A suite of new collision avoidance systems (CAS) is presented for use in heavy vehicles whose structure and size necessarily impede driver visibility is introduced. The main goal of the project is to determine the appropriate use of each of the commercially available technologies and, where possible, produce a low cost variant suitable for use in proximity detection on large mining industry haul trucks. CAS variants produced were subjected to a field demonstration and, linked to the output from the earlier CAS 1 project, (a production high-definition in-cabin video monitor and r/f tagging system). The CAS 2 system used low cost Doppler continuous wave radar antennae coupled to the CAS 1 monitor to indicate the presence of an object moving at any speed above 3 Km/h relative to the antennae. The novelty of the CAS 3 system lies in the design of 3 interconnected, modules. The modules are 8 radar antennae (as used in CAS 2) modules located on the truck, software to interface with the end user (i.e. the drivers of the trucks) and a display unit. Modularisation enables the components to be independently tested, evaluated and replaced when in use. The radar antennae modules and the system as a whole are described together with the empirical tests conducted and results obtained. The tests, drawing on Monte-Carlo simulation techniques, demonstrate both the 'correctness' of the implementations and the effectiveness of the system. The results of the testing of the final prototype unit were highly successful both as a computer simulation level and in practical tests on light vehicles. A number of points, (as a consequence of the field test), are reviewed and their application to future projects discussed.

Mine haul road maintenance is traditionally done at scheduled intervals or after regular inspection. Both these methods can lead to unwarranted expenditure, either through over-maintaining the road, or failure to recognise significant deterioration, resulting in an increase in vehicle operating costs. Predictive maintenance management models for unpaved roads have been developed in recent years. These methods work well in a trivial environment where variables such as traffic volume can be predicted. However, many mining systems are too complex for such models to be effective. This work investigates the possibility of using haul truck response to aid haul road maintenance management. The approach adopted for the study was twofold: Firstly, can truck response data be used to recognise specific road defects, in terms of location, type and size? This is important since different defect types require different road maintenance strategies. Secondly, can road roughness be measured on a qualitative basis? With the emphasis on road defect reconstruction, a mathematical modelling approach was adopted. The truck was characterised in terms of its suspension and tyre properties. Dynamic truck response data was acquired during field measurements in which the vehicle was driven over defects of known dimensions. With these data sets available, mathematical modelling and simulation was possible. Quarter vehicle and seven degree of freedom vehicle models played a vital role in this work by laying a foundation in the use of haul truck response for the purpose of road defect reconstruction. A modelling methodology that is based on dynamic equilibrium of an independent front unsprung mass of the truck is proposed in which the vertical dynamic tyre force and eventually the road geometry is calculated. It is shown that defects can be reconstructed from measured truck response data with an accuracy sufficient to fulfil the requirements of defect recognition for road maintenance management purposes. Secondly, a preliminary investigation into the qualitative assessment of road condition via truck response measurements was conducted. The inherent response properties of the truck pertaining to road roughness measurement were studied and some correlation between measured suspension motion and road roughness measured with a high speed profilometer was found. / Dissertation (MEng (Mechanical))--University of Pretoria, 2005. / Mechanical and Aeronautical Engineering / MEng / Unrestricted