Location Estimation and Trajectory Prediction for Collision Risk Assessment in Connected Vehicle Environment

Simple item page
dc.contributor.advisorFar, Behrouz H.
dc.contributor.advisorFapojuwo, Abraham O.
dc.contributor.authorAfkhami Goli, Sepideh
dc.contributor.committeememberShahbazi, Mozhdeh M.
dc.contributor.committeememberKrishnamurthy, Diwakar
dc.date2019-11
dc.date.accessioned2019-09-20T18:56:19Z
dc.date.available2019-09-20T18:56:19Z
dc.date.issued2019-09-18
dc.description.abstractSafety systems in intelligent and autonomous vehicles rely heavily on the accuracy of localization and location prediction of nearby road users. Current vehicular systems use a variety of sensors to perceive the environment. Cameras, proximity and ranging sensors are the most common types of devices used for this purpose. The main limitation of onboard sensors is the partial perception of the surrounding environment due to occlusions, limited field of view, or resolution and range restrictions. Wireless vehicular communication offers new opportunities for safety applications via information sharing and extending the perception of a car, beyond the limitations of its onboard sensors. This thesis first explores the problem of fusing multiple sources of location information, including the sensor data and information shared via Vehicle-to-Vehicle (V2V) communication to improve localization accuracy. Using sensor data adds more challenges as it is usually noisy, mixed with clutter and false alarms. To address these challenges, the problem is formulated in Random Finite Set (RFS) statistics and solved via the Probability Hypothesis Density (PHD) filter. Second, this thesis investigates the location prediction problem in the connected vehicle environment. A data-driven framework is proposed to learn motion patterns from historical trajectory data via Gaussian Process Regression (GPR) and share this information among vehicles. In this framework, a vehicle leverages GPR models alongside sensory location data to predict the positions of nearby cars. Third, to improve the accuracy of both location estimation and prediction, a new multi-target Bayesian filtering algorithm is proposed that incorporates the GPR models in the Multi-source Multi-target Bayesian filters. Simulations based on real-world data and comparisons to similar algorithms from the state-of-the-art demonstrate the performance of the proposed methods. The results show about 30% improvement in estimating and predicting the location of surrounding vehicles for seconds in advance, fulfilling the requirements for a real-time collision risk assessment system.en_US
dc.identifier.citationAfkhami Goli, S. (2019). Location Estimation and Trajectory Prediction for Collision Risk Assessment in Connected Vehicle Environment (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/37088
dc.identifier.urihttp://hdl.handle.net/1880/111023
dc.language.isoengen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectConnected Vehicle Environmenten_US
dc.subjectBayesian Filteringen_US
dc.subjectCollision Avoidanceen_US
dc.subjectIntelligent Vehicleen_US
dc.subjectMachine Learningen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationComputer Scienceen_US
dc.subject.classificationEngineering--Automotiveen_US
dc.titleLocation Estimation and Trajectory Prediction for Collision Risk Assessment in Connected Vehicle Environmenten_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineEngineering – Electrical & Computeren_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
ucalgary.item.requestcopytrueen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ucalgary_2019_afkhami-goli_sepideh.pdf
Size:
6.64 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Open Theses and Dissertations