On the analysis of naturalistic driving data
Licentiate thesis, 2015

In the last several years, the focus of traffic safety research has shifted from injury prevention during a crash to measures taken before a crash, in order to mitigate its effects or avoid it completely. Measures include advanced driver assistance systems, safety aspects of autonomous driving and infrastructure design, behavior-based safety (driver training), and policy-making. All of these pre-crash measures require an understanding of driver behavior. As a result of this need, naturalistic driving data (NDD) has emerged as a crucial data source with high ecological validity. NDD enable not only the real-world assessment of driver behavior, but also that of road infrastructure and pre-crash safety measures. However, NDD’s great potential is hindered by its complexity. Consequently, new methods to analyze NDD are greatly needed. This thesis presents a novel framework for traffic safety research using NDD and discusses the framework’s benefits and drawbacks. Furthermore it presents novel methods for analyzing NDD. The first paper presents a robust method to reduce bias in the analysis of time-series NDD. The second paper ports the DREAM method, used in traditional on-scene crash investigations, to vehicle-to-pedestrian incidents in NDD with video data. The third paper analyzes NDD with a novel method based on expert judgment. This method, inspired by DREAM, is currently applied to commercially collected and event-based, real-world crashes with driver and forward video. Finally, the fourth paper presents a new, pragmatic method to extracting range, range rate and optical parameters (e.g. looming) from the forward video in commercially collected lead-vehicle NDD. In summary, the methods developed and presented in this thesis use quantitative and qualitative analyses of time-series and video data from naturalistic driving to augment our understanding of driver behavior. Pre-crash safety measures will be further advanced not only by these insights, but also by future applications of the methods developed in this thesis.

Alpha-Salen i hus Saga
Opponent: Mikael Ljung Aust,


Jonas Bärgman

Chalmers, Applied Mechanics, Vehicle Safety

Using manual measurements on event recorder video and image processing algorithms to extract optical parameters.

Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design,; (2013)p. 177-183

Paper in proceedings

Analysis of the role of inattention in road crashes based on naturalistic on-board safety monitoring data

PROCEEDINGS of the 3rd International Conference on Driver Distraction and Inattention,; (2013)

Paper in proceedings

Chunking: a procedure to improve naturalistic data analysis

Accident Analysis and Prevention,; Vol. 58(2013)p. 309-317

Journal article

Driving Forces

Sustainable development

Innovation and entrepreneurship

Areas of Advance


Subject Categories

Vehicle Engineering

Technical report - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden

Alpha-Salen i hus Saga

Opponent: Mikael Ljung Aust,

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