Data from vehicles approaching an intersection during a red-light phase has been recorded by measuring real traffic in urban areas. Using a laser scanner based tracking system, vehicle velocities during approaches to the red light have been estimated and various metadata (such as object class, distance to the intersection when the traffic light turned from green to orange and weather data) has been collected. The experimental setup is validated using a Real-Time Kinematic (RTK) GPS system. The resulting information can be used when designing warning strategies for Advanced Driver Assistant Systems (ADAS). Examples of a warning strategy estimation for a misinterpretation of the traffic situation for both the host vehicle's driver as well as other drivers endangering the host vehicle are presented.
Abstract-This paper describes the design and implementation of a reinforcement learner based on Q-Learning. This adaptive agent is applied to the city placement selection task in the commercial computer game Civilization IV. The city placement selection determines the founding sites for the cities in this turn-based empire building game from the Civilization series. Our aim is the creation of an adaptive machine learning approach for a task which is originally performed by a complex deterministic script. This machine learning approach results in a more challenging and dynamic computer AI. We present the preliminary findings on the performance of our reinforcement learning approach and we make a comparison between the performance of the adaptive agent and the original static game AI. Both the comparison and the performance measurements show encouraging results. Furthermore the behaviour and performance of the learning algorithm are elaborated and ways of extending our work are discussed.
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