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2017-03-28
Technical Paper
2017-01-0060
Heiko Doerr, Thomas End, Lena Kaland
Abstract The release of the ISO 26262 in November 2011 was a major milestone for the safeguarding of safety-related systems that include one or more electrical and / or electronic (E/E) systems and that are installed in series production passenger cars. Although no specific requirements exist for a model-based software development process, ISO 26262 compiles general requirements and recommendations that need to be applied to model-based development. The second edition of the ISO 26262 has been distributed for review with a final publication scheduled for 2018. This revised edition not only integrates the experiences of the last few years but also extends the overall scope of safety-related systems. In order to determine the necessary adaptions for already existing software development processes, a detailed analysis of this revision is necessary. In this work, we focus on an analysis and the impact on model-based software development of safety-related systems.
2017-03-28
Journal Article
2017-01-0290
Veera Aditya Yerra, Srikanth Pilla
Abstract The advancements in automation, big data computing and high bandwidth networking has expedited the realization of Industrial Internet of Things (IIoT). IIoT has made inroads into many sectors including automotive, semiconductors, electronics, etc. Particularly, it has created numerous opportunities in the automotive manufacturing sector to realize the new aura of platform concepts such as smart material flow control. This paper provides a thought provoking application of IIoT in automotive composites body shop. By creating a digital twin for every physical part, we no longer need to adhere to the conventional manufacturing processes and layouts, thus opening up new opportunities in terms of equipment and space utilization. The century-old philosophy of the assembly line might not be the best layout for vehicle manufacturing, thus proposing a novel assembly grid layout inspired from a colony of ants working to accomplish a common goal.
2017-03-28
Journal Article
2017-01-0317
James Henry Wrock, Pengying Niu, Huairui Guo
Abstract Automobiles have a high degree of mechanical and electrical complexity. However, product complexity has the accompanying effect of requiring high levels of design and process oversight. The net result is a product creation process which is prone to creating failures. These failures typically have their origin in an overall lack of complete understanding of the system in terms of materials, geometries and energy flows. Despite all of the engineering intentions, failures are inevitable, common, and must be dealt with accordingly. In the worst case, if a failure manifests itself into an observable failure the customer may have a negative experience. Therefore, it is imperative that design engineers, suppliers along with reliability professionals be able to assess the design risk. One approach to assess risk is the use of degradation analysis.
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.
2017-03-28
Technical Paper
2017-01-0262
Taewon Kim, Xi Luo, Mustafa Al-Sadoon, Ming-Chia Lai, Marcis Jansons, Doohyun Kim, Jason Martz, Angela Violi, Eric Gingrich
Abstract Three jet fuel surrogates were compared against their target fuels in a compression ignited optical engine under a range of start-of-injection temperatures and densities. The jet fuel surrogates are representative of petroleum-based Jet-A POSF-4658, natural gas-derived S-8 POSF-4734 and coal-derived Sasol IPK POSF-5642, and were prepared from a palette of n-dodecane, n-decane, decalin, toluene, iso-octane and iso-cetane. Optical chemiluminescence and liquid penetration length measurements as well as cylinder pressure-based combustion analyses were applied to examine fuel behavior during the injection and combustion process. HCHO* emissions obtained from broadband UV imaging were used as a marker for low temperature reactivity, while 309 nm narrow band filtered imaging was applied to identify the occurrence of OH*, autoignition and high temperature reactivity.
2017-03-28
Technical Paper
2017-01-0137
Akira Ando, Koichi Hamashima, Shinji Kato, Noriyuki Tomita, Takahiro Uejima
Abstract In respect to the present large refrigerator trucks, sub-engine type is the main product, but the basic structure does not change greatly since the introduction for around 50 years. A sub-engine type uses an industrial engine to drive the compressor, and the environmental correspondence such as the fuel consumption, the emission is late remarkably. In addition, most of trucks carry the truck equipment including the refrigerator which consumes fuel about 20% of whole vehicle. Focusing on this point, the following are the reports about the system development plan for fuel consumption reduction of the large size refrigerator truck. New concept is to utilize electrical power from HV system to power the electric-driven refrigerator. We have developed a fully electric-driven refrigerator system, which uses regenerated energy that is dedicated for our refrigerator system.
2017-03-28
Technical Paper
2017-01-0114
Jorge De-J. Lozoya Santos, J. C. Tudon-Martinez
Abstract The project consists on the mechanical and electronic instrumentation of an existing vehicle (built at Universidad de Monterrey for the SAE Supermileage Competition) to be able to control its steering, braking and throttle systems “by wire”. Insight to the stages of turning the vehicle into an autonomous one is presented. This includes identification of the current mechanical properties, choosing adequate components and the use of a simulation to allow early work on the software involving cameras and motors to provide autonomy to the vehicle. Using software in the loop methodology mathematical models of the dynamics of the vehicle are run in Simulink and update the position and orientation of the 3D model of the vehicle in V-REP, a robot simulator.
2017-03-28
Technical Paper
2017-01-0116
Ankit Goila, Ambarish Desai, Feng Dang, Jian Dong, Rahul Shetty, Rakesh Babu Kailasa, Mahdi Heydari, Yang Wang, Yue Sun, Manikanta Jonnalagadda, Mohammed Alhasan, Hanlong Yang, Katherine R. Lastoskie
ADAS features development involves multidisciplinary technical fields, as well as extensive variety of different sensors and actuators, therefore the early design process requires much more resources and time to collaborate and implement. This paper will demonstrate an alternative way of developing prototype ADAS concept features by using remote control car with low cost hobby type of controllers, such as Arduino Due and Raspberry Pi. Camera and a one-beam type Lidar are implemented together with Raspberry Pi. OpenCV free open source software is also used for developing lane detection and object recognition. In this paper, we demonstrate that low cost frame work can be used for the high level concept algorithm architecture, development, and potential operation, as well as high level base testing of various features and functionalities. The developed RC vehicle can be used as a prototype of the early design phase as well as a functional safety testing bench.
2017-03-28
Technical Paper
2017-01-0113
Vaclav Jirovsky
Abstract Today's vehicles are being more often equipped with systems, which are autonomously influencing the vehicle behavior. More systems of the kind and even fully autonomous vehicles in regular traffic are expected by OEMs in Europe around year 2025. Driving is highly multitasking activity and human errors emerge in situations, when he is unable to process and understand the essential amount of information. Future autonomous systems very often rely on some type of inter-vehicular communication. This shall provide the vehicle with higher amount of information, than driver uses in his decision making process. Therefore, currently used 1-D quantity TTC (time-to-collision) will become inadequate. Regardless the vehicle is driven by human or robot, it’s always necessary to know, whether and which reaction is necessary to perform. Adaptable autonomous vehicle systems will need to analyze the driver’s situation awareness level.
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
2017-01-0107
Arvind Jayaraman, Ashley Micks, Ethan Gross
Abstract Recreating traffic scenarios for testing autonomous driving in the real world requires significant time, resources and expense, and can present a safety risk if hazardous scenarios are tested. Using a 3D virtual environment to enable testing of many of these traffic scenarios on the desktop or cluster significantly reduces the amount of required road tests. In order to facilitate the development of perception and control algorithms for level 4 autonomy, a shared memory interface between MATLAB, Simulink, and Unreal Engine 4 can send information (such as vehicle control signals) back to the virtual environment. The shared memory interface conveys arbitrary numerical data, RGB image data, and point cloud data for the simulation of LiDAR sensors.
2017-03-28
Technical Paper
2017-01-0104
Maryam Moosaei, Yi Zhang, Ashley Micks, Simon Smith, Madeline J. Goh, Vidya Nariyambut Murali
Abstract In this work, we outline a process for traffic light detection in the context of autonomous vehicles and driver assistance technology features. For our approach, we leverage the automatic annotations from virtually generated data of road scenes. Using the automatically generated bounding boxes around the illuminated traffic lights themselves, we trained an 8-layer deep neural network, without pre-training, for classification of traffic light signals (green, amber, red). After training on virtual data, we tested the network on real world data collected from a forward facing camera on a vehicle. Our new region proposal technique uses color space conversion and contour extraction to identify candidate regions to feed to the deep neural network classifier. Depending on time of day, we convert our RGB images in order to more accurately extract the appropriate regions of interest and filter them based on color, shape and size.
2017-03-28
Technical Paper
2017-01-0103
Thomas Beyerl, Bernard Ibru, Charvi Popat, Deborah Ojo, Alexander Bakus, Jessica Elder, Valentin Soloiu
Abstract Autonomous vehicles must possess the capability to navigate complex intersections, which do not conform to typical models. Such intersections may have multiple roadways of different classes, highly acute angles, or unique multi-modal combinations. These may include railway grade crossings, bicycle lanes, or unique signal arrangements. Conventional navigation systems, which gather data from the surrounding area then plan a path through the collected data require faultless and complex analysis of extremely unstructured environments. The vehicle must then avoid obstacles as well as successfully navigate the intersection with extremely low tolerance for error. Computer decision making challenges can arise from this method of navigation, especially when interacting with non-autonomous vehicles.
2017-03-28
Technical Paper
2017-01-0102
Mahdi Heydari, Feng Dang, Ankit Goila, Yang Wang, Hanlong Yang
In this paper, a sensor fusion approach is introduced to estimate lane departure. The proposed algorithm combines the camera, inertial navigation sensor, and GPS data with the vehicle dynamics to estimate the vehicle path and the lane departure time. The lane path and vehicle path are estimated by using Kalman filters. This algorithm can be used to provide early warning for lane departure in order to increase driving safety. By integrating inertial navigation sensor and GPS data, the inertial sensor biases can be estimated and the vehicle path can be estimated where the GPS data is not available or is poor. Additionally, the algorithm can be used to reduce the latency of information embedded in the controls, so that the vehicle lateral control performance can be significantly improved during lane keeping in Advanced Driver Assistance Systems (ADAS) or autonomous vehicles. Furthermore, it improves lane detection reliability in situations when camera fails to detect lanes.
2017-03-28
Technical Paper
2017-01-0110
Hao Sun, Weiwen Deng, Chen Su, Jian Wu
Abstract The ability to recognize traffic vehicles’ lane change maneuver lays the foundation for predicting their long-term trajectories in real-time, which is a key component for Advanced Driver Assistance Systems (ADAS) and autonomous automobiles. Learning-based approach is powerful and efficient, such approach has been used to solve maneuver recognition problems of the ego vehicles on conventional researches. However, since the parameters and driving states of the traffic vehicles are hardly observed by exteroceptive sensors, the performance of traditional methods cannot be guaranteed. In this paper, a novel approach using multi-class probability estimates and Bayesian inference model is proposed for traffic vehicle lane change maneuver recognition. The multi-class recognition problem is first decomposed into three binary problems under error correcting output codes (ECOC) framework.
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
Technical Paper
2017-01-0108
Zaydounr Y. Rawashdeh, Trong-Duy Nguyen, Anoop Pottammal, Rajesh Malhan
Abstract In this work, Dedicated Short Range Communication (DSRC) capabilities combined with classical autonomous vehicles’ on-board sensors (Camera) are used to trigger a Comfortable Emergency Brake (CEB) for urban traffic light intersection scenario. The system is designed to achieve CEB in two phases, the Automated Comfortable Brake (ACB) and the full stop Automated Emergency Brake (AEB). The ACB is triggered first based on the content of the Signal Phase and Timing (SPaT) / Map data (MAP) messages received from the Road Side Unit (RSU) at larger distances. And, once the traffic light becomes in the detection field of view of the camera, the output of the Camera-based Traffic Light Detection (TLD) and recognition software is fused with the SPaT/MAP content to decide on triggering the full stop AEB. In the automated vehicle, the current traffic light color and duration received in the SPaT message is parsed; and compared with the TLD output for color matching.
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
2017-01-0091
Songyao Zhou, Gangfeng Tan, Kangping Ji, Renjie Zhou, Hao Liu
Abstract The mountainous roads are rugged and complex, so that the driver can not make accurate judgments on dangerous road conditions. In addition, most heavy vehicles have characteristics of large weight and high center of gravity. The two factors above have caused most of the car accidents in mountain areas. A research shows that 90% of car accidents can be avoided if drivers can respond within 2-3 seconds before the accidents happen. This paper proposes a speed warning scheme for heavy-duty vehicle over the horizon in mountainous area, which can give the drivers enough time to respond to the danger. In the early warning aspect, this system combines the front road information, the vehicle characteristics and real-time information obtained from the vehicle, calculates and forecasts the danger that may happen over the horizon ahead of time, and prompts the driver to control the vehicle speed.
2017-03-28
Technical Paper
2017-01-0090
Ondrej Santin, Jaroslav Beran, Jaroslav Pekar, John Michelini, Junbo Jing, Steve Szwabowski, Dimitar Filev
Abstract Conventional cruise control systems in automotive applications are usually designed to maintain the constant speed of the vehicle based on the desired set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods namely adopting the Model Predictive Control (MPC) technology utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on the extension of the Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier by application to the trailer tow use-case. As the connected trailer changes the aerodynamic drag and the overall vehicle mass, it may lead to the undesired downshifts for the conventional cruise controller introducing the fuel economy losses. In this work, the ANLMPC concept is extended to avoid downshifts by translating the downshift conditions to the constraints of the underlying optimization problem to be solved.
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
2017-01-0096
Valentin Soloiu, Bernard Ibru, Thomas Beyerl, Tyler Naes, Charvi Popat, Cassandra Sommer, Brittany Williams
Abstract An important aspect of an autonomous vehicle system, aside from the crucial features of path following and obstacle detection, is the ability to accurately and effectively recognize visual cues present on the roads, such as traffic lanes, signs and lights. This ability is important because very few vehicles are autonomously driven, and must integrate with conventionally operated vehicles. An enhanced infrastructure has yet to be available solely for autonomous vehicles to more easily navigate lanes and intersections non-visually. Recognizing these cues efficiently can be a complicated task as it not only involves constantly gathering visual information from the vehicle’s surroundings, but also requires accurate real time processing. Ambiguity of traffic control signals challenges even the most advanced computer decision making algorithms. The vehicle then must keep a predetermined position within its travel lane based on its interpretation of its surroundings.
2017-03-28
Technical Paper
2017-01-0093
Balachander Dhanavanthan
Abstract Radio Frequency (RF) propagation in vehicular environments exhibits major transformations from indoor, outdoor and farmland multipath environments. The innovative advancement in Wireless Sensor Networks (WSNs) has made it necessary to recognise and predict the RF propagation losses for WSNs in vehicular environments. Very few models exist for network planning and deployment in vehicular environments. All of these models need an extensive statistical estimations and an in-depth knowledge of the vehicular environment. In this paper a different approach has been pursued and as a first step is to evaluate the factors which affect RF propagation in vehicular environments and how these factors affect each other while predicting propagation losses in vehicular environments.
2017-03-28
Technical Paper
2017-01-0081
Majid Majidi, Majid Arab, Vahid Tavoosi
Abstract In this research, an optimal real-time trajectory planning method is proposed for autonomous ground vehicles in case of overtaking a moving obstacle. When an autonomous vehicle detects a moving vehicle ahead of it in a proper speed and distance and the braking is not efficient due to the lost of its kinematic energy, the autonomous vehicle decides to overtake the obstacle by performing a double lane-change maneuver. A two-phase nonlinear optimal problem is developed for generating the path for the overtaking maneuver. The cost function of the first phase is defined in such a way that the vehicle approaches the moving obstacle as close as possible. Besides, the cost function of the second phase is defined as the minimization of the sum of the vehicle lateral deviation from the reference path and the rate of steering angle during the overtaking maneuver while the lateral acceleration of the vehicle does not exceed a safe limit.
2017-03-28
Journal Article
2017-01-0083
Yi Tian, Hangxin Liu, Tomonari Furukawa
Abstract This paper presents a novel infrastructural traffic monitoring approach that estimates traffic information by combining two sensing techniques. The traffic information can be obtained from the presented approach includes passing vehicle counts, corresponding speed estimation and vehicle classification based on size. This approach uses measurement from an array of Lidars and video frames from a camera and derives traffic information using two techniques. The first technique detects passing vehicles by using Lidars to constantly measure the distance from laser transmitter to the target road surface. When a vehicle or other objects pass by, the measurement of the distance to road surface reduces in each targeting spot, and triggers detection event. The second technique utilizes video frames from camera and performs background subtraction algorithm in each selected Region of Interest (ROI), which also triggers detection when vehicle travels through each ROI.
2017-03-28
Technical Paper
2017-01-0071
Vahid Taimouri, Michel Cordonnier, Kyoung Min Lee, Bryan Goodman
Abstract While operating a vehicle in either autonomous or occupant piloted mode, an array of sensors can be used to guide the vehicle including stereo cameras. The state-of-the-art distance map estimation algorithms, e.g. stereo matching, usually detect corresponding features in stereo images, and estimate disparities to compute the distance map in a scene. However, depending on the image size, content and quality, the feature extraction process can become inaccurate, unstable and slow. In contrast, we employ deep convolutional neural networks, and propose two architectures to estimate distance maps from stereo images. The first architecture is a simple and generic network that identifies which features to extract, and how to combine them in a multi-resolution framework.
2017-03-28
Technical Paper
2017-01-0072
Yang Zheng, Navid Shokouhi, Amardeep Sathyanarayana, John Hansen
Abstract With the embedded sensors – typically Inertial Measurement Units (IMU) and GPS, the smartphone could be leveraged as a low-cost sensing platform for estimating vehicle dynamics. However, the orientation and relative movement of the smartphone inside the vehicle yields the main challenge for platform deployment. This study proposes a solution of converting the smartphone-referenced IMU readings into vehicle-referenced accelerations, which allows free-positioned smartphone for the in-vehicle dynamics sensing. The approach is consisted of (i) geometry coordinate transformation techniques, (ii) neural networks regression of IMU from GPS, and (iii) adaptive filtering processes. Experiment is conducted in three driving environments which cover high occurrence of vehicle dynamic movements in lateral, longitudinal, and vertical directions. The processing effectiveness at five typical positions (three fixed and two flexible) are examined.
2017-03-28
Journal Article
2017-01-0073
Andreas Barthels, Christian Ress, Martin Wiecker, Manfred Müller
Abstract Vehicle to Vehicle Communication use case performance heavily relies on market penetration rate. The more vehicles support a use case, the better the customer experience. Enabling these use cases with acceptable quality on vehicles without built-in navigation systems, elaborate map matching and highly accurate sensors is challenging. This paper introduces a simulation framework to assess system performance in dependency of vehicle positioning accuracy for matching approach path traces in Decentralized Environmental Notification Messages (DENMs) in absence of navigation systems supporting map matching. DENMs are used for distributing information about hazards on the road network. A vehicle without navigation system and street map can only match its position trajectory with the trajectory carried in the DENM.
2017-03-28
Technical Paper
2017-01-0076
Modar Horani, Ghaith Al-Refai, Osamah Rawashdeh
Abstract Current implementations of vision-based Advanced Driver Assistance Systems (ADAS) are largely dependent on real-time vehicle camera data along with other sensory data available on-board such as radar, ultrasonic, and GPS data. This data, when accurately reported and processed, helps the vehicle avoid collisions using established ADAS applications such as Forward Collision Avoidance (FCA), Autonomous Cruise Control (ACC), Pedestrian Detection, etc. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) over Dedicated Short Range Communication (DSRC) provides basic sensory data from other vehicles or roadside infrastructure including position information of surrounding traffic. Exchanging rich data such as vision data between multiple vehicles, and between vehicles and infrastructure provides a unique opportunity to advance driver assistance applications and Intelligent Transportation Systems (ITS).
2017-03-28
Journal Article
2017-01-0077
Scott E. Bogard, Shan Bao, David LeBlanc, Jun Li, Shaobo Qiu, Bin Liu
Abstract This paper provides an analysis of how communication performance between vehicles using Dedicated Short-range Communication (DSRC) devices varies by antenna mounting, vehicle relative positions and orientations, and between receiving devices. DSRC is a wireless technology developed especially for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. A frequency band near 5.9 GHz has been set aside in the US and other countries for exploring safety and other uses for road vehicles. DSRC devices installed onboard vehicles broadcast their location using global navigation space systems (GNSS), speed, heading, and other information. This can be used to study communication performance in many scenarios including: car-following situations, rear-end crash avoidance, oncoming traffic situations, left turn advisory, head-on crash avoidance and do-not-pass warnings.
Viewing 91 to 120 of 16460