Prediction of Cyclists Movement in Different Terrain Conditions

Abstract

In Malaysia, most of the accidents involving a bicycle and another vehicle are due to either the driver or rider ‘failing to look properly’. This is more significant with the government initiatives to support the use of bicycle making the carbon-free environment, a vision of TN50. This research addresses the safety aspect of the cyclists in terms of the driver’s point of view which improves cyclist visibility during driving. The proposed helmet system implements a rule-based algorithm which predicts the turning and braking movement of the cyclists. With this system, additional illumination and signaling are provided for the cyclists. The major challenge faced is the implementation of an algorithm for various situations of cycling. To ensure the system could be used on the road, the accuracy and speed of the automatic signaling system need to adhere. Situations that affects the output of the indicators include bicycle speed, the angle of turning, body tilt, duration of turn and random body movements. This paper implements a 3-axis accelerometer and a microcontroller in a data logger to acquire the required data which are analyzed in MATLAB. Using filtering technique, the acquired data are then be cleaned to remove noise due to vibration during cycling. The characteristics of braking and turning are then analyzed in the time domain as well as frequency domain to ensure the optimum algorithm used for gesture recognition and movement prediction. The algorithm is based on sliding window, FFT and threshold-based rule algorithm. The output based on the rule-based algorithm then illuminate the corresponding signals which provide the safety feature of the system.