Lane Change Intent Analysis for Preceding Vehicles : a Study Using Various Machine Learning Techniques

Sammanfattning: In recent years, the level of technology in heavy duty vehicles has increased significantly. Progress has been made towards autonomous driving, with increaseddriver comfort and safety, partly by use of advanced driver assistance systems (ADAS). In this thesis the possibilities to detect and predict lane changes for the preceding vehicle are studied. This important information will help to improve the decision-making for safety systems. Some suitable approaches to solving the problem are presented, along with an evaluation of their related accuracies. The modelling of human perceptions and actions is a challenging task. Several thousand kilometers of driving data was available, and a reasonable course of action was to let the system learn from this off-line. For the thesis it was therefore decided to review the possibility to utilize a branch within the area of artificial intelligence, called supervised learning. The study of driving intentions was formulatedas a binary classification problem. To distinguish between lane-change and lane-keep actions, four machine learning-techniques were evaluated, namely naive Bayes, artificial neural networks, support vector machines and Gaussian processes. As input to the classifiers, fused sensor signals from today commercially accessible systems in Scania vehicles were used. The project was carried out within the boundaries of a Master’s Thesis projectin collaboration between Linköping University and Scania CV AB. Scania CV AB is a leading manufacturer of heavy trucks, buses and coaches, alongside industrialand marine engines.