Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

Platooning is highly tractable for enabling fuel savings for autonomous and semi-autonomous cars and trucks. Safety concerns are one of the main impediments that need to be overcome before vehicle platoons can be deployed on ordinary roads despite their readily available technical feasibility. Simulation studies remain vital for evaluating platoon safety applications primarily due to the high cost of field tests. To this end, we present PlatoonSAFE, an open-source simulation tool that promotes the simulation studies of fault tolerance in platooning by enabling the monitoring of transient communication outages during runtime and assigning an appropriate performance level as a function of the instantaneous communication quality. In addition, PlatoonSAFE facilitates the simulation of several emergency braking strategies to evaluate their efficacy in transitioning a platoon to a fail-safe state. Furthermore, two Machine Learning (ML) models are integrated into PlatoonSAFE that can be employed as an onboard prediction tool in the platooning vehicles to facilitate online training of ML models and real-time prediction of communication, network, and traffic parameters. In this paper, we present the PlatoonSAFE structure, its features and implementation details, configuration parameters, and evaluation metrics required to evaluate the fault tolerance of platoon safety applications.

Cooperative Adaptive Cruise Control (CACC) utilizes Vehicle-to-Vehicle (V2V) communications and onboard sensors to facilitate cooperative maneuvering among a group of automated vehicles called a vehicle string. Such string formation of automated vehicles enables improved safety, fuel efficiency, traffic flow, and road capacity. A vehicle using CACC computes its acceleration through information obtained from its preceding vehicle and/or the Leading Vehicle (LV) of the string through V2V communications. However, wireless communication is susceptible to inevitable transient outages due to irregular packet losses, which has severe consequences on the safety and stability of a vehicle string. To address this problem, this paper proposes an enhancement to an existing CACC algorithm; the idea is that when a vehicle does not receive information from its intended sources, i.e., the LV and the predecessor, for a certain duration, it uses information from the closest available longitudinal neighbors to the intended sources to compute its desired acceleration. Furthermore, we also investigate the possibility of using such information for training Machine Learning (ML) models and making predictions on the desired accelerations of the intended sources. Rigorous simulation studies demonstrate that when information from alternative sources is utilized during transient outages, a significant improvement in terms of safety, string stability, and fuel efficiency can be observed compared to the existing CACC. Moreover, the proposed approach can handle transient outages without requiring changes in the CACC communication topology, increasing the number of transmitted messages, or degrading string performance, as proposed by many works in the literature.