Filtering and modelling for automotive safety systems
Doktorsavhandling, 2011

This thesis makes five important contributions to the development of an automotive safety system: filtering algorithms, three modelling frameworks concerning the usage of radar detections in tracking, vehicle motion, and decision-making for intervention decisions, and finally the implementation architecture. In the filtering context, we have developed a new sigma-point method for estimating the moments of a transformed Gaussian random variable. These estimates are derived from analytical expressions and are based on evaluations of the transforming function. The method is applied to the moment estimation task in a Gaussian filter and the resulting algorithm is denoted the marginalised Kalman filter (MKF). Compared to traditional radar models, ours is specifically designed for vehicle radars, which often yield several measurements from each object. These measurements can provide useful information, such as vehicle orientation, if they are accurately modelled. We introduce a tracking filter using such a sensor model, and show how the complex data association problem can be facilitated by merging similar hypotheses into groups. The presented vehicle motion model includes the control input from the driver. Uncertainties regarding, e.g., driver style, are formally treated with increased prediction accuracy as a result. Similar to this model, the third framework also takes the driver into consideration by allowing interventions only when the driver is believed to accept them. Our evaluations indicate an increased benefit in collision avoidance systems --- particularly in traffic situations where the future trajectory of another road user is hard for the driver to predict. Finally, we present a modular functional design for implementing a real-time data fusion system. We conclude that a tracking system, using modern estimation techniques, is well suited for sensor data fusion in an automotive environment.

decision-making

automotive safety

sensor data fusion

motion models

radar sensor models

filtering theory

moment estimation

ED-salen, Hörsalsvägen 11, Chalmers tekniska högskola
Opponent: Prof. Fredrik Gustafsson, Department of Electrical Engineering, Linköping University, Sweden.

Författare

Fredrik Sandblom

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Styrkeområden

Informations- och kommunikationsteknik

Transport

Fundament

Grundläggande vetenskaper

Drivkrafter

Innovation och entreprenörskap

Ämneskategorier

Sannolikhetsteori och statistik

Annan elektroteknik och elektronik

ISBN

978-91-7385-608-9

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie

ED-salen, Hörsalsvägen 11, Chalmers tekniska högskola

Opponent: Prof. Fredrik Gustafsson, Department of Electrical Engineering, Linköping University, Sweden.