When mining vehicle operators take risks, there is a increased probability of an accident that can cause injuries, fatalities and large financial costs to the mine operators. It can be assumed that the operators do not intentially take unnecessarily high risk, and that the risks are hidden due to factors such as adverse weather, fatigue, visual obstructions, boredom, etc. This paper examines the potential of measuring the risk of danger in a multi-agent situation by using the safe rules of operation defined by mining safety management. The problem with measuring safety is that the safe rules of operation are heavily dependent on the context of the situation. What is considered normal practice and safe in one part of the mine (such as performing a u-turn in a parking lot) is not safe on a haul road. To be able to measure safety, it is therefore necessary to understand the different context areas in a mine so that feedback of unsafe behaviour presented to the operator is relevant to the context of the situation. This paper presents a novel method for generating context area information using the vehicle trajectory information collected from a group of vehicles interacting in an area. Results are presented using real-life data collected from several operating fleets of mining vehicles. The algorithms presented have potential application to a large variety of environments including Intelligent Transportation Systems (ITS).
Vehicle safety; Collision avoidance; Context; Data mining