A Hybrid Particle Approach for GNSS Applications With Partial GPS Outages

A method to estimate the instantaneous fuel consumption of a personal car, using speed and height data recorded by a global positioning system receiver and vehicle parameters accessible via national vehicle registers and databases on the world wide web, is proposed. The method is based upon a physical model describing the relationship between the dynamics of the car, engine speed, and energy consumption of the system. An evaluation of the proposed method is done by comparing the estimated instantaneous fuel consumption with that measured by the car's onboard diagnostics data bus. The results of three tests with different cars driven in mixed highway and urban conditions indicate that the instantaneous fuel consumption may be estimated with a root mean square error of ∼0.3 [g/s], in terms of a normalized mean square error, that corresponded to slightly <10%. One application of the proposed method is in the development of smartphone applications that educate drivers to drive more fuel efficiently.Index Terms-Acceleration measurement, energy measurement, engines, fuels, global positioning system, velocity measurement.1 A fuel consumption map is a 3-D plot of the specific fuel consumption versus the engine rotation speed and brake torque. 0018-9456

Section: Calculating the Model Inputs From Gps-datamentioning

confidence: 99%

Indirect Instantaneous Car-Fuel Consumption Measurements

Skog

Händel

2014

“…This has become problematic in intelligent transport systems in which the digital road map is one of the essential parts to ensure a continuous and accurate location of vehicles [1]. For instance, it is known that navigation systems need an up-to-date road map to manage GPS outages [2]- [4].…”

Section: Overviewmentioning

confidence: 99%

Automatic Detection of Topological Changes for Digital Road Map Updating

Boucher

Noyer

2012

Self Cite

This paper highlights a method to detect topological changes of digital road maps automatically. Here, the road network updating can be done in real time. It relies on collected GPS data from vehicles. These data are fused with existing road map data by an unscented Kalman filter in a centralized scheme. We modeled the map data as a sensor that allows accounting for the errors and uncertainties of land surveying. The core of our method is a probabilistic map-matching approach that is used to manage the road network database through the computation of the Mahalanobis distance. We show experimental results from an urban transport network scenario in which we deal with the case of opened roads and the recent conversion of a crossroad into a roundabout. Then, the obsolete map database is updated to ensure a more reliable route planning.Index Terms-Digital road map management, globalnavigation-satellite-system-based navigation, land-vehicle localization, multisensor fusion, nonlinear filtering.

“…1) Equip the Vehicle With Sensors: Global position systems (GPSs) have been widely used for localization and navigation, but they suffer from position errors in the order of meters and are difficult to use in indoor applications [4]. Inertial measurement units (IMU) can also be used, but they generate cumulative errors due to the integral of acceleration as the velocity error is a function of time [5].…”

Section: Introductionmentioning

confidence: 99%

Velocity Estimation for Autonomous Vehicles Based on Image Analysis

Lins

Givigi

Kurka

2016

Stereo vision is widely used in many 3-D image applications. In this paper, a system based on stereo vision concepts has been developed with the goal to measure the velocity of vehicles using images. Using this method, an autonomous vehicle is able to measure its velocity using only a single camera. From a sequence of images, the method uses techniques to locate homologous points and then estimates the velocity of the vehicle. The experimental results validate the application of the proposed method to autonomous vehicles.