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Electric vehicles will drive the future, therefore forecasting and simulating ‘transportation electrification’ demand over the coming years has become important for utilities. Ever since electricity was commercialized there has been a need for demand forecasting and simulation because electricity provider's ability to produce energy far exceeds their ability to store energy. This is an industry worth...
This paper aims to present a methodology that helps improving systems modelling by using Process Integration and Design Optimization technologies. Then, the methodology has been applied on an automotive case study: the improvement of the hybrid vehicle TOYOTA PRIUS III modelling. This work has been carried out in the context of the international research program PLACIS (PLAteforme Collaborative d'Ingénierie...
This article introduces a framework to compute decision making policies (mission planning) for a UAV-based delivery system serving impatient customers. Customers arrive on a finite number of locations L separated by arbitrary but fixed distances and eventually leave if not served. Policies seek to minimize the average net cost (maximize the average net revenue), i.e. loss from customers' abandonment...
Much of estimation of human internal state (goal, intentions, activities, preferences, etc.) is passive: an algorithm observes human actions and updates its estimate of human state. In this work, we embrace the fact that robot actions affect what humans do, and leverage it to improve state estimation. We enable robots to do active information gathering, by planning actions that probe the user in order...
We present LOG2MODEL, an approach, supported by a tool, that builds behavioral models from log data. The logged data consists of time series encoding the values of the states of a system observed at discrete time steps. The models generated are Discrete-Time Markov Chains with states and transitions representing the values recorded in the log. The models contain key information that can be visualized...
Future Advanced Driver Assistance Systems (ADAS) require the continuous computation of detailed maps of the vehicle's environment. Due to the high demand of accuracy and the enormous amount of data to be fused and processed, common architectures used today, like single-core processors in automotive Electronic Control Units (ECUs), do not provide enough computing power. Here, emerging embedded multi-core...
In recent years, category-level object detection has gained a lot of attention. In addition to object localization, estimation of the object pose has practical applications in intelligent transportation, autonomous driving and robotics. Parts based models have been used for pose estimation in recent years, but these models depend on manual supervision or require a complex algorithm to locate the object...
In this paper, we present the "Slow Start Problem" in participatory sensing applications where a service is provided based on data collected by participants. The slow start problem refers to the initial stage in participatory sensing service deployment, during which service adoption remains sparse and, hence, the collected data does not offer adequate coverage. Predictive models, learned...
In this paper, we mainly focus on a comparison of three types of dynamic programming based algorithms for optimal and near-optimal solutions of traffic signal control problem. The algorithms are backward dynamic programming (BDP), forward dynamic programming (FDP), and approximate dynamic programming (ADP). The traffic signal control model at isolated intersection is formulated by discrete-time Markov...
Cell transmission model (CTM) is one of the most comprehensive, updatable and easy-to-implement models among the existing macroscopic traffic models that approximates the road conditions for immediate information uphold. For instance, it is believed that minimal interpretation from traffic modeling in crowd-sourced route navigation apps will support for user input information checking and verification...
The limited energy density in batteries has been a challenge for electric vehicle, which indirectly causes range anxiety among the drivers. Range extender (RE) is seen to be a potential solution for this problem. This paper presents a parametric study on the impact of components sizing (RE power and battery capacity) on the range extended electric vehicle (REEV). Urban (MUDC) and highway (HWFET) driving...
This paper employs a computational optimal control framework to develop a mission planning tool for a team of heterogeneous unmanned vehicles conducting a nominal mine countermeasures (MCM) mission. We first describe our motivation for developing vehicle-specific sensor models for unmanned surface and underwater vehicles working collaboratively to detect mines. Next, we describe the sonar detection...
Buses and other vehicles with regular routes and stop patterns are an important application field for hybrid electric drives. Given initial and final desired state of charge (SOC) of the battery, the optimal distribution of power between both sources, battery and engine, can be computed off-line for a known driving cycle. In the case of a range extender (REX) with an engine switched between two operating...
In this paper an improved method for designing electric propulsion systems consisting of e-machine and inverter, based on a co-simulation, is presented. As the mandated emission limits of vehicles are expected to become stricter, advanced design methods are necessary to calculate accurate losses of machine and inverter. The approach contains the influence of inverter effects with acceptable calculation...
Deep learning has attracted great research interest in recent years in many signal processing application areas. However, investigation of deep learning implementations in highly resource-constrained contexts has been relatively unexplored due to the large computational requirements involved. In this paper, we investigate the implementation of a deep learning application for vehicle classification...
Position information of vehicles is a vital requirement in most VANETs (Vehicular ad hoc networks) applications. Thus, positioning is one of the critical issues in VANETs. The existing vehicle localization schemes only consider static estimating problem, while little or no attention is paid to the effect of vehicle movement on localization performance. In this paper, we propose a real-timely vehicle...
The development of railway systems is often supported by a range of tools, each addressing individual, but overlapping concerns such as, e.g., performance or safety analysis. However, it is a challenge for users to organise work-flows; results are often in different, non-aligning data formats; furthermore, tools work on different levels of abstraction from macro to microscopic. Thus, tool integration...
This paper deals with the model of optimal maneuver of units and its implementation in the Tactical Decision Support System (TDSS). The model is designed to be used for planning the optimal movement of units (soldiers, vehicles, unmanned robots) on the battlefield. The paper is separated into three main parts. Firstly, the model of maneuver is discussed; it is divided into five independent layers:...
In this paper a pedestrian-vehicle mixed evacuation model is presented for the large common place which involves both pedestrians and vehicles. The total evacuation time and total saturation degree of the road network are optimized simultaneously. A multi-particle swarm optimization algorithm is proposed to simulate and optimize the interaction and cooperation between pedestrians and vehicles. The...
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