Detection of Driving Events using Sensory Data on Smartphone

In a fast-paced environment of today society, safety issue related to driving is considered a second priority in contrast to travelling from one place to another in the shortest possible time. This often leads to possible accidents. In order to reduce road traffic accidents, one domain which requires to be focused on is driving behaviour. This paper proposes three algorithms which detect driving events using motion sensors embedded on a smartphone since it is easily accessible and widely available in the market. More importantly, the proposed algorithms classify whether or not these events are aggressive based on raw data from various on board sensors on a smartphone. In addition, one of the outstanding features of the proposed algorithm is the ability to fine tune and adjust its sensitivity level to suit any given application domain appropriately. Initial experimental results reveal that the pattern matching algorithm outperforms the rule-based algorithm for driving events in both lateral and longitudinal movements where a high percentage of detection rate has been obtained for 11 out of 12 types of driving events. In addition, a trade-off between the detection rate and false alarm rate has been demonstrated under a range of algorithm settings in order to illustrate the proposed algorithm’s flexibility.