Learning controller for prediction of lane change times : A study of driving behaviour using naive Bayes and Artificial Neural Networks

Abstract: Today's trucks are becoming more and more safe due to the use of an Advanced Driver Assistance System (ADAS). This system is aimed to assist the driver in the driving process, and to increase the safety for both the driver and the environment around the vehicle. These systems require strict design criteria to enable sufficiently high precision and robustness. ADAS are developing intensely today, and these systems represent a way towards a completely autonomous vehicle community. The main focus of this master thesis project is to investigate the possibility of predicting a driver's typical lane change time before the truck reaches a highway. This was done by trying to identify the driving behaviour using sensor data from non-highway driving. Techniques from machine learning, such as naive Bayes and Artificial Neural Networks (ANN), with various combinations of sensor inputs were used during this process. The results indicate that the assumption that different driving behaviours are representing different lane change times is true. Furthermore, predicting lane change times in whole seconds was as difficult as predicting lane change of three classes, fast, medium and slow. Predicting fast or slow lane change gave a better result. Only one set of validation data of totally five was predicted incorrectly. There was no big difference in the results between naive Bayes and the designed ANN. However, the results were not good enough for practical use, and more research is needed. Methods for increasing the performance and future work are also discussed.