Driving Behaviour Estimation System Considering the Effect of Road Geometry by Means of Deep NN and Hotelling Transform

Abstract

In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers’ safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver’s perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one’s driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving.