Accurate behavior prediction on highways based on a systematic combination of classifiers

Abstract

To drive safely, a good driver observes his surroundings, anticipates the actions of other traffic participants and then decides for a maneuver. But if a driver is inattentive or overloaded he may fail to include some relevant information. This can then lead to wrong decisions and potentially result in an accident. In order to assist a driver in his decision making, Advanced Driver Assistance Systems (ADAS) are becoming more and more popular in commercial cars. The quality of these existing systems compared to an experienced driver is weak, because they rely purely on physical observation and thus react shortly before an accident. For an earlier warning of the driver behavior prediction is used. We classify existing research in this area with respect to two aspects: quality and scope. Quality means the ability to warn a driver early before a dangerous situation. Scope means the diversity of scenes in which the approach can work. In general we see two tendencies, methods targeting for broad scope but having low quality and those targeting for narrow scope but high quality. Our goal is to have a system with high quality and wide scope. To achieve this, we propose a system that combines classifiers to predict behaviors for many scenarios. To show that a combination of general and specific classifiers is a solution to improve quality and scope, this paper will introduce the generic concept of our system followed by a concrete implementation for lane change prediction for highway scenarios.