The high mobility characteristics of connected vehicles present noteworthy difficulties in the domain of federated learning. Based on our understanding, current federated learning strategies do not tackle the challenge of continuously training multiple models for vehicles in constant motion, which are subject to variable network conditions and changing environments. In response to this challenge, we have created and implemented iFLOW, a versatile and intelligent multi-model federated learning infrastructure specifically designed for highly mobile-connected vehicles. iFLOW addresses these challenges by integrating four key aspects: (1) a strategically devised model allocation algorithm that dynamically selects vehicle computing units for distinct model training tasks, optimizing for both resource efficiency and performance; (2) a dynamic client vehicle joining mechanism that ensures smooth participation of vehicles, even in the face of signal loss or weak connectivity, mitigating disruptions in the training process; (3) integration of a large language model (Llama3.3 70B) as an intelligent arbiter for decision-making within the framework, enhancing adaptability and robustness; and (4) real-world deployment and testing on distributed vehicular devices to validate the approach. The experimental evaluation demonstrates that iFLOW allows multiple models to train asynchronously and outperform centralized training. These results affirm the effectiveness of iFLOW in practical, real-world scenarios involving highly mobile vehicular networks.

Abstract Cyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the Internet-of-Things (IoT), and smart cities. An important property of a CPS is tolerance; i.e., its ability to function safely under possible disturbances and uncertainties in the actual operation. In this paper, we introduce a new, expressive notion of tolerance that describes how well a controller is capable of satisfying a desired system requirement, specified using Signal Temporal Logic (STL), under possible deviations in the system. Based on this definition, we propose a novel analysis problem, called the tolerance falsification problem, which involves finding small deviations that result in a violation of the given requirement. We present a novel, two-layer simulation-based analysis framework and a novel search heuristic for finding small tolerance violations. To evaluate our approach, we construct a set of benchmark problems where system parameters can be configured to represent different types of uncertainties and disturbances in the system. Our evaluation shows that our falsification approach and heuristic can effectively find small tolerance violations.

The exploration of software Over-the-Air (OTA) updates for automotive applications is currently very limited. Our work introduces an edge-assisted framework for automotive OTA updates that carefully accounts for various factors, including different software models in vehicles, communication distances, and cluster sizes. We present valuable insights using key evaluation metrics like update speed, data transmission efficiency, and success rate, accompanied by a thorough scalability analysis. Our research involves three distinct vehicle software models: ResNet-18 (46.8 MB), ResNet-50 (102.5 MB), and Faster R-CNN (175.2 MB). These models are used to evaluate update performance across eight distance categories ranging from 0 to 21 meters with a 3-meter interval. We also utilize diverse computing platforms to assess the success rate and conduct a comprehensive scalability analysis. This innovative approach significantly advances our understanding and practical implementation of OTA updates in the automotive field.

Ride-sharing has important implications for improving the efficiency of mobility-on-demand systems. However, it remains a challenge due to the complex dynamics between vehicles and requests. This paper presents a decentralized ride-sharing algorithm suitable for shared autonomous vehicles (SAVs) deployment. The ride-sharing problem is formulated as a multi-agent reinforcement learning problem. We explore state representation with the request-vehicle graph to encode shareability and potential coordination information. We use a graph attention network to build a hierarchical structure that unifies ride-sharing assignments with rebalancing and handles real-world scenarios where hundreds of user requests can be associated with vehicles. We show results in both generic grid-world and SUMO simulation with real-world data from the Manhattan area. We empirically demonstrate that our proposed approach can achieve similar performance compared with a state-of-the-art centralized optimization method and higher computation efficiency.

A Digital Twin is a digital replica of a living or nonliving physical entity, and this emerging technology attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, a mobility digital twin (MDT) framework is developed, which is defined as an artificial intelligence (AI)-based data-driven cloud–edge–device framework for mobility services. This MDT consists of three building blocks in the physical space (namely,Human,Vehicle, andTraffic), and their associated Digital Twins in the digital space. An example cloud–edge architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to fulfill its digital functionalities of storage, modeling, learning, simulation, and prediction. A case study of the personalized adaptive cruise control (P-ACC) system is conducted, which integrates the key microservices of all three digital building blocks of the MDT framework: 1) theHuman Digital Twinwith user management and driver type classification; 2) theVehicle Digital Twinwith cloud-based advanced driver-assistance systems (ADAS); and 3) theTraffic Digital Twinwith traffic flow monitoring and variable speed limit. Future challenges of the proposed MDT framework are discussed toward the end of the article, including standardization, AI for computing, public or private cloud service, and network heterogeneity.

Differential space-time coded (DSTC) modulation is a viable scheme for MIMO communication systems that do not utilize channel information at the receiver either because of rapid channel variation and/or limited training data. The drawback of DSTC is the 3 dB performance loss because of incoherent detection. In order to alleviate this performance loss, we develop a semi-coherent receiver structure in which partial channel information is acquired and utilized to generalize the traditional non-coherent receiver. This partial channel information takes the form of a subspace where the channel parameter vector must reside in. It is blindly extracted from the receive data without additional pilot assistance.

Space-time block coded (STBC) transmission has been established as an efficient tool to enhance communication performance over wireless fading channels. The success of STBC decoding relies on accurate channel knowledge at receivers. In this work, we present a channel estimation approach that does not require training data to estimate unknown channels. Focusing on STBC from orthogonal designs, we present channel identification conditions that are largely verifiable in terms of the code and the antenna array configuration. We also develop a simple subspace-based algorithm to identify the unknown space-time channel matrix for complex transmission. Finally, we present simulation test results to illustrate the performance of the proposed method.

Linear space-time block codes (STBC) are well established as an efficient means to improve the performance of wireless MIMO communication systems. Their success is contingent upon the accurate knowledge of the channel parameters. In this paper we present a blind channel estimation scheme for general linear STBC. We furnish a set of identification conditions that are largely verifiable in terms of the code parameters and the antenna array configuration. We also present a simple algorithm to estimate the unknown channel matrix. Finally we supply some simulation results to illustrate the performance of the proposed scheme.

Time reversed space-time block code (TR-STBC) was originally proposed to handle frequency selective fading channels. Its detection requires accurate channel knowledge at the receiver side. In this work, we present a channel estimation approach that does not require training data under TR-STBC encoding. We provide identification conditions that are based on the known code parameters as well as channel matrix rank. We present a simple subspace algorithm for channel estimation. Additionally, simulations results are presented to highlight the performance of the estimation scheme.