|Title:||Data-driven Modelling on Powered Two Wheelers using Machine Learning|
According to the European Commission (ec.europa.eu), in 2015, 15% motorcycle riders and 3% moped (and similar powered two-wheelers) riders road fatalities. As it can be found in [1-2], there are around 90% of road-traffic crashes caused by driver error (i.e. as inattention, loss of vigilance, mental under/overload) and unsafe behavior (i.e. inadequate training or lack of experience). Improving road safety includes understanding the individual, collective and interaction behaviour of riders. This work proposes a development of a methodology for riding patterns classification by using machine learning techniques. The riding pattern classification problem will be formulated as a classification problem aiming to identify the class of the riding situation by using inertial sensor data. This inertial sensor data was collected from three accelerometer and three-gyroscope sensors mounted on the motorcycle. These measurements constitute experimental database which was valuable to analyze Powered Two Wheelers (PTW) rider behavior. In a previous work , the obtained results based on the raw 3D inertial measurements (accelerometers / gyroscopes) data shown the effectiveness of such approach.
The project work is subdivided as follows:
1. Literature study and state-of-the-art
This task requires a systematic literature review to identify the features, time and frequency domain analysis approaches for feature extractions, approaches for feature selection/ranking, and machine learning approaches for classification for PTW riding manoeuvring. Student requires presenting an analytical summary of the state-of-the-art based on the literature study.
Analysing the PTW's riding data of various events for a given dataset. Student also requires developing an approach using machine-learning algorithms for detection of different riding patterns based on the riding events and sensory dataset.
Student should evaluate the proposed approach and learning algorithm for detecting the patterns. Make a comparison between different feature extraction and selection methods on the data set that will be given.
4. Report writing
It is expected that student provide a report as a completion of the project work. Report consists of background, problem formulation, state-of-the-art, methods, evaluation, and discussion.
1. Elander, J., West, R, French D, "Behavioural correlates of individual differences in road-traffic crash risk: An examination of methods and findings", Psychological bulletin 113.2 (1993): 279
2. Feyer, A.M., Williamson, A. & Friswell, R., "Balancing work and rest to combat driver fatigue: an investigation of two-up driving in Australia". Accident Analysis and Prevention 1997 Jul, 29, 541-553.
3. F. Attal, A. Boubezoul, L. Oukhellou and S. Espié, "Riding patterns recognition for Powered two-wheelers users' behaviors analysis," 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, 2013, pp. 2033-2038.
|IDT supervisors:||Mobyen Uddin Ahmed|