A low-cost intelligent localisation system to improve cyclist safety

Cycling is an increasingly popular mode of travel in cities owing to the great advantages that it offers in terms of space consumption, health and environmental sustainability, and it is therefore favoured and promoted by many city authorities worldwide. A large number of recently introduced cycling-related schemes in many cities demonstrates this trend. However, the relatively low safety of pedal cycles as perceived by the users currently presents itself as a hurdle, and therefore cycling has yet to be adopted to a wider extent by users as a true alternative to the private car. Rising accident numbers, unfortunately, confirm this perception as reality, with a particular source of hazard appearing to originate from the interaction of cyclists with motorised traffic at low speeds in urban areas. Technological advances in recent years have resulted in a number of attempts to develop systems to prevent cyclist-vehicle collisions, but they have generally stumbled upon the challenge of accurate cyclist localisation and tracking, which can enable predicting a collision within a short-term time-horizon (5-10 seconds). Indeed, cyclist positioning accuracy is essential for any collision avoidance system, not only to ensure the effective operation of the system but also to minimise the occurrence of false alerts. Thus, motivated by the poor safety record, the research reported here involves the development and testing of an innovative technological solution for accurately localising and tracking cyclists, where the ultimate aim is to utilise the techniques in a concept called Cyclist 360° Alert to avoid collisions. The overarching innovation of this PhD is the development of the instrumented bicycle system, called iBike, which can be employed to track cyclists’ positions more precisely. The system relies on bicycles being instrumented with low-cost Micro-electromechanical systems (MEMS) sensors, and utilises multiple Kalman filters, which were developed from the geometrical and kinematics modelling of the bicycles, to conduct a multi-sensor fusion on the iBike acquisition data with the measurements from the Global Positioning System, Wi-Fi hotspots and mobile communication systems. Apart from the above, the thesis also reports on the results obtained from a number of field trials where an enhanced off-the-shelf positioning system was employed to validate the developed system. The overall results from the field experiments demonstrate that, on average with an 80% probability, the iBike system can be used to estimate a position with less than 0.5 m error compared to a 16.2 m error from the enhanced positioning system under the same circumstances. Thus, the results from the field trials using the iBike have shown successful outcomes for the developed methodologies. This means that the iBike can be used to predict a collision more precisely. These results are presented in detail together with the hardware and software of the iBike system in this thesis