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.
Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir Interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the Extended Virtual Sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.
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...
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