A general event data recorder is a device installed in automobiles to record information related to vehicle crashes or accidents. The data provide a better understanding of how certain crashes come about. This study made a prototype of a driving behaviour-based event data recorder (DBEDR), which provides the information of driving behaviours and a danger level. The authors approach is to recognise the seven behaviours: normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving and approaching the car in front by the hidden Markov models. All data were collected from a real vehicle and evaluated in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behaviour recognition. The danger level is inferred by fuzzy rules involved with the above behaviours. DBEDR recorded the recognised driving behaviours and the danger level, and the places were stored with the assistance of a global positioning system receiver. By integrating Google Maps, the locations, the driving behaviour occurrences, the danger level on the travel routes and the recorded images, the proposed DBEDR could be more useful compared with the traditional EDRs.

Pedestrian navigation services guide people to reach their destinations as the vehicle navigation services do. However, the moving way of people differs from that of vehicles, and hence the assumptions for car-navigation services are not suitable for pedestrian navigation. People may walk through the place without GPS signals due to the shelter in the sky. In some space, the accuracy of GPS may not be enough for pedestrian navigation. In this situation, localization ability is necessary in this space, we call it as a special interest zone (SIZ), to assist the GPS-navigation systems. This paper proposes a system by offering a navigation service on general routes and a localization service to SIZ. For navigation service, GPS and GIS technologies were used for guiding, and a modified A* algorithm was developed to implement the path planning function. Because the accuracy of GPS is insufficient to offer the precise localization needed at SIZ, ZigBee-based sensor networks were applied and deployed around SIZ. When the user is close to SIZ, a dynamic swap mechanism enables the localization function to provide higher localization results. The localization algorithm, using an extended Kalman filter to fuse the data of ZigBee and GPS, achieved the localization error less than 1 m, when the error compared to ZigBee-only or GPS-only approach. Both services were successfully implemented on an embedded platform and evaluated on the real environment.

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