Visual state estimation of traffic lights using hidden Markov models

The comprehension of dynamic objects in the environment is a major concern of prospective assistance systems. Among the relevant dynamic objects are not only road users, but also parts of the traffic infrastructure: Traffic lights switch between different light colors to manage traffic at intersections. We propose a camera-based approach to incorporate the visual information of traffic lights. Assistance systems can use it to realize comfort, fuel economy and safety functions. We focus on the classification and state estimation using support vector machines and hidden Markov models. Our system is able to distinguish different types of traffic lights - even blinking lights - in real-time.