## 1 Introduction  
Cars are becoming increasingly equipped with sensing, computing and communication capabilities. Fed by advances in many areas of computer science (from signal processing and wireless communications to computer vision, human-computer interaction and many others), technology has been yielding significant advances in terms of safety during the last few years. Advanced Driving Assistance Systems (ADAS) are now commonplace, covering features such as automatic braking, collision protection and emergency assistance [1]. Likewise, better connectivity through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications [2] enables the automobiles to assemble accurate views of the areas they traverse, and thereby optimize their routes and fuel consumption [3]. Assistance to find parking spaces and richer entertainment services are among recent proposals too [4; 5]. However, the major change will be that of autonomous driving, featuring adaptive cruise control, self-parking, highway autopilot and more [6]. In this paper, we investigate a combination of two ideas enabled by V2V communications for the optimization of traffic flow and fuel consumption, leaning on a certain level of automation in the driving:* On the one hand, we consider virtual traffic lights (VTLs), which were proposed to overcome the inefficiency of the classic traffic light systems by letting the vehicles in the vicinity of an intersection create and coordinate the traffic light signals by themselves [7]. An autonomous driving system can take control of the steering wheel, brake and throttle to pass the intersection safely, auto-adjusting the path and the speed to avoid collisions and maximize the fluency of the traffic. * On the other hand, we look at _platooning_, a mode of cooperative driving in which groups of vehicles travel as single units (the platoons) for some time, by maintaining a very small and nearly constant distance between them [8; 9]. Low-latency communications and autonomous driving features can eliminate the gaps needed for human reaction, increasing road capacity, reducing congestion and achieving greater fuel economy due to reduced air resistance [10; 11]. Whereas previous works have dealt with virtual traffic lights that coordinate individual vehicles only, and platooning has been predominantly investigated in highway scenarios, in this paper we present an approach to coordinate groups of vehicles that form and evolve, whenever possible, at the intersections of city roads. Once the platoons are formed, we estimate and disseminate the arrival times to the intersections along their routes. That information is maintained and updated periodically at each intersection, so that any passing vehicles can check whether they might benefit from joining any nearby platoon. The information is also used to adapt the VTLs to facilitate the transit of the platoons. We present protocols for the management of VTLs and urban platoons, built upon a virtual infrastructure for urban V2V communications [12; 13] that turns intersections into virtual containers of data and algorithms that facilitate platoon join maneuvers (hence the title \"intersection intelligence\"). The proposal is assessed by means of simulations, measuring the performance achieved with various communication protocols in terms of travel duration, fuel consumption and number of platoon join operations completed successfully. The paper is organized as follows. Section 2 provides a summary of the state-of-the-art in approaches to virtual traffic lights (in cities) and platooning (in highways). Then, Section 3 includes some background information on the virtual communications infrastructure from our previous works, and on the routing protocols we used in the simulations. Section 4 presents the main procedures that we have devised to manage the urban platoons and coordinate the virtual traffic lights. The evaluation study is described in Section 5, with Section 6 discussing the results.