A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Accident Analysis of Ice Control Operations

Highway maintenance involves all work necessary to assure that the highway system is kept safe, open to traffic, and in proper working order. During winter, the removal of snow and ice from streets, roads, and highways is a major maintenance operation. With more than 135 million motor vehicles registered in the United States and roughly four million miles of roads and streets, local governments must be prepared to deal with the removal of snow and ice to insure public safety, and to reduce the adverse impact on the affected area’s economy. Snow and ice covered roads can paralyze the functioning of the community and pose a considerable threat to the public safety. They produce hazardous driving conditions which increase traffic deaths, injuries, and property damage. The general assumption has always been that snow and ice on highways causes accidents. There are a number of reasons for this assumption. Snow and ice reduce the coefficient of friction between the pavement and vehicle tires, making maneuvering of the vehicle very difficult and occasionally impossible. Ice is not always apparent to the motorist and is not uniform, so that the driver is not always prepared when he encounters an icy section on the roadway. Vehicle mobility is reduced, causing possible severe disruption of important public emergency services, such as fire, police, and ambulance operations. Without close attention to the effective removal of snow and ice from roads, the economy of the region involved will suffer, and traffic accidents will escalate. Most activities of individuals, industries, utilities, schools, and government activities are handicapped in social and economic ways during the duration of snow and ice conditions on roads and streets

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

A BAYESIAN NETWORK APPROACH TO BATTERY AGING IN ELECTRIC VEHICLE TRANSPORTATION AND GRID INTEGRATION

Nowadays, batteries in electric vehicles (EVs) are facing a variety of tasks in their connection to the power grid in addition to the main task, driving. All of these tasks play a very significant role in the battery aging, but they are highly variable due to the change in the driver behavior, grid connection availability and weather conditions. The effect of these external factors in the battery degradation have been studied in literature by mostly deterministic and some stochastic approaches, but limited to specific cases. In this dissertation, first, a large-scale deterministic approach is implemented to evaluate the effect of variations in the EV battery daily tasks. To do so, a software tool named REV-Cycle is developed to simulate the EV powertrain and studied the effect of driving behavior, recharging facilities and timings, grid services and temperature/weather change effects, one by one. However, there are two main problems observed in the deterministic aging evaluation: First, the battery capacity fade factors such as temperature, cycling current, state of charge (SOC) … are dependent to the external variables such as location, vehicle owner’s behavior and availability of the grid connection. Therefore, it is not possible to accurately evaluate the battery degradation with a deterministic model, while its inputs are stochastic. Second, the battery aging factors’ dependency is hierarchical and it is not easy to follow and implement this hierarchy with deterministic models. Therefore, using a hierarchical probabilistic framework is proposed that can better represent the problem and realized that the Bayesian statistics with Markov Chain Monte Carlo (MCMC) can provide the problem solving structure needed for this purpose. A comprehensive hierarchical probabilistic model of the battery capacity fade is proposed using Hierarchical Bayesian Networks (HBN). The model considers all uncertainties of the process including vehicle acceleration and velocity, grid connection for charging and utility services, temperatures and all unseen intermediate variables such as battery power, auxiliary power, efficiencies, etc. and estimates the capacity fade as a probability distribution. Metropolis-Hastings MCMC algorithm is applied to generate the posterior distributions. This modeling approach shows promising result in different case studies and provides more informative evaluation of the battery capacity fade

The Impact of Driver Cell Phone Use on Accidents

Cell phone use is increasing worldwide, leading to a concern that cell phone use while driving increases accidents. Several countries, three states and Washington, D.C. have banned the use of hand-held cell phones while driving. In this paper, we develop a new approach for estimating the relationship between cell phone use while driving and accidents. Our approach is the first to allow for the direct estimation of the impact of a cell phone ban while driving. It is based on new survey data from over 7,000 individuals. This paper differs from previous research in two significant ways: first, we use a larger sample of individual-level data; and second, we test for selection effects, such as whether drivers who use cell phones are inherently less safe drivers, even when not on the phone. The paper has two key findings. First, the impact of cell phone use on accidents varies across the population. This result implies that previous estimates of the impact of cell phone use on risk for the population, based on accident-only samples, may be overstated by about one-third. Second, once we correct for endogeneity, there is no significant effect of hands-free or hand-held cell phone use on accidents.

The Impact of Driver Cell Phone Use on Accidents

Cell phone use is increasing worldwide, leading to a concern that cell phone use while driving increases accidents. Several countries, three states and Washington, D.C. have banned the use of hand-held cell phones while driving. In this paper, we develop a new approach for estimating the relationship between cell phone use while driving and accidents. Our approach is the first to allow for the direct estimation of the impact of a cell phone ban while driving. It is based on new survey data from over 7,000 individuals. This paper differs from previous research in two significant ways: first, we use a larger sample of individual-level data; and second, we test for selection effects, such as whether drivers who use cell phones are inherently less safe drivers, even when not on the phone. The paper has two key findings. First, the impact of cell phone use on accidents varies across the population. This result implies that previous estimates of the impact of cell phone use on risk for the population, based on accident-only samples, may be overstated by about one-third. Second, once we correct for endogeneity, there is no significant effect of hands-free or hand-held cell phone use on accidents.