Lateral controller design for an unmanned vehicle via Kalman filtering

This paper proposes a lateral control system for an unmanned vehicle that is designed to improve the responsiveness of the system with the use of a PD control. The vehicle heading error can be stabilized, and the transient response characteristics can be improved using the proposed controller. A mathematical model of the vehicle dynamics using two degrees of freedom was developed for the controller design. The waypoint tracking method for autonomous navigation was tested with incorporation of the Point-to-Point algorithm with position and heading measurements received from GPS receivers via Kalman filtering. The performance of the designed controller was verified through experiments with a real vehicle.     

Travel differences by gender for older people in London

As a guarantee of life quality, older people’s travel demand has to be satisfied, so understanding their travel demand is important. An important point to bear in mind is that older people are not a homogeneous group but that distinguishing between older women and older men in their mobility needs and travel patterns is necessary. Older people’s travel demand needs to be investigated from many different aspects, including trip generation, travel time, and mode choice. In this study, the investigation is focused on older people’s trip chaining, including trip chain complexity, tour composition in a chain.     

Estimating traffic volumes on intercity road locations using roadway attributes,socioeconomic features and other work-related activity characteristics

Traffic volume data are key inputs to many applications in highway design and planning. But these data are collected in only a limited number of road locations due to the cost involved. This paper presents an approach for estimating daily and hourly traffic volumes on intercity road locations combining clustering and regression modelling techniques. With the aim of applying the procedure to any road location, it proposes the use of roadway attributes and socioeconomic characteristics of nearby cities as explanatory variables, together with a set of previously discovered patterns with the hourly traffic percent distribution. Test results show that the proposed approach significantly produces accurate estimates of daily volumes for most locations. The accuracy at hourly level is a bit more reduced but, for periods when traffic is significant, more than half of the estimates are within 20% of absolute percentage error. Moreover, the main peak period is approximately identified for most cases. These findings together with its great applicability make this approach attractive for planners when no traffic data are available and an estimate is helpful.     

Preview-Based Control of Suspension Systems for Commercial Vehicles

An active suspension with preview is developed for the rear axle of a commercial vehicle. The obtained improvements are promising and justify further investigation of the more feasible semi-active suspensions with preview. The inherent non-linearity of semi-active suspensions with switching shock absorbers and the need for controllers that can handle a broad class of control objectives has led to the development of several numerical control methods for both multi-level and continuously variable shock absorbers. The most promising control method and shock absorber type are selected and mounted in a test vehicle. Some first experimental results are discussed.     

Optimization for Vehicle Suspension II: Frequency Domain

The objective of this study is optimizing the components design of a vehicle suspension system under excitation due to road roughness. The vehicle is modelled as a dynamic system made of masses interconnected by, linear, springs and dampers. The optimizing code provides values corresponding to the caracteristics of masses, dampers and springs which, within a range, minimize the objective function for a defined excitation. This objective function auantifies the vehicle comfort level.

The optimization method used is the sequential linear programming by iteratively applying the Simplex algorithm. The model response is obtained in frequency domain and the vehicle excitation can be either random or deterministic.

The exact nature of the optimization problem, objective function and restrictions, depend on the type of excitation considered.

In succeeding paragraphs, the problem formulation together with a comparison with other authors is presented.     

Active Control of Non-stationary Response of Vehicles with Nonlinear Suspensions

Active control of non-stationary response of a single degree of freedom vehicle model with nonlinear passive suspension elements is considered in this paper. The method of equivalent linearization is used to derive the equivalent linear model and the optimal control laws are obtained by using stochastic optimal control theory based on full state information. Velocity squared quadratic damping and hysteresis type of stiffness nonlinearities are considered. The effect of the nonlinearities on the active system performance is studied. The performance of active suspensions with nonlinear passive elements is found to be superior to the corresponding passive suspension systems.     

Automated detection of lane-blocking freeway incidents using artificial neural networks

A major source of urban freeway delay in the U.S. is non-recurring congestion caused by incidents. The automated detection of incidents is an important function of a freeway traffic management center. A number of incident detection algorithms, using inductive loop data as input, have been developed over the past several decades, and a few of them are being deployed at urban freeway systems in major cities. These algorithms have shown varying degrees of success in their detection performance. In this paper, we present a new incident detection technique based on artificial neural networks (ANNs). Three types of neural network models, namely the multi-layer feedforward (MLF), the self-organizing feature map (SOFM) and adaptive resonance theory 2 (ART2), were developed to classify traffic surveillance data obtained from loop detectors, with the objective of using the classified output to detect lane-blocking freeway incidents. The models were developed with simulation data from a study site and tested with both simulation and field data at the same site. The MLF was found to have the highest potential, among the three ANNs, to achieve a better incident detection performance. The MLF was also tested with limited field data collected from three other freeway locations to explore its transferability. Our results and analyzes with data from the study site as well as the three test sites have shown that the MLF consistently detected most of the lane-blocking incidents and typically gave a false alarm rate lower than the California, McMaster and Minnesota algorithms currently in use.     

On the structure of weekly activity/travel patterns

Understanding the process of activity scheduling is a critical pre-requisite to an understanding of changes in travel behavior. To examine this process, a computerized survey instrument was developed to collect household activity scheduling data. The instrument is unique in that it records the evolution of activity schedules from intentions to final outcomes for a weekly period. This paper summarizes an investigation of the structure of activity/travel patterns based on data collected from a pilot study of the instrument. The term “structure” refers to the sequence by which various activities enter one’s daily activity scheduling process. Results of the empirical analyses show that activities of shorter duration were more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Additionally, analysis of travel patterns reveals that many trip-chains were formed opportunistically. Travel time required to reach an activity was positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations.     

Integrating Naval Aviation Maintenance Training with Sailors' Proficiency Qualification and Its Correlation with Improved Weapon System Readiness

A core role for human systems integration is providing support of weapon systems operational effectiveness with optimal human performance (HP). When applied to the readiness of an aviation squadron, this focus on enhanced operational HP translates into ensuring that highly trained and proficient maintenance technicians are resident in these warfighting units. A key objective of the Naval Aviation Enterprise (NAE) Total Force Readiness program is to bridge Core Capabilities to Missions. This is accomplished through the linking of Navy Mission Essential Task Lists (NMETLs) to Skill Sets and Performance Standards for its future workforce. These goals can be accomplished from Initial Development to Deployment of a weapons system through its operational and support phases of the Department of Defense acquisition process using a mission/task-centric approach to Manpower, Personnel, and Training (MPT) requirements, with NMETLs as the operational thread. This paper will describe a life-cycle MPT analytic environment with its task-level links to systems and human engineering disciplines. A systematic approach to accessing and training a proficient maintenance workforce is presented, focusing on task-based maintenance training solutions for optimal unit readiness and mission capabilities. An innovative analytic framework from various skills-based manpower analysis studies will be cited as a means to transform system and unit MPT performance for Total Force Readiness. Enhanced by a Qualified and Proficient Technician methodology, a capability (missions) and competency (proficient) future workforce with skills-efficient maintainers will be best suited to achieving optimal readiness of weapons systems within the NAE.     

Forecasting front displacements with a satellite based ocean forecasting (SOFT) system

Relatively long term time series of satellite data are nowadays available. These spatio–temporal time series of satellite observations can be employed to build empirical models, called satellite based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. The forecast skill of SOFT systems predicting the sea surface temperature (SST) at sub-basin spatial scale (from hundreds to thousand kilometres), has been extensively explored in previous works. Thus, these works were mostly focussed on predicting large scale patterns spatially stationary. At spatial scales smaller than sub-basin (from tens to hundred kilometres), spatio–temporal variability is more complex and propagating structures are frequently present. In this case, traditional SOFT systems based on Empirical Orthogonal Function (EOF) decompositions could not be optimal prediction systems. Instead, SOFT systems based on Complex Empirical Orthogonal Functions (CEOFs) are, a priori, better candidates to resolve these cases.In this work we study and compare the performance of an EOF and CEOF based SOFT systems forecasting the SST at weekly time scales of a propagating mesoscale structure. The SOFT system was implemented in an area of the Northern Balearic Sea (Western Mediterranean Sea) where a moving frontal structure is recurrently observed. Predictions from both SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the implemented SOFT systems are superior in terms of predictability to persistence. No substantial differences have been found between the EOF and CEOF-SOFT systems.