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2017-11-05
Technical Paper
2017-32-0033
Akinori Shinagawa, Hisayuki Nozawa, Yutaro Uchiyama
Two-wheeled off-road vehicles are mainly ridden on slippery dirt roads that include steep slopes and rough, uneven surfaces. An analysis method for the driving state and the vehicle movement limits that would be suitable for analyzing the movement of such two-wheeled off-road vehicles under these conditions was examined. These movement limits were then formulated by taking into consideration the coefficient of friction and the road surface gradient in accordance with the basic laws of physics and also by focusing on the vehicle movement in the longitudinal direction. Measurements were also taken during actual off-road riding by top-class Japanese off-road motorcycle riders. It was confirmed that this measurement data was distributed within the range of the assumed vehicle movement limits. Consequently, it was confirmed that it is possible to use such measurements to accurately grasp the vehicle movement limits and the associated driving state for two-wheeled off-road vehicles.
2017-09-23
Technical Paper
2017-01-1967
Wei Liu, Huan Tian, Jun Hu, Shuai Cheng, Huai Yuan
Abstract Image segmentation is critical in autonomous driving field. It can reveal essential clues such as objects’ shape or boundary information. The information, moreover, can be leveraged as input information of other tasks: vehicle detection, for example, or vehicle trajectory prediction. SegNet, one deep learning based segmentation model proposed by Cambridge, has been a public baseline for scene perception tasks. It, however, suffers an accuracy deficiency in objects marginal area. Segmentation of this area is very challenging with current models. To alleviate the problem, in this paper, we propose one edge enhanced deep learning based model. Specifically, we first introduced one simple, yet effective Artificial Interfering Mechanism (AIM) which feeds segmentation model manual extracted key features. We argue this mechanism possesses the ability to enhance essential features extraction and hence, ameliorate the model performance.
2017-09-23
Technical Paper
2017-01-1997
Cui Hua
Abstract Vision based driving environment perception is current research hotspot in automatic driving field, which has made great progress due to the continuous breakthroughs in the research of deep neural network. As is well known, deep neural network has won tremendous successes in a wide variety of image recognition tasks, such as pedestrian detection and vehicle identification, which have accomplished the commercialization successfully in intelligent monitor system. Nevertheless, driving environment perception has a higher request for the generalization performance of deep neural network, which needs further studies on its design and training methods. In this paper, we presented a new boosted deep neural network in order to improve its generalization performance and meanwhile keep computational budget constant. Above all, the most representative methods to improve the generalization performance of deep neural network were introduced.
2017-09-23
Technical Paper
2017-01-2007
Fang Li, Lifang Wang, Yan Wu
Abstract With the rapid development of vehicle intelligent and networking technology, the IT security of automotive systems becomes an important area of research. In addition to the basic vehicle control, intelligent advanced driver assistance systems, infotainment systems will all exchange data with in-vehicle network. Unfortunately, current communication network protocols, including Controller Area Network (CAN), FlexRay, MOST, and LIN have no security services, such as authentication or encryption, etc. Therefore, the vehicle are unprotected against malicious attacks. Since CAN bus is actually the most widely used field bus for in-vehicle communications in current automobiles, the security aspects of CAN bus is focused on. Based on the analysis of the current research status of CAN bus network security, this paper summarizes the CAN bus potential security vulnerabilities and the attack means.
2017-09-23
Technical Paper
2017-01-2005
Zhihong Wu, Jian_ning Zhao, Yuan Zhu, Qingchen Li
Abstract Vehicle cybersecurity consists of internal security and external security. Dedicated security hardware will play an important role in car’s internal and external security communication. TPM (Trusted Platform Module) can serve as the security cornerstone when vehicle connects with external entity or constructs a trusted computing environment. Based on functions such as the storage of certificate, key derivation and integrity testing, we research the principle of how to construct a trusted environment in a vehicle which has telematics unit. HSM (Hardware Security Module) can help to realize the onboard cryptographic communication securely and quickly so as to protect data. For certain AURIX MCU consisting of HSM, the experiment result shows that cheaper 32-bit HSM’s AES calculating speed is 25 times of 32-bit main controller, so HSM is an effective choice to realize cybersecurity.
2017-09-19
Journal Article
2017-01-2018
Won Il Jung, Larry Lowe, Luis Rabelo, Gene Lee, Ojeong Kwon
Abstract Operator training using a weapon in a real-world environment is risky, expensive, time-consuming, and restricted to the given environment. In addition, governments are under intense scrutiny to provide security, yet they must also strive for efficiency and reduce spending. In other words, they must do more with less. Virtual simulation, is usually employed to solve these limitations. As the operator is trained to maximize weapon effectiveness, the effectiveness-focused training can be completed in an economical manner. Unfortunately, the training is completed in limited scenarios without objective levels of training factors for an individual operator to optimize the weapon effectiveness. Thus, the training will not be effective. For overcoming this problem, we suggest a methodology on guiding effectiveness-focused training of the weapon operator through usability assessments, big data, and Virtual and Constructive (VC) simulations.
2017-09-04
Technical Paper
2017-24-0054
Francesco de Nola, Giovanni Giardiello, Alfredo Gimelli, Andrea Molteni, Massimiliano Muccillo, Roberto Picariello
Abstract In the last few years, the automotive industry had to face three main challenges: compliance with more severe pollutant emission limits, better engine performance in terms of torque and drivability and simultaneous demand for a significant reduction in fuel consumption. These conflicting goals have driven the evolution of automotive engines. In particular, the achievement of these mandatory aims, together with the increasingly stringent requirements for carbon dioxide reduction, led to the development of highly complex engine architectures needed to perform advanced operating strategies. Therefore, Variable Valve Actuation (VVA), Exhaust Gas Recirculation (EGR), Gasoline Direct Injection (GDI), turbocharging, powertrain hybridization and other solutions have gradually and widely been introduced into modern internal combustion engines, enhancing the possibilities of achieving the required goals.
2017-09-04
Technical Paper
2017-24-0068
Roberto Finesso, Ezio Spessa, Yixin Yang, Giuseppe Conte, Gennaro Merlino
Abstract A real-time approach has been developed and assessed to control BMEP (brake mean effective pressure) and MFB50 (crank angle at which 50% of fuel mass has burnt) in a Euro 6 1.6L GM diesel engine. The approach is based on the use of feed-forward ANNs (artificial neural networks), which have been trained using virtual tests simulated by a previously developed low-throughput physical engine model. The latter is capable of predicting the heat release and the in-cylinder pressure, as well as the related metrics (MFB50, IMEP - indicated mean effective pressure) on the basis of an improved version of the accumulated fuel mass approach. BMEP is obtained from IMEP taking into account friction losses. The low-throughput physical model does not require high calibration effort and is also suitable for control-oriented applications.
2017-09-04
Journal Article
2017-24-0051
Ferdinando Taglialatela, Mario Lavorgna, Silvana Di Iorio, Ezio Mancaruso, Bianca Maria Vaglieco
Abstract In order to meet the increasingly strict emission regulations, several solutions for NOx and PM emissions reduction have been studied. Exhaust gas recirculation (EGR) technology has become one of the more used methods to accomplish the NOx emissions reduction. However, actual control strategies do not consider, in the definition of optimal EGR, its effect on particle size and density. These latter have a great importance both for the optimal functioning of after-treatment systems, but also for the adverse effects that small particles have on human health. Epidemiological studies, in fact, highlighted that the toxicity of particulate particles increases as the particle size decreases. The aim of this paper is to present a Neural Network model able to provide real time information about the characteristics of exhaust particles emitted by a Diesel engine.
2017-06-05
Technical Paper
2017-01-1904
Tan Li, Ricardo Burdisso, Corina Sandu
Abstract Tire-pavement interaction noise (TPIN) is a dominant source for passenger cars and trucks above 40 km/h and 70 km/h, respectively. TPIN is mainly generated from the interaction between the tire and the pavement. In this paper, twenty-two passenger car radial (PCR) tires of the same size (16 in. radius) but with different tread patterns were tested on a non-porous asphalt pavement. For each tire, the noise data were collected using an on-board sound intensity (OBSI) system at five speeds in the range from 45 to 65 mph (from 72 to 105 km/h). The OBSI system used an optical sensor to record a once-per-revolution signal to monitor the vehicle speed. This signal was also used to perform order tracking analysis to break down the total tire noise into two components: tread pattern-related noise and non-tread pattern-related noise.
2017-03-28
Technical Paper
2017-01-1726
Sameer Shah, Aayoush Sharma, Raghav Angra, Nitin Singh, Khalique Ahmed
Abstract In an unavoidable event of a suspect being chased by police, there is high probability for the criminal to evade the police while driving his vehicle. At many instances, criminal escapes without leaving a trail behind and becomes untraceable. A new concept of Vigilance Assistance System Network (VASN) has been developed, which is spread across the city and helps in catching the escaping criminals. At every junction, the traffic-signals are installed with a microcontroller chip and these connected traffic signals form a network with distinct city areas demarcated on the map. The vehicle is installed with GPS and a RFID module on their ECU when it approaches any intersection or junction; they receive wireless signals from traffic-signals and transmit another registering signal to the traffic-light wirelessly through RFID.
2017-03-28
Technical Paper
2017-01-0092
Vladimir Hahanov, Wajeb Gharibi, Eugenia Litvinova, Svitlana Chumachenko, Arthur Ziarmand, Irina Englesi, Igor Gritsuk, Vladimir Volkov, Anastasiia Khakhanova
Abstract The new cyber-technological culture of the transport control based on virtual road signs and streetlight signals on the screen of car is the future of Humanity. A cyber-physical system (CPS) Smart Cloud Traffic Control, which realizes the mentioned culture, is proposed; it is characterized by the presence of the digitized regulatory rules, vehicles, infrastructure components, and also accurate monitoring, active cloud streetlight-free cyber control of road users, traffic lights, automatic output of operational regulatory actions (virtual traffic signs and traffic signals) to monitor of each vehicle. The main components of the cyber-physical system are the following: infrastructure, road users and rules, which have digital representation in cyberspace to realize a route, based on digital monitoring and cloud mobile control.
2017-03-28
Technical Paper
2017-01-1428
Berkan Guleyupoglu, Ryan Barnard, F. Scott Gayzik
Abstract Computational modeling of the human body is increasingly used to evaluate countermeasure performance during simulated vehicle crashes. Various injury criteria can be calculated from such models and these can either be correlative (HIC, BrIC, etc.) or based on local deformation and loading (strain-based rib fracture, organ damage, etc.). In this study, we present a method based on local deformation to extract failed rib region data. The GHMBC M50-O model was used in a Frontal-NCAP severity sled simulation. Failed Rib Regions (FRRs) in the M50-O model are handled through element deletion once the element surpasses 1.8% effective strain. The algorithm central to the methodology presented extracts FRR data and requires 4-element connectivity to register a failure. Furthermore, the FRRs are localized to anatomical sections (Lateral, Anterior, and Posterior), rib level (1,2,3 etc.) and element strain data is recorded.
2017-03-28
Technical Paper
2017-01-0099
Jose E. Solomon, Francois Charette
Abstract The proposed technique is a tailored deep neural network (DNN) training approach which uses an iterative process to support the learning of DNNs by targeting their specific misclassification and missed detections. The process begins with a DNN that is trained on freely available annotated image data, which we will refer to as the Base model, where a subset of the categories for the classifier are related to the automotive theater. A small set of video capture files taken from drives with test vehicles are selected, (based on the diversity of scenes, frequency of vehicles, incidental lighting, etc.), and the Base model is used to detect/classify images within the video files. A software application developed specifically for this work then allows for the capture of frames from the video set where the DNN has made misclassifications. The corresponding annotation files for these images are subsequently corrected to eliminate mislabels.
2017-03-28
Technical Paper
2017-01-0240
Yanli Zhao, Hao Zhou, Yimin Liu
Abstract Ride Hailing service and Dynamic Shuttle are two key smart mobility practices, which provide on-demand door-to-door ride-sharing service to customers through smart phone apps. On the other hand, some big companies spend millions of dollars annually in third party vendors to offer shuttle services to pick up and drop off employees at fixed locations and provide them daily commutes for employees to and from work. Efficient fixed routing algorithms and analytics are the key ingredients for operating efficiency behind these services. They can significantly reduce operating costs by shortening bus routes and reducing bus numbers, while maintaining the same quality of service. This study developed an off-line optimization routing method for employee shuttle services including regular work shifts and demand based shifts (e.g. overtime shifts) in some regions.
2017-03-28
Technical Paper
2017-01-1652
Rolf Schneider, Andre Kohn, Martin Klimke, Udo Dannebaum
Abstract Driven by the growing internet and remote connectivity of automobiles, combined with the emerging trend to automated driving, the importance of security for automotive systems is massively increasing. Although cyber security is a common part of daily routines in the traditional IT domain, necessary security mechanisms are not yet widely applied in the vehicles. At first glance, this may not appear to be a problem as there are lots of solutions from other domains, which potentially could be re-used. But substantial differences compared to an automotive environment have to be taken into account, drastically reducing the possibilities for simple reuse. Our contribution is to address automotive electronics engineers who are confronted with security requirements. Therefore, it will firstly provide some basic knowledge about IT security and subsequently present a selection of automotive specific security use cases.
2017-03-28
Technical Paper
2017-01-1654
Arun Ganesan, Jayanthi Rao, Kang Shin
Abstract Modern vehicles house many advanced components; sensors and Electronic Control Units (ECUs) — now numbering in the 100s. These components provide various advanced safety, comfort and infotainment features, but they also introduce additional attack vectors for malicious entities. Attackers can compromise one or more of these sensors and flood the vehicle’s internal network with fake sensor values. Falsified sensor values can confuse the driver, and even cause the vehicle to misbehave. Redundancy can be used to address compromised sensors, but adding redundant sensors will increase the cost per vehicle and is therefore less attractive. To balance the need for security and cost-efficiency, we exploit the natural redundancy found in vehicles. Natural redundancy occurs when the same physical phenomenon causes symptoms in multiple sensors. For instance, pressing the accelerator pedal will cause the engine to pump faster and increase the speed of the vehicle.
2017-03-28
Technical Paper
2017-01-0117
Raja Sekhar Dheekonda, Sampad Panda, Md Nazmuzzaman khan, Mohammad Hasan, Sohel Anwar
Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object’s description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms.
2017-03-28
Technical Paper
2017-01-0239
Seth Bryan, Maria Guido, David Ostrowski, N. Khalid Ahmed
Abstract It is desirable to find methods to increase electric vehicle (EV) driving range and reduce performance variability of Plug-in Hybrid Electric Vehicles (PHEV). One strategy to improve EV range is to increase the charge power limit of the traction battery, which allows for more brake energy recovery. This paper applies Big Data technology to investigate how increasing the charge power limit could affect EV range in real world usage with respect to driving behavior. Big Data Drive (BDD) data collected from Ford employee vehicles in Michigan was analyzed to assess the impact of regenerative braking power on EV range. My Ford Mobile (MFM) data was also leveraged to find correlation to drivers nationwide based on brake score statistics. Estimated results show incremental improvements in EV range from increased charge power levels. Subsequently, this methodology and process could be applied to make future design decisions based on the dynamic nature of driving habits.
2017-03-28
Technical Paper
2017-01-1612
Tri P. Doan, Subramaniam Ganesan
Abstract Robert Bosch GmBH proposed in 2012 a new version of communication protocol named as Controller area network with Flexible Data-Rate (CANFD), that supports data frames up to 64 bytes compared to 8 bytes of CAN. With limited data frame size of CAN message, and it is impossible to be encrypted and secured. With this new feature of CAN FD, we propose a hardware design - CAN crypto FPGA chip to secure data transmitted through CAN FD bus by using AES-128 and SHA-1 algorithms with a symmetric key. AES-128 algorithm will provide confidentiality of CAN message and SHA-1 algorithm with a symmetric key (HMAC) will provide integrity and authentication of CAN message. The design has been modeled and verified by using Verilog HDL – a hardware description language, and implemented successfully into Xilinx FPGA chip by using simulation tool ISE (Xilinx).
2017-03-28
Technical Paper
2017-01-1659
Mert D. Pesé, Karsten Schmidt, Harald Zweck
Abstract The automotive industry experiences a major change as vehicles are gradually becoming a part of the Internet. Security concepts based on the closed-world assumption cannot be deployed anymore due to a constantly changing adversary model. Automotive Ethernet as future in-vehicle network and a new E/E Architecture have different security requirements than Ethernet known from traditional IT and legacy systems. In order to achieve a high level of security, a new multi-layer approach in the vehicle which responds to special automotive requirements has to be introduced. One essential layer of this holistic security concept is to restrict non-authorized access by the deployment of embedded firewalls. This paper addresses the introduction of automotive firewalls into the next-generation domain architecture with a focus on partitioning of its features in hardware and software.
2017-03-28
Technical Paper
2017-01-1653
Jon Barton Shields, Jörg Huser, David Gell
Abstract This paper discusses the merits, benefits and usage of autonomous key management (with implicit authentication) (AKM) solutions for securing ECU-to-ECU communication within the connected vehicle and IoT applications; particularly for transmissions between externally exposed, edge ECU sensors connected to ECUs within the connected vehicle infrastructure. Specific benefits addressed include reductions of communication latency, implementation complexity, processing power and energy consumption. Implementation issues discussed include provisioning, key rotation, synchronization, re-synchronization, digital signatures and enabling high entropy.
2017-03-28
Technical Paper
2017-01-1657
Jesse Edwards, Ameer Kashani
Abstract In the past few years, automotive electronic control units (ECUs) have been the focus of many studies regarding the ability to affect the deterministic operation of safety critical cyber-physical systems. Researchers have been able to successfully demonstrate flaws in security design that have considerable, dramatic impacts on the functional safety of a target vehicle. With the rapid increase in data connectivity within a modern automobile, the attack surface has been greatly broadened to allow adversaries remote access to vehicle control system software and networks. This has serious implications, as a vast number of vulnerability disclosures released by security researchers point directly to common programming bugs and software quality issues as the root cause of successful exploits which can compromise the vehicle as a whole. In this paper, we aim to bring to light the most prominent categories of bugs found during the software development life cycle of an automotive ECU.
2017-03-28
Technical Paper
2017-01-1658
Qingwu Zou, Wai Keung Chan, Kok Cheng Gui, Qi Chen, Klaus Scheibert, Laurent Heidt, Eric Seow
Abstract Cyber security is becoming increasingly critical in the car industry. Not only the entry points to the external world in the car need to be protected against potential attack, but also the on-board communication in the car require to be protected against attackers who may try to send unauthorized CAN messages. However, the current CAN network was not designed with security in mind. As a result, the extra measures have to be taken to address the key security properties of the secure CAN communication, including data integrity, authenticity, confidentiality and freshness. While integrity and authenticity can be achieved by using a relatively straightforward algorithms such as CMAC (Cipher-based Message Authentication Code) and Confidentiality can be handled by a symmetric encryption algorithm like AES128 (128-bit Advanced Encryption Standard), it has been recognized to be more challenging to achieve the freshness of CAN message.
2017-03-28
Technical Paper
2017-01-0534
Bojan S. Jander, Roland Baar
Abstract The knowledge of thermal behavior of combustion engines is extremely important e.g. to predict engine warm up or to calculate engine friction and finally to optimize fuel consumption. Typically, thermal engine behavior is modeled using look-up tables or semi-physical models to calculate the temperatures of structure, coolant and oil. Using look-up tables can result in inaccurate results due to interpolation and extrapolation; semi-physical modeling leads to high computation time. This work introduces a new kind of model to calculate thermal behavior of combustion engines using an artificial neural network (ANN) which is highly accurate and extremely fast. The neural network is a multi-layered feed-forward network; it is trained by data generated with a validated semi-physical model. Output data of the ANN-based model are calculated with nonlinear transformation of input data and weighting of these transformations.
2017-03-28
Journal Article
2017-01-1662
Tom R. Markham, Alex Chernoguzov
Abstract The On-Board Diagnostics II (OBD-II) port began as a means of extracting diagnostic information and supporting the right to repair. Self-driving vehicles and cellular dongles plugged into the OBD-II port were not anticipated. Researchers have shown that the cellular modem on an OBD-II dongle may be hacked, allowing the attacker to tamper with the vehicle brakes. ADAS, self-driving features and other vehicle functions may be vulnerable as well. The industry must balance the interests of multiple stakeholders including Original Equipment Manufacturers (OEMs) who are required to provide OBD function, repair shops which have a legitimate need to access the OBD functions, dongle providers and drivers. OEMs need the ability to protect drivers and manage liability by limiting how a device or software application may modify the operation of a vehicle.
2017-03-28
Technical Paper
2017-01-0109
Yi Zhang, Madeline J. Goh, Vidya Nariyambut Murali
Abstract This work describes a single camera based object distance estimation system. As technology on vehicles is constantly advancing on the road to autonomy, it is critical to know the locations of objects in 3D space for safe behavior of the vehicle. Though significant progress has been made on object detection in 2D sensor space from a single camera, this work additionally estimates the distance to said object without requiring stereo vision or absolute knowledge of vehicle motion. Specifically, our proposed system is comprised of three modules: vision based ego-motion estimation, object-detection, and distance estimation. In particular, we compensate for the vehicle ego-motion by using pin-hole camera model to increase the accuracy of the object distance estimation.
2017-03-28
Journal Article
2017-01-0236
Zhigang Wei, Kamran Nikbin
In the Big Data era, the capability in statistical and probabilistic data characterization, data pattern identification, data modeling and analysis is critical to understand the data, to find the trends in the data, and to make better use of the data. In this paper the fundamental probability concepts and several commonly used probabilistic distribution functions, such as the Weibull for spectrum events and the Pareto for extreme/rare events, are described first. An event quadrant is subsequently established based on the commonality/rarity and impact/effect of the probabilistic events. Level of measurement, which is the key for quantitative measurement of the data, is also discussed based on the framework of probability. The damage density function, which is a measure of the relative damage contribution of each constituent is proposed. The new measure demonstrates its capability in distinguishing between the extreme/rare events and the spectrum events.
2017-03-28
Journal Article
2017-01-0233
Weihong Guo, Shenghan Guo, Hui Wang, Xiao Yu, Annette Januszczak, Saumuy Suriano
Abstract The wide applications of automatic sensing devices and data acquisition systems in automotive manufacturing have resulted in a data-rich environment, which demands new data mining methodologies for effective data fusion and information integration to support decision making. This paper presents a new methodology for developing a diagnostic system using manufacturing system data for high-value assets in automotive manufacturing. The proposed method extends the basic attributes control charts with the following key elements: optimal feature subset selection considering multiple features and correlation structure, balancing the type I and type II errors in decision making, on-line process monitoring using adaptive modeling with control charts, and diagnostic performance assessment using shift and trend detection. The performance of the developed diagnostic system can be continuously improved as the knowledge of machine faults is automatically accumulated during production.
2017-03-28
Journal Article
2017-01-0247
N. Khalid Ahmed, Jimmy Kapadia
Abstract Electrified vehicles including Battery Electric Vehicles (BEVs) and Plug-In Hybrid Vehicles (PHEVs) made by Ford Motor Company are fitted with a telematics modem to provide customers with the means to communicate with their vehicles and, at the same time, receive insight on their vehicle usage. These services are provided through the “MyFordMobile” website and phone applications, simultaneously collecting information from the vehicle for different event triggers. In this work, we study this data by using Big Data Methodologies including a Hadoop Database for storing data and HiveQL, Pig Latin and Python scripts to perform analytics. We present electrified vehicle customer behaviors including geographical distribution, trip distances, and daily distances and compare these to the Atlanta Regional Survey data. We discuss customer behaviors pertinent to electrified vehicles including charger types used, charging occurrence, charger plug-in times etc.
Viewing 1 to 30 of 1576