Modelling Tools and Simulation Platforms for Advanced Driver Assistance Systems

Recent developments in Advanced Driver Assistance Systems (ADAS) lead to the concept of eco-driving support systems, which assist drivers in controlling vehicles in a sustainable way by reducing fuel consumption and carbon dioxide emissions. In this thesis, an algorithm for an ecological Adaptive Cruise Control (ecoACC) system is designated. In order to test the system in MIL (Model in the Loop) and DIL (Driver In the Loop) environments, an important task is the specification of a fuel consumption model. In the first part of the thesis an overview is presented about fuel consumption models and several eco-devices developed by different authors. In the second part of the thesis an experimental campaign (with a sample of 100 drivers) is carried out in order to specify and validate the model. The survey is supported by the DRIVEIN2 (DRIVEr monitoring: technologies, methodologies, and IN-Vehicle Innovative systems) project. As the major data are collected using the OBD-II port of the vehicle, and in particular the data related the instantaneous fuel consumption, a validation of the former data is carried out in collaboration with the CNR (Consiglio Nazionale delle Ricerche †" Istituto motori). To carry out a more realistic simulation a vehicle dynamics model was coupled with the ADAS model. The purpose of the implemented vehicle dynamics model is to simulate, in real time, the vehicle's dynamic behaviour, thanks to continuous integration of the balance equations regulating the longitudinal and lateral vehicle motion. Furthermore, a model able to provide an estimation of real-time fuel consumption of the vehicle is developed: such a model is embedded in the vehicle model discussed in the following, in order to optimize consumption performance during driving task. The implemented system is a next-generation eco-drive system, in the sense that it is able to observe the driver's behaviour and the current fuel consumption, as well as to simulate the fuel consumption in the same traffic conditions and stimuli the driver is subject to, and finally to compare the actual fuel consumption with this of an eco driver. In this way the system gives a reward to the driver if his/her fuel consumption is lower than those of the eco driver, vice versa the system indicates the possibility to improve his/her driving style.