Centre for Automotive Safety Research (CASR)

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https://hdl.handle.net/2440/17679

The Centre for Automative Safety Research (formerly the Road Accident Research Unit) conducts high quality independent research that enables rational decision making, leading to reductions in the human and economic losses from road crashes.

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Centre for Automotive
Safety Research
The University of Adelaide
SA 5005
AUSTRALIA

Email: casr@adelaide.edu.au
Tel: +61 8 8313 5997
Fax: +61 8 8232 4995

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Browsing Centre for Automotive Safety Research (CASR) by Author "ACT Road Safety Fund"

Now showing 1 - 3 of 3
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    An assessment of ACT road infrastructure for compatibility with Advanced Driver Assistance Systems
    (Centre for Automotive Safety Resarch, 2024) Mackenzie, J.; van den Berg, A.; Ponte, G.; ACT Road Safety Fund
    To explore the compatibility of the ACT road network with modern vehicle ADAS, an instrumented vehicle was driven throughout the Territory to collect data over a period of five days. Feedback from the consultation of ACT road infrastructure stakeholders was used to assist in the selection of roads for data collection, which included all main highways as well as significant proportions of the urban and rural arterial network. The instrumented vehicle was fitted with a Mobileye dev-kit and Video Vbox HD2 system which provided the capability to collect details about what a commercial-grade ADAS is able to “see” while travelling through the road network. There were 759,772 points of data collected over 1,349 km of roadway during the study along with the detection of 1,963 speed limit signs. This dataset was then analysed to investigate what details regarding line markings and speed limit signs the Mobileye was able to detect. These analyses were also augmented with additional data obtained from the Open Street Map road network. Based on the analyses, high-resolution maps were generated that show ADAS is likely to have a good compatibility with the ACT road network in general. Geographic datasets were also generated as an output, providing an opportunity for further analyses.
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    An evaluation of bicycle passing distances in the ACT
    (Centre for Automotive Safety Research, 2019) Mackenzie, J.; Dutschke, J.K.; Ponte, G.; ACT Road Safety Fund
    To evaluate bicycle passing distances in the Australian Capital Territory (ACT), specialised passing distance measurement devices (PDMDs) were installed on a sample of 23 cyclists who ride in the ACT. Passing distance data and GPS data was collected by cyclists using the PDMDs for a four-week period, during a trial phase of a newly legislated minimum passing distance (MPD) rule. The MPD rule requires drivers to provide more than 1 metre of space when passing a cyclist on a road with a speed limit of 60 km/h or below, and 1.5 meters of space when passing a cyclist on a road with a speed limit above 60 km/h Analysis of the data collected in the study identified 16,476 passing events during 6,531 kilometres of cycling, over a period of 271 riding hours. Non-compliance with the MPD rule on roads zoned 60 km/h or less was 2.7% and the mean passing distance was 1.85 metres. On roads zoned greater than 60 km/h non-compliance was 11.2% and the mean passing distance was 1.97 metres. The degree of non-compliance varied considerably with road characteristics and location.
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    Video capture and analysis of cyclists using infrastructure in the ACT through machine learning
    (Centre for Automotive Safety Research, 2022) Mackenzie, J.; Ponte, G.; ACT Road Safety Fund
    The implementation of infrastructure to reduce traffic conflicts and improve road safety for cyclists is critical. However, potential benefits resulting from strategic interventions can only be achieved if there is corresponding compliance or good utilisation of that infrastructure. Traditional methods for evaluating cycling behaviours and interactions with the traffic system either involve expensive roadside observations, which can influence cycling behaviours through the observer effect, or by using induction or pneumatic tube system which can only yield count information and cannot be used in mixed traffic situations. This study used long duration portable video cameras to, somewhat covertly, record mixed road traffic, with the captured video subsequently being processed with bespoke machine learning software. This innovative process required negligible set-up or manual processing time and a researcher was only required to do a desktop analysis on 6% of the total traffic video recorded. Agencies requiring evaluations of cyclist interactions with specific infrastructure or behaviours at specific locations could use this rapid and cost effective process to assist with data collection and analyses to inform or optimise their cycling safety strategies.