Australia’s first large-scale Naturalistic Design Study (NDS), involving 360 cars, aims to understand what people actually do when they drive their cars in normal, impaired and safety-critical situations. The NDS is the first in a series to be run in Australia over the coming decade. It will provide a huge living database of information that can be interrogated for many years to deliver new and improved evidence-based countermeasures and improve Australia’s road safety performance relative to other OECD countries. The NDS will be used to answer a broad range of research questions relating to intersection safety, interaction with vulnerable road users, speed choice, fatigue, distraction and inattention and interaction with intelligent transport systems.
The project started in the second half of 2014, with the first wave of vehicles to be deployed early in 2015. A sample of 360 volunteer ordinary licensed drivers, 180 from New South Wales (NSW) and 180 from Victoria, will be recruited. During four months, each driver will have his/her own car instrumented with data recording equipment, allowing the research team to record continuously their driving behaviour and those of others with whom they interact.
Major equipment for the study will include 110 Data Acquisition System (DAS) units and 90 Mobileye units. These will be rotated between 360 vehicles over a two-year period.
Passenger cars will be the focus of the study, as they account for around 75% of all vehicles on the road in Australia. Other vehicle types (e.g. trucks, motorcycles) will be instrumented and deployed in separate, follow-on, studies using slightly modified DAS units.
The project, led by the University of NSW in Sydney Australia, in collaboration with 3 other Australian universities (Monash University, Adelaide University and QUT) and Virginia Tech in the US, is running in six phases.
The first phase covers driver selection and recruitment. Recruitment strategies are based on strategies used in other parts of the world that have proven to be successful. Interested parties log onto a custom-developed website and complete an expression of interest questionnaire that specifies the inclusion and exclusion criteria. Those meeting the criteria are selected.
The second phase relates to the DAS installation. A dedicated DAS Installation Site (DIS) has been created at each data collection site in each State. DAS and Mobileye units will be fitted, aligned, calibrated, and checked to ensure that everything is operable. Participants will be inducted at the DIS in each State. Each driver will be being assessed on several dimensions (visual, cognitive and motor) relevant to driving to allow the study to explain variability in the driving data collected, and determine what dimensions predict observed driving behaviour.
The next phase covers data collection. The DAS units, which are the same as those deployed in the US SHRP 2 NDS, were chosen to enable the researchers to collect all data required to answer the research questions of interest in this study. Each DAS unit comprises 3 main components: the head unit, main unit and a forward radar assembly. The head unit includes 3 low-light video cameras that record different views, a still camera that takes images of passengers, a microphone for voluntary voice recording of critical incidents, a cabin alcohol senor, an incident push button, a luminance sensor and a GPS. A Mobileye sensor will be co-located with the DAS head unit. The main unit located in the boot hosts computer functions that coordinate sensor input, communications and data storage. Around 60 TB of data will be collected in total. The M2M mobile telephone link set up between each DAS unit and a central computer at the Virginia Tech Transportation Institute (VTTI) in the United States, will assess remotely the health status of each DAS unit, update DAS software, detect critical incidents and notify researchers when the hard drive is almost full.
Data management will occur at two of the participating Australian universities that will securely house the data transfer and storage equipment. The raw data from vehicles will be sent to a central computer at UNSW. It will then be sent via internet to the Virginia Tech Data Centre in the US, where it will be pre-processed by VTTI into a form ready for analysis by the Australian research teams, and securely stored in a database. To access the analysis-ready data, each Australian research team will generate data queries that are transmitted to the Data Centre at Virginia Tech via an Internet portal. The raw and data pre-processed by VTTI will be downloaded regularly and stored securely in a Database Server at UNSW.
During the final phase of the project, various data analysis techniques will be used to answer the questions relating to the seven research themes. Exposure analysis will identify and quantify the risks to which drivers and other road users are exposed. Risk analysis will derive measures of increased or decreased crash risk associated with driver and road user exposure to various risks, data mining techniques will be used to discover recurring patterns in driving behaviour associated with safety outcomes, descriptive analyses will focus on the characterisation of the way in which drivers and other road users modify their behaviour to adapt to conditions of increased risk, and correlational analyses will identify and quality the relationship between driver characteristics, driving behaviour and safety outcomes.
Reporting will happen in months 35 and 36 of the project. A one-day workshop, convened by UNSW, will be held at the conclusions of the study to convey the key findings and recommendations, and discuss the lessons learned; to which the partners, key industry bodies, and state, national and relevant international agencies will be invited.
The project is being funded by the Australian Research Council, Transport for NSW, NRMA, The Transport Accident Commission, VicRoads, the Motor Accident Commission and the Office of Road Safety.
To read the full presentation, please click here http://acrs.org.au/files/arsrpe/Paper%2012%20-%20Regan%20-%20Research%20Methods.pdf
