Vehicle predictive trajectory patterns from isochronous data

TL;DR

This paper presents a method for analyzing vehicle trajectory patterns using fused sensor data to predict future driving behaviors and identify hazardous situations, aiding city planning and vehicle design.

Contribution

It introduces a detailed data fusion approach for assessing and mapping isochronous vehicle trajectories, enabling prediction of future paths and hazard detection.

Findings

Trajectory patterns successfully predict future vehicle paths.

Method identifies extreme driving behaviors and hazardous road geometries.

Data can inform city planning and vehicle engineering improvements.

Abstract

Measuring and analyzing sensor data is the basic technique in vehicle dynamics development and with the advancement of embedded and data acquisition systems it is possible to analyze large data sets. In this paper a detailed method is presented for assessing and mapping isochronous trajectory patterns in Graz (Austria) by using data fusion from video, ArduinoUno and the compass sensor HDMM01. The predictive isochronous trajectory patterns are derived from the data values for a predefined time horizon. Both extreme driving behavior and hazardous road geometries can be identified. It is possible to provide instant road sensor data which can be used to compare the data from a trajectory path as well as for different time instances. Results of this study show that the trajectory patterns are successful in predicting the likely evolution of a current trajectory pattern and can provide assessment on future driving situations. The obtained data from this study can be useful as reference in future city planning for energy saving driving pathways as well as vehicle design and engineering improvements based on quantitative and relevant dynamic measurements.