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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

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

The Placement of Digitized Objects in a Point Cloud as a Photogrammetric Technique

2018-08-08
Abstract The frequency of video-capturing collision events from surveillance systems are increasing in reconstruction analyses. The video that has been provided to the investigator may not always include a clear perspective of the relevant area of interest. For example, surveillance video of an incident may have captured a pre- or post-incident perspective that, while failing to capture the precise moment when the pedestrian was struck by a vehicle, still contains valuable information that can be used to assist in reconstructing the incident. When surveillance video is received, a quick and efficient technique to place the subject object or objects into a three-dimensional environment with a known rate of error would add value to the investigation.
Journal Article

Optimization Control for 4WIS Electric Vehicle Based on the Coincidence Degree of Wheel Steering Centers

2018-07-24
Abstract The steering centers of four wheels for passenger car do not coincide, which may result in tire wear and the unharmoniously movement of the vehicle. In this article, an optimization control method for Four Wheel Independent Steering (4WIS) electric vehicle based on the coincidence degree of steering centers is proposed, to improve the driving performance. The nonlinear vehicle model of the four-wheel independent steering vehicle is established, and the formula of the wheel steering center is derived. The coincidence degree of wheel steering centers is defined as the evaluation index, to describe and evaluate the performance of the coordination for wheels’ movement. Meanwhile, the structure design of 4WIS system and the establishment of Direct-Current (DC) steering motor model are carried out, and the Model Predictive Control (MPC) controller for steering actuator is designed.
Journal Article

3D Scene Reconstruction with Sparse LiDAR Data and Monocular Image in Single Frame

2017-09-23
Abstract Real-time reconstruction of 3D environment attributed with semantic information is significant for a variety of applications, such as obstacle detection, traffic scene comprehension and autonomous navigation. The current approaches to achieve it are mainly using stereo vision, Structure from Motion (SfM) or mobile LiDAR sensors. Each of these approaches has its own limitation, stereo vision has high computational cost, SfM needs accurate calibration between a sequences of images, and the onboard LiDAR sensor can only provide sparse points without color information. This paper describes a novel method for traffic scene semantic segmentation by combining sparse LiDAR point cloud (e.g. from Velodyne scans), with monocular color image. The key novelty of the method is the semantic coupling of stereoscopic point cloud with color lattice from camera image labelled through a Convolutional Neural Network (CNN).
Journal Article

Personalized Controller Design for Electric Power Steering System Based on Driver Behavior

2017-09-23
Abstract Electric power steering (EPS) system is a kind of dynamic control system for vehicle steering, which can amplify the driver steering torque inputs to the vehicle to improve steering comfortable and performance, but the present EPS can’t cater to the driving habits of different people. In this paper, a personalized EPS controller is designed based on the driver behavior, which combines real-time driver behavior identification strategy with personalized assistance characteristic. Firstly, the driver behavior data acquisition system is designed and established, based on which, the input data of different kinds of drivers along with vehicle signals are collected under typical working conditions, then the identification of driver behavior online is realized using the BP neural network.
Journal Article

Transient Operation and Over-Dilution Mitigation for Low-Pressure EGR Systems in Spark-Ignition Engines

2018-09-17
Abstract Low-Pressure cooled Exhaust Gas Recirculation (LP-cEGR) is proven to be an effective technology for fuel efficiency improvement in turbocharged spark-ignition (SI) engines. Aiming to fully exploit the EGR benefits, new challenges are introduced that require more complex and robust control systems and strategies. One of the most important restrictions of LP-cEGR is the transient response, since long air-EGR flow paths introduce significant transport delays between the EGR valve and the cylinders. High dilution generally increases efficiency, but can lead to cycle-by-cycle combustion variation. Especially in SI engines, higher-than-requested EGR dilution may lead to combustion instabilities and misfires. Considering the long EGR evacuation period, one of the most challenging transient events is throttle tip-out, where the engine operation shifts from a high-load point with high dilution tolerance to a low-load point where EGR tolerance is significantly reduced.
Journal Article

Comparative Analysis of Performance of Neural Estimators for Diagnostics in Engine Emission System

2018-06-14
Abstract This article describes the results of a comparative performance analysis on the use of neural estimators to accurately estimate the Differential Pressure (DP) signal from diesel engine systems equipped with a Diesel Particulate Filter (DPF) aftertreatment system. For most systems, there are known and modeled relationships between system inputs and outputs; however, in the case of nonlinear, time-varying systems a detailed modeling of the system might not be readily available. Therefore, Artificial Neural Networks (ANNs) have been used for developing critical relationship between system inputs (engine and aftertreatment parameters) and system output (DP signal). Both batch (offline) and online learning ANN estimators have been proposed. A control-oriented engine out DPF-DP model is desirable for on-board applications as a virtual DPF-DP sensor which could be used in parallel as an alternate analytical redundancy-based sensor.
Journal Article

Prediction and Control of Response Time of the Semitrailer Air Braking System

2019-05-09
Abstract The response time of the air braking system is the main parameter affecting the longitudinal braking distance of vehicles. In this article, in order to predict and control the response time of the braking system of semitrailers, an AMESim model of the semitrailer braking system involving the relay emergency valve (REV) and chambers was established on the basis of analyzing systematically the working characteristics of the braking system in different braking stages: feedback braking, relay braking, and emergency braking. A semitrailer braking test bench including the brake test circuit and data acquisition system was built to verify the model with typical maneuver. For further evaluating the semitrailer braking response time, an experiment under different control pressures was carried out. Experimental results revealed the necessity of controlling the response time.
Journal Article

A Combination of Intelligent Tire and Vehicle Dynamic Based Algorithm to Estimate the Tire-Road Friction

2019-04-08
Abstract One of the most important factors affecting the performance of vehicle active chassis control systems is the tire-road friction coefficient. Accurate estimation of the friction coefficient can lead to better performance of these controllers. In this study, a new three-step friction estimation algorithm, based on intelligent tire concept, is proposed, which is a combination of experiment-based and vehicle dynamic based approaches. In the first step of the proposed algorithm, the normal load is estimated using a trained Artificial Neural Network (ANN). The network was trained using the experimental data collected using a portable tire testing trailer. In the second step of the algorithm, the tire forces and the wheel longitudinal velocity are estimated through a two-step Kalman filter. Then, in the last step, using the estimated tire normal load and longitudinal and lateral forces, the friction coefficient can be estimated.
Journal Article

CAN-Bus Remote Monitoring: Standalone CAN Sensor Reading and Automotive Diagnostics

2019-02-08
Abstract A vehicle may be a font of data for some applications in safety, maintenance, and entertainment systems, once its electronic control units are connected to each other by a Controller Area Network (CAN) bus. By plugging a compatible device on the vehicle onboard diagnostics interface, reading raw data or conducting automotive diagnostics by International Standardization Organization 15765 and Society of Automotive Engineers J1979 is possible. The usual low-cost CAN data acquisition devices do not allow the connection to a cloud service for remote monitoring. Looking at this issue, this work proposes a low-cost NodeMCU CAN shield for data acquisition which is able to read the CAN frame of a Steering Angle Sensor, in Scenario 1, and standardized information from a vehicle such as its speed, identification number, and engine coolant temperature by automotive diagnostics, in Scenario 2.
Journal Article

Evaluation of a Robust Haptic Interface for Semi-Autonomous Vehicles

2019-05-15
Abstract The advent of steer-by-wire technologies has changed the driving paradigm for drivers and vehicle autonomy. Such technologies integrate electric motors to actuate the tire-road plus human-machine interfaces. Steer-by-wire vehicles can benefit from haptic concepts through the provision of tunable force feedback, coupled with nonlinear control, to introduce lane keeping and pathway following technologies that minimize and possibly eliminate driver actions. In this article, two vehicle haptic interfaces, including a robotic grip and a joystick, both of which are accompanied by nonlinear sliding mode control, have been developed and studied on a steer-by-wire platform integrated with a virtual reality driving environment. An operator-in-the-loop evaluation that included 30 human test subjects investigated these haptic steering interfaces over a prescribed series of driving maneuvers through real-time data logging and post-test questionnaires.
Journal Article

Erosion Wear Response of Linz-Donawitz Slag Coatings: Parametric Appraisal and Prediction Using Imperialist Competitive Algorithm and Neural Computation

2019-03-14
Abstract Slag, generated from basic oxygen furnace (BOF) or Linz-Donawitz (LD) converter, is one of the recyclable wastes in an integrated steel plant. The present work aims at utilization of waste LD slag to develop surface coatings by plasma spraying technique. This study reveals that LD slag can be gainfully used as a cost-effective wear-resistant coating material. A prediction model based on an artificial neural network (ANN) is also proposed to predict the erosion performance of these coatings. The 2.27% error shows that ANN successfully predicts the erosion wear rate of the coatings both within and beyond the experimental domain. In addition to it, a novel optimization algorithm called imperialist competitive algorithm (ICA) is used to obtain minimum erosion wear rate of 12.12 mg/kg.
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