Detection and prediction of lane-changes: A study to infer driver intent using support vector machine

The master thesis in this report has been made in cooperation between Scania CV AB in Södertälje and the department of machine design at KTH in Stockholm. Scania CV AB is a leading manufacturer of heavy trucks, buses and coaches, and industrialand marine engines. Increasing road safety demands for vehicles in development has resulted in a new generation of safety systems, called Advanced Driver Assistance Systems (ADAS). These new systems do not only try to mitigate the effects of a crash, they also try to prevent them from happening. To avoid that ADAS will interfere during controlled maneuvers by a driver, it is necessary to determine the intent and current actions of the driver from sensor measurements. This important information will help to improve the decision-making for safety systems of when to engage in assistive actions. In this study, the possibilities to detect and predict lane-changes from patterns in sensor measurements have been made using a truck. The main objectives were to decide how to approach this type of problem without using turn signals, estimate the accuracies that can be achieved and determine which sensors that are required to solve the task. From the concept evaluation it was determined to use the pattern recognition technique support vector machine (SVM) for the task. To train and test the algorithm real vehicle data was used, recorded from a truck during motorway driving. From this data the algorithm was able to correctly classify, with accuracy of 82%, between lane-keep and lane-change actions. The algorithm could also quickly detect a lane-change before the first wheel crossed the lane marking. With the set-up of sensors available in the test vehicle, no significant patterns were found in driver actions prior to a lane-change (the intent). Therefore, an alternative approach was suggested that aimed to determine what the driver should prefer to do in a specific situation from contextual data. This additional information helped to reduce the number of false positives with 8 percentage points when classifying between lane-change and lane-keep actions.