Utility-Based Agent for Vehicle Driver Behaviour Modelling

Abstract

Knowledge on driver behaviour is a major factor that can possibly aid in future strategies for minimising if not fully controlling road fatalities. The behaviour of human vehicle drivers is the main cause of road accidents and is also the factor which has so far proved to be the most difficult to establish and model. Studies conducted on driver behaviour modelling have been limited by five factors: study methodology; vehicle model compatibility; cost; overestimation of critical driving events; and scope for driver behaviour monitoring. Probabilistic reasoning and intelligence, which are critical in modelling under stochastic environments are lacking in the applied methodologies. Fortunately, a combination of computing and communication technologies now makes it possible to model the behaviour of drivers operating in complex environments. The main objective of the research was to model human vehicle driver behaviour using a utility-based agent. To realise this objective, the research identified parameters that describe the behaviour of a human vehicle driver operating under diverse environments, formulated a vehicle driver behaviour dataset and developed and evaluated a vehicle driver agent that can operate in a complex environment. A sample of 30 drivers was used, with tonnes of data collected and analysed. Vehicle position coordinates, speed, direction, altitude, time and a reflected signal signifying the presence of an obstacle were collected using the Global Positioning System (GPS) comprising of satellites, GPS receivers and a server. Data analysis generated a driver behaviour dataset that was used in the preparation of the driver agent through three main phases: training, validation and testing. The driver agent was founded on Mixture Models with Bayesian inferencing techniques that performed driver behavioural pattern recognition and predictive analyses. The agent’s actions under dynamic conditions were evaluated against sets of performance standards, yielding mean success rates of over 68% accuracies and over 70% F-scores, +/- 5. This was an indicator of the appropriateness of the data collection tools and techniques, data analysis algorithms and the driver behaviour dataset. The significance of the study is three-fold. First, the function of the system could be extended to providing advisory services to drivers in real-time. Second, data gathered from the system could be used by road safety stakeholders to vet drivers and to diagnose causes of road accidents. Finally, the resulting knowledge-base could establish standards of rationality in driving and/or formulate rules for use in driverless vehicle control systems.