the trapped ethanol as unconverted ethanol at low temperature, and some ethene was released at a higher temperature by a dehydration reaction. The addition of Pd to BEA zeolite showed a decrease in the release of unconverted ethanol emissions even after the lab aging. The release of methane (CH4), acetaldehyde (CH3CHO), carbon monoxide (CO), and CO2 from Pd-BEA zeolite during desorption (temperature programmed desorption (TPD)) demonstrated that multiple ethanol reaction mechanisms were involved including dehydrogenation and decomposition reactions.
Journal Article

Power Analysis and Fault Attacks against Secure CAN: How Safe Are Your Keys?

2018-02-14
Abstract Designers of automotive systems find themselves pulled in an impossible number of directions. Systems must use the most advanced security features, but at the same time run on low-cost and resource-constrained hardware. Ultimately, an engineering trade-off will eventually be made regarding how encryption and key management is used on these systems, potentially leaving them vulnerable to attack. In this paper, we detail the applicability of side-channel power analysis and fault injection on automotive electronic systems, showing how these dangerous techniques can be used to break an otherwise secure system. We build a small example network using AES-CCM to implement an encrypted, authenticated CAN protocol. We demonstrate how open-source hardware and software can easily recover the encryption keys from some of these nodes with side-channel power analysis, and we recover a full firmware image from one device with a fault-injection attack using the same tools.
Journal Article

A Centrally Managed Identity-Anonymized CAN Communication System*

2018-05-16
Abstract Identity-Anonymized CAN (IA-CAN) protocol is a secure CAN protocol, which provides the sender authentication by inserting a secret sequence of anonymous IDs (A-IDs) shared among the communication nodes. To prevent malicious attacks from the IA-CAN protocol, a secure and robust system error recovery mechanism is required. This article presents a central management method of IA-CAN, named the IA-CAN with a global A-ID, where a gateway plays a central role in the session initiation and system error recovery. Each ECU self-diagnoses the system errors, and (if an error happens) it automatically resynchronizes its A-ID generation by acquiring the recovery information from the gateway. We prototype both a hardware version of an IA-CAN controller and a system for the IA-CAN with a global A-ID using the controller to verify our concept.
Journal Article

Anomaly-Based Intrusion Detection Using the Density Estimation of Reception Cycle Periods for In-Vehicle Networks

2018-05-16
Abstract The automotive industry intends to create new services that involve sharing vehicle control information via a wide area network. In modern vehicles, an in-vehicle network shares information between more than 70 electronic control units (ECUs) inside a vehicle while it is driven. However, such a complicated system configuration can result in security vulnerabilities. The possibility of cyber-attacks on vehicles via external services has been demonstrated in many research projects. As advances in vehicle systems (e.g., autonomous drive) progress, the number of vulnerabilities to be exploited by cyber-attacks will also increase. Therefore, future vehicles need security measures to detect unknown cyber-attacks. We propose anomaly-based intrusion detection to detect unknown cyber-attacks for the Control Area Network (CAN) protocol, which is popular as a communication protocol for in-vehicle networks.
Journal Article

Intelligent Transportation System Security: Hacked Message Signs

2018-06-18
Abstract “It cannot happen to us” is one of many common myths regarding cybersecurity in the transportation industry. The traditional view that the threats to transportation are low probability and low impact keep agencies from mitigating security threats to transportation critical infrastructure. Current transportation systems depend on closed proprietary systems, which are enhanced by connected cyber-physical systems. Variable Message Signs (VMS) deliver advisory information to road users to ensure safe and efficient trips. Since the first VMS physical hacking more than a decade ago, the importance of VMS security has been a pressing one. VMS hacks can include physical and remote breaches due to the weak protection of the signs and cyber-physical systems.
Journal Article

Enhancement of Automotive Penetration Testing with Threat Analyses Results

2018-11-02
Abstract In this work, we present an approach to support penetration tests by combining safety and security analyses to enhance automotive security testing. Our approach includes a new way to combine safety and threat analyses to derive possible test cases. We reuse outcomes of a performed safety analysis as the input for a threat analysis. We show systematically how to derive test cases, and we present the applicability of our approach by deriving and performing test cases for a penetration test of an automotive electronic control unit (ECU). Therefore, we selected an airbag control unit due to its safety-critical functionality. During the penetration test, the selected control unit was installed on a test bench, and we were able to successfully exploit a discovered vulnerability, causing the detonation of airbags.
Journal Article

Cybersecurity Considerations for Heavy Vehicle Event Data Recorders

2018-12-14
Abstract Trust in the digital data from heavy vehicle event data recorders (HVEDRs) is paramount to using the data in legal contests. Ensuring the trust in the HVEDR data requires an examination of the ways the digital information can be attacked, both purposefully and inadvertently. The goal or objective of an attack on HVEDR data will be to have the data omitted in a case. To this end, we developed an attack tree and establish a model for violating the trust needed for HVEDR data. The attack tree provides context for mitigations and also for functional requirements. A trust model is introduced as well as a discussion on what constitutes forensically sound data. The main contribution of this article is an attack tree-based model of both malicious and accidental events contributing to compromised event data recorder (EDR) data. A comprehensive list of mitigations for HVEDR systems results from this analysis.
Journal Article

An Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Model for the Temperature Prediction of Lithium-Ion Power Batteries

2018-08-14
Abstract Li-ion batteries have been widely applied in the areas of personal electronic devices, stationary energy storage system and electric vehicles due to their high energy/power density, low self-discharge rate and long cycle life etc. For the better designs of both the battery cells and their thermal management systems, various numerical approaches have been proposed to investigate the thermal performance of power batteries. Without the requirement of detailed physical and thermal parameters of batteries, this article proposed a data-driven model using the adaptive neuro-fuzzy inference system (ANFIS) to predict the battery temperature with the inputs of ambient temperature, current and state of charge. Thermal response of a Li-ion battery module was experimentally evaluated under various conditions (i.e. ambient temperature of 0, 5, 10, 15 and 20 °C, and current rate of C/2, 1C and 2C) to acquire the necessary data sets for model development and validation.
Journal Article

Adaptive Transmission Shift Strategy Based on Online Characterization of Driver Aggressiveness

2018-06-04
Abstract Commercial vehicles contribute to the majority of freight transportation in the United States. They are also significant fuel consumers, with over 23% of fuel used in transportation in the United States. The gas price volatility and increasingly stringent regulation on greenhouse-gas emissions have driven manufacturers to adopt new fuel-efficient technologies. Among others, an advanced transmission control strategy, which can provide tangible improvement with low incremental cost. In the commercial sector, individual drivers have little or no interest in vehicle fuel economy, contrary to fleet owners. Aggressive driving behavior can greatly increase the real-world vehicle fuel consumption. However, the effectiveness of transmission calibration to match the shift strategy to the driving characteristics is still a challenge.
Journal Article

Classification of Contact Forces in Human-Robot Collaborative Manufacturing Environments

2018-04-02
Abstract This paper presents a machine learning application of the force/torque sensor in a human-robot collaborative manufacturing scenario. The purpose is to simplify the programming for physical interactions between the human operators and industrial robots in a hybrid manufacturing cell which combines several robotic applications, such as parts manipulation, assembly, sealing and painting, etc. A multiclass classifier using Light Gradient Boosting Machine (LightGBM) is first introduced in a robotic application for discriminating five different contact states w.r.t. the force/torque data. A systematic approach to train machine-learning based classifiers is presented, thus opens a door for enabling LightGBM with robotic data process. The total task time is reduced largely because force transitions can be detected on-the-fly. Experiments on an ABB force sensor and an industrial robot demonstrate the feasibility of the proposed method.
Journal Article

Hydro-Pneumatic Energy Harvesting Suspension System Using a PSO Based PID Controller

2018-08-01
Abstract In this article, a unique design for Hydro-Pneumatic Energy Harvesting Suspension HPEHS system is introduced. The design includes a hydraulic rectifier to maintain one-way flow direction in order to obtain maximum power generation from the vertical oscillation of the suspension system and achieve handling and comfort car drive. A mathematical model is presented to study the system dynamics and non-linear effects for HPEHS system. A simulation model is created by using Advanced Modeling Environment Simulations software (AMEsim) to analyze system performance. Furthermore, a co-simulation platform model is developed using Matlab-Simulink and AMEsim to optimize the PID controller parameters of the external variable load resistor applied on the generator by using Particle Swarm Optimization (PSO).
Journal Article

Improving Hole Expansion Ratio by Parameter Adjustment in Abrasive Water Jet Operations for DP800

2018-09-17
Abstract The use of Abrasive Water Jet (AWJ) cutting technology can improve the edge stretchability in sheet metal forming. The advances in technology have allowed significant increases in working speeds and pressures, reducing the AWJ operation cost. The main objective of this work was to determine the effect of selected AWJ cutting parameters on the Hole Expansion Ratio (HER) for a DP800 (Dual-Phase) Advanced High-Strength Steel (AHSS) with s0 = 1.2 mm by using a fractional factorial design of experiments for the Hole Expansion Tests (HET). Additionally, the surface roughness and residual stresses were measured on the holes looking for a possible relation between them and the measured HER. A deep drawing quality steel DC06 with s0 = 1.0 mm was used for reference. The fracture occurrence was captured by high-speed cameras and by Acoustic Emissions (AE) in order to compare both methods.
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