This paper presents an architecture for Automated Valet Parking (AVP) connected to cloud-based IoT services and mobile user interfaces. The goal is to enable AVP services for automatic vehicles. From the user perspective, automatic car drop-off and pickup are activated via smart phone application, and the user will be able to continuously monitor the vehicle status together with additional services as cleaning or recharge during the parking phase. Further, the IoT platform allows the integration of live services that will interact with automatic driving and parking. As an example, the presented AVP setup includes the operation of service drones to automatically guide a vehicle to the best parking spot. The demonstration in this paper comprises a parking car and a micro aerial vehicle (MAV) connected in real-time through the IoT platform as well as the smart phone application where the car is controlled and supervised.

Urban road networks may benefit from left turn prohibition at signalized intersections regarding capacity, for particular traffic demand patterns. The objective of this paper is to propose a method for minimizing the total travel time by prohibiting left turns at intersections. With the flows obtained from the stochastic user equilibrium model, we were able to derive the stage generation, stage sequence, cycle length, and the green durations using a stage-based method which can handle the case that stages are sharing movements. The final output is a list of the prohibited left turns in the network and a new signal timing plan for every intersection. The optimal list of prohibited left turns was found using a genetic algorithm, and a combination of several algorithms was employed for the signal timing plan. The results show that left turn prohibition may lead to travel time reduction. Therefore, when designing a signal timing plan, left turn prohibition should be considered on a par with other left turn treatment options.

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