Collision avoidance is one of the most promising applications for vehicular networks, dramatically improving the safety of the vehicles that support it. In this paper, we investigate how it can be extended to benefit vulnerable users, e.g., pedestrians and bicycles, equipped with a smartphone. We argue that, owing to the reduced capabilities of smartphones compared to vehicular on-board units, traditional distributed approaches are not viable, and that multi-access edge computing (MEC) support is needed. Thus, we propose a MEC-based collision avoidance system, discussing its architecture and evaluating its performance. We find that, thanks to MEC, we are able to extend the protection of collision avoidance, traditionally thought for vehicles, to vulnerable users without impacting its effectiveness or latency.

The purpose of this paper is to evaluate the performance of a system for vehicle-with-vehicle and vehicle-with-pedestrian collision detection when cellular vehicle-to-infrastructure (C-V2I) is adopted as a communication technology. In particular, we are mainly interested in the number of collisions that could be avoided and in the number of false positive alerts (i.e., alert messages referring to situations of low or no danger, that the system delivers to the users). Indeed, a low number of false positive alerts is essential in establishing user confidence in the reliability of alerts received through the system.The remainder of this paper is organized as follows: Section II reviews the research related to the automotive collision avoidance application. Our reference scenario is introduced in Section III, while Section IV presents the design of the automotive collision avoidance system, along with the detection algorithm. The description of the methodology for our simulations and the output analysis technique are in Section V. Section VI contains the results obtained; the paper closes with our conclusions and future research directions in Section VII. II. RELATED WORKThere are several works in the literature that are related to safety applications in the automotive domain (e.g., [5]). Many of these works, such as [6] and [7], propose collision avoidance and collision detection applications that do not leverage any mobile network infrastructure. In particular [6] focuses on collisions between vehicles and pedestrians in industrial plants. In this case, positioning is achieved using a combination of GPS, MEMS and smart sensors, while the type of wireless communication to the control center is not specified. In [7], White et al. propose a way to automatically detect a collision after it has occurred, using smartphone accelerometers to reduce the time gap between the actual collision and the first aid dispatch.Our solution proposes a trajectory-based collision detection system based on a state-of-the art algorithm that we enhanced to match our needs. The same base-algorithm has been used, in different flavors, in [8] and [9].[8] offers a top-down and specification driven design of an adaptive, peer-to-peer based collision warning system, while [9] proposes a V2V-like approach. However, those two works offer little simulation results. In particular, [8] only focuses on the collision avoidance algorithm, with little attention paid to implementation and network infrastructure.[9] provides some simulation results, Abstract-One of the key applications envisioned for C-V2I (Cellular Vehicle-to-Infrastructure) networks pertains to safety on the road. Thanks to the exchange of Cooperative Awareness Messages (CAMs), vehicles and other road users (e.g., pedestrians) can advertise their position, heading and speed and sophisticated algorithms can detect potentially dangerous situations leading to a crash. In this paper, we focus on the safety application for automotive collision avoidance at intersections, and study the effect...

All the content consumed by mobile users, be it a web page or a live stream, undergoes some processing along the way; as an example, web pages and videos are transcoded to fit each device's screen. The recent multi-access edge computing (MEC) paradigm envisions performing such processing within the cellular network, as opposed to resorting to a cloud server on the Internet. Designing a MEC network, i.e., placing and dimensioning the computational facilities therein, requires information on how much computational power is required to produce the contents needed by the users. However, real-world demand traces only contain information on how much data is downloaded. In this paper, we demonstrate how to enrich demand traces with information about the computational power needed to process the different types of content, and we show the substantial benefit that can be obtained from using such enriched traces for the design of MEC-based networks.

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In this paper, we present an enhanced Collision Avoidance (eCA) service that leverages vehicle connectivity through a cellular network to avoid vehicle collisions and increase road safety at intersections. The eCA service is assumed to be deployed at the edge of the network, thus curbing the latency incurred by the communication process. The core of the eCA service is composed of a Collision Avoidance Algorithm (CAA), and a Collision Avoidance Strategy (CAS). The former predicts the vehicle's future trajectory through the positional information advertised by periodic beacons and detects if two vehicles are on a collision course. The latter decides which of the vehicles potentially involved in a collision should yield. The vehicles are then notified of both the impending danger and of the actions needed to avoid it. We have simulated our solution using SUMO (Simulation of Urban MObility) and ns-3 (network simulator 3) with the LENA (LTE-EPC Network simulAtor) framework on a Manhattan-grid road topology, and observed its good performance in terms of avoided collisions percentage as a function of vehicle speed and different vehicles densities. INDEX TERMS Advanced driver assistance systems, automated vehicles, collision avoidance, edge computing, intelligent vehicles, vehicle safety.