Robust video/ultrasonic fusion-based estimation for automotive applications

In this paper, we use recently developed robust estimation ideas to improve object tracking by a stationary or nonstationary camera. Large uncertainties are always present in vision-based systems, particularly, in relation to the estimation of the initial state as well as the measurement of object motion. The robustness of these systems can be significantly improved by employing a robust extended Kalman filter (REKF). The system performance can also be enhanced by increasing the spatial diversity in measurements via employing additional cameras for video capture. We compare the performances of various image segmentation techniques in moving-object localization and show that normal-flow-based segmentation yields comparable results to, but requires significantly less time than, optical-flow-based segmentation. We also demonstrate with simulations that dynamic system modeling coupled with the application of an REKF significantly improves the estimation system performance, particularly, when subjected to large uncertainties.

CM-based blind equalization of time-varying SIMO-FIR channel with single pulsation estimation

It is known that the constant modulus (CM) property of the source signal can be exploited to blindly equalize time-invariant single-inputmultiple-output (SIMO) and finite-impulse-response (FIR) channels. However, the time-invariance assumption about the channel cannot be satisfied in several practical applications, e.g., mobile communication. In this paper, we show that, under some mild conditions, the CM criterion can be extended to the blind equalization of a time-varying channel that is described by the complex exponential basis expansion model (CE-BEM). Although several existing blind equalization methods that are based on the CE-BEM have to employ higher order statistics to estimate all nonzero channel pulsations, the CM-based method only needs to estimate one pulsation using second-order statistics, which yields better estimation results. It also relaxes the restriction on the source signal and is applicable to some classes of signals with which the existing methods cannot deal.

Unicast and broadcast throughput maximization in amplify-and-forward relay networks

Cooperative communication (CC) offers an efficient and low-cost way to achieve spatial diversity by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of CC. Most existing relay selection strategies focus on optimizing the outage probability or energy consumption. To fill in the vacancy of research on throughput improvement via CC, we study the relay selection problem with the objective of optimizing the throughput in this paper. For unicast, it is a P problem, and an optimal relay selection algorithm is provided with a correctness proof. For broadcast, we show the challenge of relay selection by proving it nonprobabilistic hard (NP-hard). A greedy heuristic algorithm is proposed to effectively choose a set of relay nodes that maximize the broadcast throughput. Simulation results show that the proposed algorithms can achieve high throughput under various network settings.

Stochastic models of road geometry and wind condition for vehicle energy management and control

Modeling and simulation is commonly used to improve vehicle performance, to optimize vehicle system design, and to reduce vehicle development time. Vehicle performances can be affected by environmental conditions and driver behavior factors, which are often uncertain and immeasurable. To incorporate the role of environmental conditions in the modeling and simulation of vehicle systems, both real and artificial data are used. Often, real data are unavailable or inadequate for extensive investigations. Hence, it is important to be able to construct artificial environmental data whose characteristics resemble those of the real data for modeling and simulation purposes. However, to produce credible vehicle simulation results, the simulated environment must be realistic and validated using accepted practices. This paper proposes a stochastic model that is capable of creating artificial environmental factors such as road geometry and wind conditions. In addition, road geometric design principles are employed to modify the created road data, making it consistent with the real-road geometry. Two sets of real-road geometry and wind condition data are employed to propose probability models. To justify the distribution goodness of fit, Pearson's chi-square and correlation statistics have been used. Finally, the stochastic models of road geometry and wind conditions (SMRWs) are developed to produce realistic road and wind data. SMRW can be used to predict vehicle performance, energy management, and control strategies over multiple driving cycles and to assist in developing fuel-efficient vehicles.

Feel bored? Join Verse! Engineering Vehicular Proximity Social Network

Travellers in vehicles often have strong willingness to share their travel experience and exchange information to each other through social networks2, such as Facebook and Twitter. This, however, can be costly due to the limited connections to Internet on the road. In this paper we develop Verse to facilitate the social communications among vehicle travellers on highways. Verse enables passengers on-board vehicles to share the content information, such as travel blogs with pictures, among each other using the impromptu wireless inter-vehicle communications. Unlike traditional online social networks, which are built upon the reliable IP networks, vehicular social networks face fundamental challenges in that: 1) users are anonymous and strangers to each other and hard to identify potential friends of shared interests, and 2) users communicate through intermittent and unreliable inter-vehicle connections. On addressing the two challenges, Verse implements a friend recommendation function, which helps passengers efficiently identify potential social friends with both shared interests and relatively reliable wireless connections. In addition, Verse is equipped with a social-aware rate control scheme towards efficient utilization of network bandwidth. Using extensive simulations, we show that the friend recommendation function of Verse can effectively predict the mobility of vehicles to assist the social communication, and the social-aware rate control scheme quickly and efficiently adapts the vehicle’s transmission rate according to their social impacts.

Engineering a distributed infrastructure for large-scale cost-effective content dissemination over urban vehicular networks

This paper proposes a practical and cost-effective approach to construct a fully distributed roadside communication infrastructure to facilitate the localized content dissemination to vehicles in the urban area. The proposed infrastructure is composed of distributed lightweight low-cost devices called roadside buffers (RSBs), where each RSB has the limited buffer storage and is able to transmit wirelessly the cached contents to fast-moving vehicles. To enable the distributed RSBs working toward the global optimal performance (e.g., minimal average file download delays), we propose a fully distributed algorithm to determine optimally the content replication strategy at RSBs. Specifically, we first develop a generic analytical model to evaluate the download delay of files, given the probability density of file distribution at RSBs. Then, we formulate the RSB content replication process as an optimization problem and devise a fully distributed content replication scheme accordingly to enable vehicles to recommend intelligently the desirable content files to RSBs. The proposed infrastructure is designed to optimize the global network utility, which accounts for the integrated download experience of users and the download demands of files. Using extensive simulations, we validate the effectiveness of the proposed infrastructure and show that the proposed distributed protocol can approach to the optimal performance and can significantly outperform the traditional heuristics.

Joint resource allocation for max-min throughput in multicell networks

We investigate the resource-allocation problem in multicell networks targeting the max-min throughput of all cells. A joint optimization over power control, channel allocation, and user association is considered, and the problem is then formulated as a nonconvex mixed-integer nonlinear problem (MINLP). To solve this problem, we proposed an alternating-optimization-based algorithm, which applies branch-and-bound and simulated annealing in solving subproblems at each optimization step. We also demonstrate the convergence and efficiency of the proposed algorithms by thorough numerical experiments. The experimental results show that joint optimization over all resources outperforms the restricted optimization over individual resources significantly.

Asymptotic throughput capacity analysis of VANETs exploiting mobility diversity

Vehicular ad hoc networks (VANETs) rely on intervehicle relay to extend the communication range of individual vehicles for message transmissions to roadside units (RSUs). With the presence of a large number of quickly moving vehicles in the network, the end-to-end transmission performance from individual vehicles to RSUs through intervehicle relaying is, however, highly unreliable due to the violative intervehicle connectivity. As an effort toward this issue, this paper develops an efficient message routing scheme that can maximize the message delivery throughput from vehicles to RSUs. Specifically, we first develop a mathematical framework to analyze the asymptotic throughput scaling of VANETs. We demonstrate that in an urban-like layout, the achievable uplink throughput per vehicle from vehicle to RSUs scales as Θ(1/ log n) when the number of RSUs scales as Θ(n/log n) with n denoting vehicle population. By noting that the network throughput is bottlenecked by the unbalanced data traffic generated by hotspots of realistic urban areas, which may overload the RSUs nearby, a novel packet-forwarding scheme is proposed to approach the optimal network throughput by exploiting the mobility diversity of vehicles to balance the data traffic across the network. Using extensive simulations based on realistic traffic traces, we demonstrate that the proposed scheme can improve the network throughput approaching the asymptotic throughput capacity.

Practical asynchronous neighbor discovery in ad hoc networks with directional antennas

Neighbor discovery is a crucial step in the initialization of wireless ad hoc networks. When directional antennas are used, this process becomes more challenging since two neighboring nodes must be in transmit and receive states, respectively, pointing their antennas to each other simultaneously. Most of the proposed neighbor discovery algorithms only consider the synchronous system and cannot work efficiently in the asynchronous environment. However, asynchronous neighbor discovery algorithms are more practical and offer many potential advantages. In this paper, we first analyze a one-way handshake-based asynchronous neighbor discovery algorithm by introducing a mathematical model named 'Problem of Coloring Balls.' Then, we extend it to a hybrid asynchronous algorithm that leads to a 24.4% decrease in the expected time of neighbor discovery. Compared with the synchronous algorithms, the asynchronous algorithms require approximately twice the time to complete the neighbor discovery process. Our proposed hybrid asynchronous algorithm performs better than both the two-way synchronous algorithm and the two-way asynchronous algorithm. We validate the practicality of our proposed asynchronous algorithms by OPNET simulations.

Error performance analysis of a clustered multiway relay network

In this paper, we propose a new clustered structure for a multiway relay network (MWRN) with G clusters, N users per cluster, one intracluster relay per cluster, and a single intercluster relay. The proposed structure allows private information exchange among users within a certain cluster through the corresponding intracluster relay and only public information exchange among users in different clusters through the intercluster relay.In this paper, we quantify the dominating error events in the proposed clustered MWRN and derive the expressions for the probability of these error events. Then, we use these expressions to derive the average bit error rate (BER) of a clustered MWRN. It is shown that clustering in an MWRN improves the error performance by reducing the number of dominating error events and, in effect, reducing error propagation, compared with the nonclustered counterpart. The analysis proves that the average BER of a clustered MWRN is minimized when the number ofclusters and the number of users per cluster are chosen to be the closest possible factors of the total number of users, i.e., L = GN. Finally, numerical simulation results are provided to verify the validity of the analysis.