Los Angeles, CA, United States

University of Southern California

www.usc.edu
Los Angeles, CA, United States

The University of Southern California is a private, not-for-profit, nonsectarian, research university founded in 1880 with its main campus in the city area of Los Angeles, California. As California's oldest private research university, USC has historically educated a large number of the region's business leaders and professionals. In recent decades, the university has also leveraged its location in Los Angeles to establish relationships with research and cultural institutions throughout Asia and the Pacific Rim. In 2011, USC was named among the Top 10 Dream Colleges in the nation. It holds a vast array of trademarks and wordmarks to the term "USC."For the 2012-2013 academic year, there were 18,316 students enrolled in four-year undergraduate programs. USC is also home to 21,642 graduate and professional students in a number of different programs, including business, law, social work, and medicine. The university has a "very high" level of research activity and received $560.9 million in sponsored research from 2009 to 2010. USC sponsors a variety of intercollegiate sports and competes in the National Collegiate Athletic Association as a member of the Pacific-12 Conference. Members of the sports teams, the Trojans, have won 100 NCAA team championships, ranking them third in the nation, and 378 NCAA individual championships, ranking them second in the nation. Trojan athletes have won 287 medals at the Olympic games , more than any other university in the world. If USC were a country, it would rank 12th in most Olympic gold medals. Wikipedia.

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News Article | December 1, 2015
Site: news.yahoo.com

Jeffrey Miller, IEEE member and associate professor of engineering practice at the University of Southern California, contributed this article to Live Science's Expert Voices: Op-Ed & Insights. Three years ago, Nissan was the first car manufacturer to announce they would have driverless vehicles ready for consumer adoption by the year 2020. While consumers, and even some experts in the field, noted that this was an aggressive timeline, it didn't seem like an unattainable goal. Void of personal and professional opinions, this announcement did a great service for the driverless vehicle industry, promoting awareness of this emerging technology. Awareness is one of the most important elements in driving this industry forward — consumers aren't going to trust what they don't know, even if the technology has been validated. In late August, IEEE —the world's largest professional organization of engineers — hosted a roundtable at the University of Southern California to discuss the current condition and future development of the autonomous vehicle industry. The roundtable featured experts from a variety of disciplines, including technology, policy/regulation and law, where we addressed comprehensive industry considerations. Along with myself, the participants included: Justin Pritchard — moderator; transportation reporter for the Associated Press Wei-Bin Zhang — research engineer and a program manager for the California PATH Program and Institute of Transportation Studies of University of California at Berkeley Bryant Walker Smith — assistant professor of law at the University of South Carolina A new vision for "seeing" the world One area that is continuing to grow and will play a large role in the further development of autonomous vehicles is Vehicle-to-Vehicle ( V2V ) and Vehicle-to-Infrastructure (V2I) communications. Currently, self-driving vehicles are guided by computer vision technology — whether it's Lidar/Ridar (laser or radar technology) or camera-based sensing — when operating on public roads. However, V2V and V2I are communication methods that will completely transform how vehicles will "see" the road and interact with its environment. Both V2V and V2I are dedicated short range communications (DSRC) devices that work in the 5.9GHz band, have a range of approximately 1000m and can support private data communications as well as public. At the rate the industry is moving, we'll start to see V2V /V2I become integrated and tested in controlled settings within the next three to five years, but the technology will require constant evaluation before being available to consumers. Although driverless cars will be on the market by 2020, they will not be able to leverage V2V or V2I until a few years later. [5 Ways Self-Driving Cars Will Make You Love Commuting] V2V and V2I communications will have large-scale benefits that reach beyond the vehicle. Such communication practices will allow for much safer travel by allowing vehicles to be in constant communication with each other as well as their environment, which will greatly reduce accidents and fatalities. Last May, the Associated Press reported on a National Highway Traffic Safety Administration study that found traffic accidents cost the US $871 billion a year — these communication platforms can greatly reduce this number. As a result, traffic patterns and road congestion will also be aided and vehicles will be able to travel at a much faster rate of speed and eventually render traffic signals irrelevant. Key to implementation will be a high penetration rate of vehicles able to communicate with each other. This will enable self-driving cars to access further data and information regarding their environment, and will work in harmony with already available sensing technology  (radar or video cameras). For example, when a vehicle is coming up to a blind intersection, a vehicle in the perpendicular direction could alert other vehicles to whether it will be able to stop as a signal changes. The next five years will be important in addressing concerns and barriers to the implementation of V2V and V2I. In August, the National Highway Traffic Safety Administration (NHTSA) issued a release that announced proposed rule-making and an initial analysis of V2V communication. The agency's primary concerns were technical feasibility, privacy/security, estimates of cost and safety benefits. Like driverless vehicles, communication standards will take some time to gain consumer trust, but eventually they will make their way into the mainstream to compliment the progress of this industry. [Rules for Self-Driving Cars in Legal Gray Area ] Below are excerpts from IEEE's related roundtable discussion, which you can watch in full in this video.   Jeffrey Miller: The world as we know it now is driven by wireless technologies. Most people have cellphones, we have wireless internet connections, and there are a lot of different technologies that are in use there. Having vehicles that are able to talk to each other or are able to communicate with the road way is nothing new, it's the next logical progression that we have.There are cars already that act as hotspots and communicate on the cellular network and they provide Internet access to the passengers of the vehicle. So vehicle-to-vehicle technology is just allowing two vehicles that are within proximity of each other the ability to communicate. This is something that is not difficult to do, we have short range personal networks like Bluetooth, we have dedicated short range communication, there's even cellular providers who are saying that we don't need to have the vehicles with each other over an add-hock network but perhaps they still communicate through the infrastructure and when they hit on of the base stations it comes back to communicate with one of the vehicles that are in close proximity to it. So the technology seems like it's there, we're going to need to increase the bandwidth and we are constantly improving the networks that we have but that's some of the technologies that are used for V2V . Justin Prichard: I'd like to shift gears a little bit here, one of the other aspects of these technological advances, is vehicle-to-infrastructure communication. In other words, a car might talk to a sensor on a stoplight or a sign on the side of the road. I'd like you to talk about where we are with that. Wei-Bin Zhang: As we were talking, most of the car manufactures now are talking about autonomous vehicles — meaning that they place intelligence on to the vehicle and have that vehicle detect everything a driver does and be able to react like a driver does. When we design an automated system, even autonomous vehicle, it does work with infrastructure, it works with land markings, it works with signs and signals and so forth. If we take a step back and take a look to say do we need to have the autonomous vehicle to totally duplicate the driver because we know that there are some limitations of drivers and the car is currently designed to somehow coop with those limitation. Automated vehicles could potentially overcome those. Giving you some examples, you already mentioned signals that are talking to cars and knowing when a signal is going to change, now to actually see the change. But also it can potentially place some infrastructure sensors, you can detect the places where the typical sensor with line of sight limitations would not be able to see. There are a lot of things that can be done. Even you could potentially build up the physical infrastructures allowing some of these problems to be isolated — other vehicles intruding or so forth. It is very important, specifically here with vehicle-to-infrastructure communication, where it is being looked at by the community currently and the USDOT, for example is leading the effort to do the connected vehicles and began making an effort earlier to this year to define, basically, what is the role of vehicle-to-infrastructure communication? Follow all of the Expert Voices issues and debates — and become part of the discussion — on Facebook, Twitter and Google+. The views expressed are those of the author and do not necessarily reflect the views of the publisher. This version of the article was originally published on Live Science. Self-Driving Cars and Teleportation: What Americans Expect from Future Science Self-driving Cars and Autonomous Robots: Where to Now? (Op-Ed) Copyright 2015 LiveScience, a Purch company. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.


Yerramalli S.,University of Southern California | Stojanovic M.,Northeastern University | Mitra U.,University of Southern California
IEEE Transactions on Signal Processing | Year: 2012

Employing Orthogonal Frequency Division Multiplexing (OFDM) signaling over time-varying channels results in inter-carrier interference (ICI) and degraded detection error probability due to the loss of orthogonality among the subcarriers. This problem is particularly exacerbated for systems operating in highly mobile scenarios such as underwater acoustic (UWA) communications, digital video broadcasting (DVB) for mobile devices and vehicle-to-vehicle (V2V) networks. To address the problem of data detection in such scenarios, we propose a novel demodulation strategy using several partial interval Fast Fourier Transforms (FFTs) instead of a conventional, single full interval FFT. Algorithms for computing the weights used to combine the outputs of the partial FFT are presented for three scenarios: full, partial and no knowledge of the time varying channel. Numerical simulations and an approximate theoretical analysis show that significant performance gains can be obtained over traditional equalizers at a very moderate complexity. © 2012 IEEE.

Document Keywords (matching the query): vehicle to vehicles, intercarrier interference, sub carriers.


Karedal J.,Lund University | Czink N.,FTW Forschungszentrum Telekommunikation Wien GmbH | Paier A.,Vienna University of Technology | Tufvesson F.,Lund University | Molisch A.F.,University of Southern California
IEEE Transactions on Vehicular Technology | Year: 2011

Vehicle-to-vehicle ( V2V ) communications have received increasing attention lately, but there is a lack of reported results regarding important quantities such as path loss. This paper presents parameterized path loss models for V2V communications based on extensive sets of measurement data collected mainly under line-of-sight conditions in four different propagation environments: highway, rural, urban, and suburban. The results show that the path loss exponent is low for V2V communications, i.e., path loss slowly increases with increasing distance. We compare our results to those previously reported and find that, while they confirm some of the earlier work, there are also differences that motivate the need for further studies. © 2010 IEEE.

Document Keywords (matching the query): vehicles, vehicle to vehicle communication.


Beygi S.,University of Southern California | Mitra U.,University of Southern California | Strom E.G.,Chalmers University of Technology
IEEE Transactions on Signal Processing | Year: 2015

Future intelligent transportation systems promise increased safety and energy efficiency. Realization of such systems will require vehicle-to-vehicle ( V2V ) communication technology. High fidelity V2V communication is, in turn, dependent on accurate V2V channel estimation. V2V channels have characteristics differing from classical cellular communication channels. Herein, geometry-based stochastic modeling is employed to develop a characterization of V2V channel channels. The resultant model exhibits significant structure; specifically, the V2V channel is characterized by three distinct regions within the delay-Doppler plane. Each region has a unique combination of specular reflections and diffuse components resulting in a particular element-wise and group-wise sparsity. This joint sparsity structure is exploited to develop a novel channel estimation algorithm. A general machinery is provided to solve the jointly element/group sparse channel (signal) estimation problem using proximity operators of a broad class of regularizers. The alternating direction method of multipliers using the proximity operator is adapted to optimize the mixed objective function. Key properties of the proposed objective functions are proven which ensure that the optimal solution is found by the new algorithm. The effects of pulse shape leakage are explicitly characterized and compensated, resulting in measurably improved performance. Numerical simulation and real V2V channel measurement data are used to evaluate the performance of the proposed method. Results show that the new method can achieve significant gains over previously proposed methods. © 2015 IEEE.

Document Keywords (matching the query): amphibious vehicles, stochastic models, stochastic systems, vehicle to vehicle communications.


Zemen T.,Telecommunications Research Center Vienna | Molisch A.F.,University of Southern California
IEEE Transactions on Vehicular Technology | Year: 2012

In this paper, we focus on adaptive time-variant channel estimation for vehicle-to-vehicle ( V2V ) communications in intelligent transportation systems (ITS) using the IEEE 802.11p physical layer. The IEEE 802.11p pilot pattern is identical to that in the well-known IEEE 802.11a/g (WiFi) standard, which was initially designed for indoor environments with little or no mobility. However, in a V2V drive-by situation, the channel impulse response rapidly changes due to the high relative velocity between transmitter and receiver, as well as the changes in the scattering environment. Hence, for such V2V channels, advanced decision directed channel estimation methods are needed to reach a frame error rate (FER) smaller than 10-1. Even more importantly, the channels are nonstationary, which implies that the Doppler power spectral density (DSD) and the power delay profile (PDP) change on a timescale comparable with the frame length, which complicates the estimator design. In this paper, we develop an adaptation method for the channel estimation filter that is suitable for the following: 1) the short frame length in IEEE 802.11p; 2) the given pilot structure; and 3) the requirement of only a single received short frame for filter adaptation. We define a set of hypotheses on the support of the DSD and a second set of hypotheses on the support of the PDP. Each hypothesis is represented by a specific subspace spanned by orthogonal basis vectors. For basis vector calculation, we develop a numerically stable algorithm utilizing generalized discrete prolate spheroidal sequences. The adaptation algorithm chooses a hypothesis from both sets such that a probabilistic bound on the channel estimation error is minimized. We implement the hypothesis test by means of a novel subspace selection algorithm that allows utilizing correlated observations of a time- and frequency-selective (2-D) fading process. We validate the adaptive channel estimation scheme in an IEEE 802.11p compliant link level simulation for a relative velocity range from 0 to 111 m/s ≈ 400 km/h ≈ 249 mi/h. Adaptive filtering enables an up to fourfold reduction in the number of required iterations to reach an FER below 10-1 for an Eb/N0 = 12 dB. © 1967-2012 IEEE.

Document Keywords (matching the query): vectors, vehicle locating systems.


Beygi S.,University of Southern California | Strom E.G.,Chalmers University of Technology | Mitra U.,University of Southern California
2014 IEEE Global Communications Conference, GLOBECOM 2014 | Year: 2014

In this paper, we consider the estimation of a signal that has both group- and element-wise sparsity (joint sparsity); motivated by channel estimation in vehicle-to-vehicle channels. A general approach for the design of separable regularizing functions is proposed to adaptively induce sparsity in the estimation. A joint sparse signal estimation problem is formulated via these regularizers and its optimal solution is computed based on proximity operations. Our optimization results are quite general and they can be applied in the context of hierarchical sparsity models as well. The proposed recovery algorithm is a nested iterative method based on the alternating direction method of multipliers (ADMM). Due to regularizer separability, key operations can be performed in parallel. V2V channels are estimated by exploiting the joint sparsity (group/element-wise) exhibited in the delay-Doppler domain. Simulation results reveal that the proposed method can achieve as much as a 10 dB gain over previously examined methods. © 2014 IEEE.

Document Keywords (matching the query): vehicle to vehicles.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 350.00K | Year: 2012

Recent developments in the automotive industry point to a new emerging domain of vehicular wireless networks, in which vehicles equipped with radios can communicate a wide range of information to each other and the wider Internet, including traffic and safety updates as well as infotainment content. The primary goal of this project is to develop a hybrid network architecture for such vehicular networks which combines both the existing cellular infrastructure as well as new vehicle-to-vehicle ( V2V ) communication capabilities. The hypothesis is that such a hybrid network architecture will improve cost, capacity and robustness, compared to either a purely centralized cellular-based approach or a purely distributed V2V approach. Under a hybrid architecture, the project aims to design information-centric protocols for information dissemination, aggregation, and storage, that can exploit the spatio-temporally localized nature of vehicular applications. Further, through mathematical analysis, computer simulations, as well as experimental implementation on a research fleet of vehicles, this project aims to evaluate the performance of these protocols.

This project will be a unique academia-industry collaborative project between researchers at the University of Southern California and General Motors. While the focus will very much be on basic research disseminated to the academic community through publications, the close interaction with a prominent industry partner will enable the research to have a strong impact on real-world vehicular networks. Material from this research project will be incorporated into graduate courses at USC. The aimed-for advance in information technology for the automotive domain could have significant social impact by enabling improvements in traffic safety, efficiency, user comfort and productivity.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 479.35K | Year: 2011

The oceans cover 71% of the earths surface and represent one of the least explored frontiers, yet the oceans are integral to climate regulation, nutrient production, oil retrieval, and transportation. Future scientific and technological efforts to achieve better understanding of oceans and water-related applications will rely heavily on our ability to communicate reliably between instruments, vehicles (manned and unmanned), human operators, platforms, and sensors of all types. The glue of such underwater networks will be the underwater acoustic communication link. Despite recent activity on acoustic physical layer single-links, digital acoustic communication is in its infancy in comparison to comparable efforts for radio-based terrestrial wireless communication. This project exploits the unique features of the underwater acoustic channel to significantly improve performance of underwater networks.

The research focus is on design for the inherently multi-scale, multipath channels in this wideband environment. Surprisingly, transceiver design for the coupled efforts of wideband channels and distinct Doppler scales for practical underwater communication systems does not appear to have been thoroughly investigated. The network is examined from the perspective of individual links up to entire networks of multiple nodes, encompassing novel multi-scale, multi-lag channel modeling, waveform design, equalizer designs, channel estimation, single-link and network capacity evaluation, novel coding to achieve capacity, as well as signaling and routing over large-scale networks. In a principled manner, the fundamental properties of multi-scale, multipath channels are explored in order to design and analyze high performance underwater acoustic networks. Implementable algorithms are designed which endeavor to achieve the fundamental limits. The research outcomes will also impact ultrasound, ultrawideband, wideband radar, sonar, acoustic signal processing, and moderate to high speed vehicle-to-vehicle (V2V) communication and possibly understanding of biosonar.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 484.21K | Year: 2014

The ability to simulate complex machinery in contact is broadly applicable to engineering practice. It can be used for virtual training, say in the operation of heavy machinery. Perhaps most importantly, it can be used to assemble and test complex mechanical structures in virtual reality (using a human-computer interface that includes haptic feedback). Such virtual prototyping, as it is commonly called, greatly shortens design cycles, decreases errors, improves product safety and saves millions of dollars in R&D costs. Applications can be found anywhere a complex structure must be designed and manufactured out of many component parts: airplanes, cars, trains, spaceships, power plants, buildings, tools, heavy equipment, etc. In this project the PI will develop computationally efficient collision detection and contact resolution methods that can accommodate complex systems consisting of many objects that are connected by joints and undergoing contact and self-contact. His goal is to devise algorithms that are sufficiently fast to accommodate high update rates (1,000 simulation steps per second for haptics, or more), and that scale to complex real-world mechanisms typically represented by millions of triangles, such as an internal combustion engine or an entire car engine compartment, an airplane landing gear or airplane doors, or excavator machines. Furthermore, whereas previous fast successful industrial penalty-based methods have typically been limited to pairs of objects in contact, in this research the PIs objective is to deal with more complex and realistic situations including rigid objects, joints, friction and self-contact.

Fast simulation of multi-body systems in contact is challenging due to the severe computational and stability requirements imposed by complex geometry. Such simulations frequently involve distributed contact, that is to say contact involving many collision sites of varying surface areas and normal orientations that change rapidly over time. Because it is challenging for constraint-based methods to resolve such contact stably at high update rates, the Principal Investigator will exploit industry-proven penalty methods between points and implicit functions (distance fields or voxmaps), and he will extend the approach, which has to date been limited to pairs of objects in contact, to accommodate N >= 2 objects in arbitrary contact, as well as objects connected with joints and undergoing active control. The technical challenges include how to stably resolve and time-step distributed contact between N >= 2 objects, how to stably simulate and render 6-DOF distributed contact in the presence of constraints (joints), and how to handle self-contact and incorporate friction, all the while maintaining high update rates (or gracefully degrading them in case of extreme contact). Because the Principal Investigators preliminary experience suggests that the discrete nature of current algorithms is an important limitation in practice, he will also investigate continuous collision detection between points and distance fields. Project outcomes will be transitioned to engineering practice via the PIs ongoing collaborations with a number of industrical leaders in high-tech virtual prototyping, and will advance the state of the art in computer graphics, haptics, robotics and virtual reality.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: CYBER-PHYSICAL SYSTEMS (CPS) | Award Amount: 240.00K | Year: 2015

The automotive industry finds itself at a cross-roads. Current advances in MEMS sensor technology, the emergence of embedded control software, the rapid progress in computer technology, digital image processing, machine learning and control algorithms, along with an ever increasing investment in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies, are about to revolutionize the way we use vehicles and commute in everyday life. Automotive active safety systems, in particular, have been used with enormous success in the past 50 years and have helped keep traffic accidents in check. Still, more than 30,000 deaths and 2,000,000 injuries occur each year in the US alone, and many more worldwide. The impact of traffic accidents on the economy is estimated to be as high as $300B/yr in the US alone. Further improvement in terms of driving safety (and comfort) necessitates that the next generation of active safety systems are more proactive (as opposed to reactive) and can comprehend and interpret driver intent. Future active safety systems will have to account for the diversity of drivers skills, the behavior of drivers in traffic, and the overall traffic conditions.

This research aims at improving the current capabilities of automotive active safety control systems (ASCS) by taking into account the interactions between the driver, the vehicle, the ASCS and the environment. Beyond solving a fundamental problem in automotive industry, this research will have ramifications in other cyber-physical domains, where humans manually control vehicles or equipment including: flying, operation of heavy machinery, mining, tele-robotics, and robotic medicine. Making autonomous/automated systems that feel and behave naturally to human operators is not always easy. As these systems and machines participate more in everyday interactions with humans, the need to make them operate in a predictable manner is more urgent than ever.

To achieve the goals of the proposed research, this project will use the estimation of the drivers cognitive state to adapt the ASCS accordingly, in order to achieve a seamless operation with the driver. Specifically, new methodologies will be developed to infer long-term and short-term behavior of drivers via the use of Bayesian networks and neuromorphic algorithms to estimate the drivers skills and current state of attention from eye movement data, together with dynamic motion cues obtained from steering and pedal inputs. This information will be injected into the ASCS operation in order to enhance its performance by taking advantage of recent results from the theory of adaptive and real-time, model-predictive optimal control. The correct level of autonomy and workload distribution between the driver and ASCS will ensure that no conflicts arise between the driver and the control system, and the safety and passenger comfort are not compromised. A comprehensive plan will be used to test and validate the developed theory by collecting measurements from several human subjects while operating a virtual reality-driving simulator.

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