Ontological Security and Private Car Use in Sydney, Australia

Transforming our roads into smart roads is an indispensable step towards future self-driving systems, and therefore has drawn increasing attention from both academia and industry. To this end, this paper develops a novel cost-effective IoT-based target detection system utilizing the multi-sensor data fusion technology with a particular focus on pedestrian detection, as an important component of smart road system. Particularly, the developed intelligent pedestrian detection module (iPDM) consists of three major sensors, i.e., Doppler microwave radar sensor, passive infrared (PIR), and geomagnetic sensor. A multisensor data fusion algorithm is developed to fuse the sensor data and achieves reliable target detection. After that, iPDM sends the relevant warning signal wirelessly to nearby base station and vehicles. Experiments are conducted on real traffic environment to evaluate the performance of iPDM. The results validate the high reliability of iPDM with an average 91.7% detection accuracy. Moreover, to our best knowledge, iPDM is the first IoT-based implementation for pedestrian detection of smart roads. It is necessary to highlight that iPDM is a low-cost, low-power, wide-coverage pedestrian detection system where the cost of a single iPDM is only US $30, which makes it suitable to large-scale deployment