Cooperative Intersection Crossing Over 5G

Abstract

Autonomous driving is a safety critical application of sensing and decision-making technologies. Communication technologies extend the awareness capabilities of vehicles, beyond what is achievable with the on-board systems only. Nonetheless, issues typically related to wireless networking must be taken into account when designing safe and reliable autonomous systems. The aim of this work is to present a control algorithm and a communication paradigm over 5G networks for negotiating traffic junctions in urban areas. The proposed control framework has been shown to converge in a finite time and the supporting communication software has been designed with the objective of minimizing communication delays. At the same time, the underlying network guarantees reliability of the communication. The proposed framework has been successfully deployed and tested, in partnership with Ericsson AB, at the AstaZero proving ground in Goteborg, Sweden. In our experiments, three heterogeneous autonomous vehicles successfully drove through a 4-way intersection of 235 square meters in an urban scenario.

Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

“…Finally, numerical analyses have highlighted the effectiveness and the capability of the DNMPC in guaranteeing an improvement of energy performance for the EVs platoon with respect to a basic scenario, where the traditional CTH spacing policy is used, with an average energy saving of approximately 2.2%. Future works could include: (i) an extensive validation of the DNMPC approach via a virtual testing co-simulation, i.e., the coordinated simulation of heterogeneous sub-models independently developed (interested readers may refer to [52][53][54] for further details), where not only a more detailed EV dynamic model (including low-level controllers, an electric motor model and inverter devices) is considered, but SUMO can also be exploited for reproducing the road network and realistic traffic conditions; and (ii) the experimental validation of the DNMPC via self-driving cars and leveraging real-time control architectures similar to the ones proposed in [32,55].…”

“…Consider a heterogeneous e-platoon consisting of N vehicles plus an additional one, labelled as 0, acting as a leader in providing the reference behaviour to the whole vehicular network. The platoon is arranged as a convoy, with vehicles travelling along a straight road and able to share their position, speed and acceleration information via V2V wireless communication networks (based on IEEE 802.11p communication standard or 5G communication) [31,32]. In our technological scenario, each EV is equipped with an on-board inertial sensor and a GPS receiver for measuring its state information, as well as with transmitting devices enabling the connectivity among vehicles within the e-platoon [33].…”

Architecture for Resource Allocation in the Internet of Vehicles for Cooperating Driving System

“…A 5G network-based control framework [27] is proposed and deployed in Sweden that minimizes communication delay in managing intersections.…”

Data-Driven Capacity Planning for Vehicular Fog Computing

“…C LOUD computing has long been the dominant solution for handling large scales of data generated from various sources [1]. However, the traditional cloud strategies are not feasible to the low latency requirement imposed by the emerging vehicular applications, such as cooperative intersection crossing [2] and lane change scheduling [3] for autonomous vehicles. Fog computing, as a promising alternative, moves the computation resource close to the edge of the network [4], and reduces network latency by its proximity to the end-users and dense geographical distribution [5].…”