Car-following refers to a control process in which the following vehicle (FV) tries keep safe distance between itself and lead (LV) by adjusting its acceleration response actions of ahead. The corresponding car-following models, describe how one follows another traffic flow, form cornerstone for microscopic simulation intelligent development. One major motivation models is replicate human drive...

Identity management (IdM) not only improves the process of creating and maintaining digital identities across the business systems, but, if implemented successfully it can contribute to the strengthening and positioning of the business for success. In order to lead successful IdM implementation organizations need to step back and determine a course of action that would solve enterprise-wide iss...

Future NASA Earth science missions, including the Earth Observing System (EOS), will generate vast amounts of data that must be processed and stored at various locations around the world. This article presents a stepwiserefinement of the Intelligent Database Management (IDM) system of the Distributed Active Archive Center (DAAC – one of seven regionally-located EOSDIS archive sites) architectur...

This paper reviews and compares car-following and lanechanging logics embedded in some microscopic traffic simulation models, and comments are made on the existing techniques of traffic simulation. Future directions of traffic simulation are identified in three aspects: vehicle modeling, driver modeling, and vehicle movement modeling. To illustrate these ideas, a conceptual model, 2DSIM, is pro...

The paper discusses a series of four user-oriented design analysis problems in a research prototype multimodal spoken language dialogue system for supporting drivers whilst driving. The problems are: (a) when should the system (not) listen to the speech and non-speech acoustics in the car; (b) how to use the in-car display in conjunction with spoken driver-system dialogue; (c) how to identify t...

Car-following behavior has been extensively studied using physics-based models, such as Intelligent Driving Model (IDM). These models successfully interpret traffic phenomena observed in the real world but may not fully capture complex cognitive process of driving. Deep learning on other hand, have demonstrated their power capturing require a large amount driving data to train. This paper aims ...

In the simulation-based testing and evaluation of autonomous vehicles (AVs), how background (BVs) drive directly influences AV’s driving behavior further affects test results. Most existing simulation platforms use either predetermined trajectories or deterministic models to model BV behaviors. However, cannot react AV maneuvers, are different from real human drivers because lack stochastic com...

The Feedback Error Learning (FEL) was found to be applicable to joint angle control by FES in our previous study. However, the inverse dynamics model (IDM) in the FEL-FES controller was not learned appropriately in some cases. In this paper, 4 methods of applying the FEL to FES control were compared in controlling 1-DOF movement of the wrist joint stimulating 2 muscles through computer simulati...

The Feedback Error Learning (FEL) for Functional Electrical Stimulation (FES) controller was examined through computer simulation using a musculoskeletal model. Feedforward controller of the FEL controller consisted of the inverse dynamics model (IDM) and the inverse statics model (ISM) in order to achieve successful learning. The new controller showed better performance in learning of the feed...

Wireless networks between vehicles formed by VANET (Vehicular Ad hoc Networks). VANET incorporates the wireless communication and data sharing capabilities to turn into vehicles as a network topology. VANET is showing great potential in research area. Inter-vehicle communication system in VANET improves traffic safety. VANET consists of high dynamic topology, Intermittent connectivity Patterned...