Design and optimization of a Neural Network based driver recognition system by means of a multiobjective genetic algorithm
Advanced Driving Assitance Systems (ADAS) cover a wide range of systems that aim to provide increasingly a safe and efficient driving. Many of these systems are endowed with some intelligent skills which are, in many cases, addressed by means of Soft Computing (SC) paradigms like Neural Networks (NN) or fuzzy systems among others. However, SC algorithms require normally large computational resources which are incompatible with the kind of the electronic systems that can be deployed in cars where the size, cost and power consumption are always very restrictive. In this paper we present a NN based driving recognition system, able to model the driving style of different drivers. Such a system could be used to detect abnormal driving behaviours and hence, to avoid dangerous situations. In order to obtain an optimized network in terms of size and complexity, we make the design by using a multiobjective genetic algorithm that provides at the same time a reduced number of input variables and a low number of neurons in the network. To make feasible the operation of the algorithm, we select Extreme Learning Machines as NNs as they have a learning mechanism very fast and precise.
2016 International Joint Conference on Neural Networks (IJCNN)