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General Behavior Prediction by a Combination of Scenario-Specific Models

Before taking a decision, a driver anticipates the future behavior of other traffic participants. However, if a driver is inattentive or overloaded, he may fail to consider relevant information. This can lead to bad decisions and potentially result in an accident. A computational system that is designed to anticipate other traffic participants' behaviors could assist the driver in his decision making by sending him an early warning when a risk of collision is predicted. Existing research in this area usually focuses on only one of two aspects, i.e., quality or scope. Quality refers to the ability to warn a driver early before a dangerous situation happens. Scope is the diversity of scenarios in which the approach can work. In general, we see methods targeting a broad scope but showing low quality, with others having a narrow scope but high quality. Our goal is to create a system with high quality and high scope. To achieve this, we propose an architecture that combines classifiers to predict behaviors for many scenarios. In this paper, we will first introduce the generic concept of such a system applicable to highway and inner-city scenarios. We will show that a combination of general and specific classifiers is a solution to improve quality and scope based on a concrete implementation for lane-change prediction in highway scenarios.