TNO is one of the partners of InDeV, an international collaboration of researchers which was created to develop new ways of measuring traffic safety. The main objective InDev is to develop a tool-box for in-depth analysis of accident causation based on a combined use of accident databases, in-depth accident investigations, surrogate safety indicators, self-reported accidents and naturalistic behavioural data.
The problem
Statistics about traffic safety were unreliable, insufficiently detailed, and hard to collect. Researchers often resort to filming busy intersections and manually reviewing the recording. This is a time-intensive and expensive process. A single intersection needs to be monitored for three weeks with two cameras to create an estimation of its safety, adding up to six weeks of footage, which can take six weeks of work to analyse. Typically, less than one percent of the recorded material is actually of interest to researchers.
The solution
TNO applied machine learning to video of accident-prone hot spots to rate intersections on a scale according to their safety. With TNO’s neural network based on Google’s TensorFlow, researchers report that it takes only one hour to review footage that would previously have taken a week to inspect.
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