Driver distraction detection (3D) is essential in improving the efficiency and safety of transportation systems. Considering the requirements for user privacy and the phenomenon of data growth in real-world scenarios, existing methods are insufficient to address four emerging challenges, i.e., data accumulation, communication optimization, data heterogeneity, and device heterogeneity. This paper presents an incremental and cost-efficient mechanism based on federated meta-learning, called ICMFed, to support the tasks of 3D by addressing the four challenges. In particular, it designs a temporal factor associated with local training batches to stabilize the local model training, introduces gradient filters of each model layer to optimize the client–server interaction, implements a normalized weight vector to enhance the global model aggregation process, and supports rapid personalization for each user by adapting the learned global meta-model. According to the evaluation made based on the standard dataset, ICMFed can outperform three baselines in training two common models (i.e., DenseNet and EfficientNet) with average accuracy improved by about 141.42%, training time saved by about 54.80%, communication cost reduced by about 54.94%, and service quality improved by about 96.86%.
Driven by technologies and demands, the modern transportation system has developed from intelligent transportation systems (ITS) to autonomous transportation systems (ATS) to resolve intertwined demands and supplies with few human interventions. In ATS, personal mobility service (PMS) is the service that can sense real-time traffic conditions comprehensively, learn travelers’ preferences accurately, recommend multimodal travel options appropriately, and provide service responses timely to elevate the level of personalization and intelligence in smart mobility services. Since current PMS widely employs centralized approaches (CPMS) to process massive sensitive data from individuals and support diverse edge devices, resulting in high pressure in privacy protection and performance balancing, this paper presents a federated PMS (FPMS) and its design architecture in logical and physical views by adopting federated learning to provide multimodal, dynamic, and personalized travel options with system-saving safety and efficiency guaranteed. Moreover, through an extensive evaluation, the performances of CPMS and FPMS are compared to reveal the merits of FPMS in reducing costs and latency.
Looking ahead to the future-stage autonomous transportation system (ATS), personal mobility service (PMS) aims to provide the recommended travel options based on both microscopic individual travel demand and the macroscopic supply system objectives. Such a goal relies on massive heterogeneous data to interpret and predict user travel intentions, facing the challenges caused by prevalent centralized approaches, such as an unbalanced utilization rate between cloud and edge, and data privacy. To fill the gap, we propose a federated logit model (FMXL), for estimating user preferences, which integrates a discrete choice model—the mixed logit model (MXL), with a novel decentralized learning paradigm—federated learning (FL). FMXL supports PMS by (1) respectively performing local and global estimation at the client and server to optimize the load, (2) collaboratively approximating the posterior of the standard mixed logit model through a continuous interaction mechanism, and (3) flexibly configuring two specific global estimation methods (sampling and aggregation) to accommodate different estimation scenarios. Moreover, the predicted rates of FMXL are about 10% higher compared to a flat logit model in both static and dynamic estimation. Meanwhile, the estimation time has been reduced by about 40% compared to a centralized MXL model. Our model can not only protect user privacy and improve the utilization of edge resources but also significantly improve the accuracy and timeliness of recommendations, thus enhancing the performance of PMS in ATS.
Along with the trend towards an autonomous transportation system (ATS), the intelligence of personal mobility service (PMS) can be further lifted by sensing travelers' statuses comprehensively, learning behavior patterns accurately, providing travel options appropriately, and giving service responses timely. Such a process relies on a seamless information flow, which shall address data silos caused by laws and regulations about privacy. This paper proposes a federated architecture for PMS, called FPMS, which adopts federated learning, to provide personalized multi-modal options by aggregating personal data in a privacy-preserving way, and utilizing idle resources of personal devices within the service cluster. In general, by analyzing the physical objects involved, functions required, and data processed, a reference architecture of FPMS is designed to guide its construction in ATS effectively and efficiently. Moreover, a performance evaluation between FPMS and conventional centralized PMS is also presented to reveal the advantages of FPMS in saving service costs.
The vast development of the next-generation network (NGN) impels its integration with emerging technologies, such as big data, artificial intelligence, and federated learning, to deliver autonomous and intelligent services in various areas. Notably, in modern transportation systems (TSs), the advances of NGN enable a transformation toward an autonomous transportation system (ATS), which can bridge the demand and supply through a self-actuating cycle (sensing, learning, rearranging, and reacting). Since NGN-enabled ATS is still in its infancy, a concrete vision is missing to forge a common research ground. To fill the gap, this article is intended to elucidate NGN-enabled ATS by first discussing its intrinsic difference against the conventional TSs (CTSs) and then depicting its service blueprint in fostering more intelligent and autonomous mobility services. After that, a full-scale ATS service design reference is proposed to ensure the generality, adaptivity, compatibility, interoperability, and scal-ability of services in and across its development stages, representing the levels of autonomy from partial to high to full automation. Furthermore, its superiority is discussed through a preliminary evaluation of personal mobility service based on centralized and federated learning. Finally, open questions and future research directions of this emerging topic are also discussed.
A personal mobility service (PMS) is developed to support personalized travel options for users in autonomous transportation systems (ATS), based on a macro-system state and micro-user behavior. However, this functionality necessitates processing and transmitting vast amounts of data, raising concerns about user privacy protection during data processing and transmission within the PMS. Furthermore, the PMS must be maintained and perform well, while preserving privacy. Therefore, we propose a novel federated PMS, denoted as a FPMS. Specifically, the FPMS can serve users’ personal mobility needs by facilitating the collaboration between the physical and information domains. Then, a common framework for FPMS architectures, which captures the features of ATSs, is proposed and discussed from both physical and logical perspectives, which include both the logical architecture and physical architecture; and we present the key algorithms for the FPMS, in conjunction with a artificial neural network (ANN). Additionally, in static estimation scenarios, the FPMS demonstrated a similar accuracy for three different models compared to the traditional PMS, while reducing the computing time by approximately 60% and communication resource consumption by approximately 85%.
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