Traded Control Architecture for Automated Vehicles Enabled by the Scene Complexity Estimation

Juan Medina-Lee, Jorge Villagra, Antonio Artuñedo


A number of urban driving situations are still today too challenging to be handled by an autonomous driving system (ADS), and an intervention from humans inside the vehicle may be necessary. In this work, a novel traded control architecture is proposed to enhance the operational domain of the ADS under the premise that vehicles and humans may need to adapt their cooperation level depending on the context. To that end, a complexity level will be defined and computed in real time for each driving scene, and the role of the ADS and the human operator will be defined accordingly. With this information in hand, the system will alert the human operator when the involvement level will be lower than required or when a complex scene is detected.


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