PhD in Crash causation based on event trigged naturalistic data with video

Information about the research and the position
Research focused on understanding why traffic crashes occur has been getting increasing attention in the last few decades. In the major effort to development advanced driver assistance systems (ADAS) that support the driver in normal and critical situation, understanding driver behavior is a key component. However, traditional accident investigation techniques have limited information about the pre-crash phase of the crashes they are investigating. There is usually no information about the exact unfolding of the events preceding the crash – which would be invaluable to understand. However, recent advances in technology have facilitated collection and analysis of naturalistic driving and riding data. These data are captured unobtrusively while drivers’ perform normal everyday driving, capturing both normal driving and crashes when they happen. Video of the driver along with video of the forward scene and other sensor i.e. GSP and accelerometers are usually included. There are two types of naturalistic driving data. First, when data has been collected as part of an effort specifically aimed at traffic safety research. Second, when data has been collected as a product of a business model not aimed specifically at traffic safety research. Recently both these sources of data have started to provide a larger set of actual crashes in real traffic, captured by different sensors, including video. These data can be used to understand, e.g., 1) which behavioral mechanisms inform drivers about the criticality of a situation (such as drivers’ vision and scanning patterns), 2) how the road infrastructure and other road users affect the behavior and interplay between road users in the pre-crash phase, and 3) what are the mismatches between drivers expectations of a situation and how it actually plays out. However, to be able to achieve this, methods to extract data from the different sources of data, as well as methods to analyze the data, has to be developed.

This PhD position is ultimately aimed at understanding driver behavior mechanisms related to the causation of crashes, but will start with the development of methods for reconstruction of the pre-crash event from video and other sensor data. 

Major responsibilities
- Carrying out research activities
- Collaboration with industry national and international research partners 
- Being awarded at least 67.5 education credits (typically from PhD and Master courses)- Teaching (or equivalent) 20% 

Your major responsibilities as PhD student is to pursue your own doctoral studies. You are expected to develop your own scientific concepts and communicate the results of your research verbally and in writing. The position generally also includes teaching on Chalmers' undergraduate level or performing other duties corresponding to 20 per cent of working hours.

Position summary
Host institution: Chalmers University of Technology
Postion: PhD Student.
Objectives: To develop methods to reconstruct event triggered naturalistic driving data and from this data understand driver behavior mechanisms for one or a few crash scenarios.
Tasks: Conduct theoretical and empirical research with the aim to understand driver safety.
Methodology: Reconstruction of road user kinematics from video and other available sensors, and develop methods for quantitative and qualitative analysis of the reconstructed data and other event information.

Full-time temporary employment. The position is limited to a maximum of five years.
Qualifications




• English language: It is a requirement that fellows will be able to express themselves in English at a high level.

• To qualify as a PhD student, you must have a master's level degree corresponding to at least 240 higher education credits in a relevant field.

• You are either 1) an engineer with a Master degree, with a clear interest in the understanding of human behavior, or 2) a behavioral scientist with formal training in the topics needed for eligibility as a PhD student as Chalmers (contact us for more information) and extensive experience in working with time-series data.

• You are skilled in the practical use of analysis tools such as Matlab, and skills including advanced statistics and/or a programming background are advantageous.

• Preferably you have experience working in the traffic safety domain, and more specifically with time-series data related to traffic safety. 

The position requires sound verbal and written communication skills in Swedish and English. If Swedish is not your native language, you should be able to teach in Swedish after two years. Chalmers offers Swedish courses.


Application deadline: 2014-03-12

For questions, please contact:
PhD Marco Dozza, Vehicle Safety/Accident Prevention
E-mail: marco.dozza@chalmers.se
Phone: +46-31-772 3621

Jonas Bärgman, Vehicle Safety/Accident Prevention
E-mail: jonas.bargman@chalmers.se 
Phone: +46 31-772 5846




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