A Novel Temporal Multi-Gate Mixture-of-Experts Approach for Vehicle Trajectory and Driving Intention Prediction

Accurate Vehicle Trajectory Prediction is critical for automated vehicles and advanced driver assistance systems. Vehicle trajectory prediction consists of two essential tasks, i.e., longitudinal position prediction and lateral position prediction. There is a significant correlation between driving intentions and vehicle motion. In existing work, the three tasks are often conducted separately without considering the relationships between the longitudinal position, lateral position, and driving intention. In this paper, we propose a novel Temporal Multi-Gate Mixture-of-Experts (TMMOE) model for simultaneously predicting the vehicle trajectory and driving intention. The proposed model consists of three layers: a shared layer, an expert layer, and a fully connected layer. In the model, the shared layer utilizes Temporal Convolutional Networks (TCN) to extract temporal features. Then the expert layer is built to identify different information according to the three tasks. Moreover, the fully connected layer is used to integrate and export prediction results. To achieve better performance, uncertainty algorithm is used to construct the multi-task loss function. Finally, the publicly available CitySim dataset validates the TMMOE model, demonstrating superior performance compared to the LSTM model, achieving the highest classification and regression results. Keywords: Vehicle trajectory prediction, driving intentions Classification, Multi-tas

Deep Learning Techniques for Mobility Prediction and Management in Mobile Networks

Trajectory prediction is an important research topic in modern mobile networks (e.g., 5G and beyond 5G) to enhance the network quality of service by accurately predicting the future locations of mobile users, such as pedestrians and vehicles, based on their past mobility patterns. A trajectory is defined as the sequence of locations the user visits over time. The primary objective of this thesis is to improve the modeling of mobility data and establish personalized, scalable, collective-intelligent, distributed, and strategic trajectory prediction techniques that can effectively adapt to the dynamics of urban environments in order to facilitate the optimal delivery of mobility-aware network services. Our proposed approaches aim to increase the accuracy of trajectory prediction while minimizing communication and computational costs leading to more efficient mobile networks. The thesis begins by introducing a personalized trajectory prediction technique using deep learning and reinforcement learning. It adapts the neural network architecture to capture the distinct characteristics of mobile users’ data. Furthermore, it introduces advanced anticipatory handover management and dynamic service migration techniques that optimize network management using our high-performance trajectory predictor. This approach ensures seamless connectivity and proactively migrates network services, enhancing the quality of service in dense wireless networks. The second contribution of the thesis introduces cluster-level prediction to extend the reinforcement learning-based trajectory prediction, addressing scalability challenges in large-scale networks. Cluster-level trajectory prediction leverages users’ similarities within clusters to train only a few representatives. This enables efficient transfer learning of pre-trained mobility models and reduces computational overhead enhancing the network scalability. The third contribution proposes a collaborative social-aware multi-agent trajectory prediction technique that accounts for the interactions between multiple intra-cluster agents in a dynamic urban environment, increasing the prediction accuracy but decreasing the algorithm complexity and computational resource usage. The fourth contribution proposes a federated learning-driven multi-agent trajectory prediction technique that leverages the collaborative power of multiple local data sources in a decentralized manner to enhance user privacy and improve the accuracy of trajectory prediction while jointly minimizing computational and communication costs. The fifth contribution proposes a game theoretic non-cooperative multi-agent prediction technique that considers the strategic behaviors among competitive inter-cluster mobile users. The proposed approaches are evaluated on small-scale and large-scale location-based mobility datasets, where locations could be GPS coordinates or cellular base station IDs. Our experiments demonstrate that our proposed approaches outperform state-of-the-art trajectory prediction methods making significant contributions to the field of mobile networks

Optical Rotary Sensors for Avionic Applications

RÉSUMÉ Cette thèse concerne des nouveaux capteurs optiques dédiés aux systèmes de contrôle de vol d’avions «fly-by-wire (FBW)». Les capteurs de déplacement sont utilisés dans les systèmes de contrôle de vol pour détecter la distraction du pilote, les déplacements de l'actionneur et ceux de la surface de vol. Actuellement, les capteurs « Rotary variable displacement transducers - RVDTs» utilisés dans les systèmes de contrôle de vol d'avions FBW sont les capteurs basés sur des circuits magnétiques et électroniques analogiques. Donc, une interface électronique est nécessaire pour la démodulation et numérisation des signaux reçus. Par conséquent, des paires de fils longs torsadés sont utilisés pour connecter le RVDT à l’ordinateur installé à bord de l’avion. Les paires de fils torsadés sont lourds et sensibles aux interférences électromagnétiques (IEM) et aux coups de foudre qui peuvent se produire pendant le vol. Nous proposons des capteurs optiques intelligents pour réduire le poids de l’avion, la consommation du carburant pour un environnement vert, l’IEM et pour utiliser moins de pièces métalliques afin de protéger davantage l’avion contre les coups de foudre. La conception des encodeurs de capteurs optiques rotatifs (Optical rotation sensors - ORSs) est basée sur trois exigences importantes, soient la fiabilité, la linéarité, et l’exactitude de mesures. Ces capteurs intégrés dans le système de vol doivent être intelligents. Pour la fiabilité, la réponse du capteur est calculée à partir du ratio des deux puissances optiques ou celui de la différence divisée par la somme de ces deux puissances optiques. Cependant, pour la linéarité, la réponse du capteur consiste en une relation linéaire avec le paramètre à mesurer qui est l’angle de rotation. Quant à l’exactitude, l’erreur doit être moins de 1% sur toute la gamme de mesures. De plus, pour un capteur intelligent, le capteur basé sur des circuits analogiques, les convertisseurs au monde numérique et l’étape de démodulation doivent être emballés dans un boîtier commun. Dans un premier prototype, un capteur de déplacement ratio-métrique, auto-référant, analogique et optique a été proposé pour les applications avioniques. La position de rotation est déterminée par le ratio de deux puissances lumineuses réfléchie et transmise qui rendent le capteur indépendant de fluctuations de puissance. L’encodeur multi-gradient original proposé compense pour l’usage d’une source non-uniforme.----------ABSTRACT This thesis is on novel optical sensors for smart sensor system needed in flight control system (FCS) of fly-by-wire (FBW) aircraft. Displacement sensors are needed in FBW-FCS to detect pilot inceptors, actuator displacements, and flight control surface movement. Currently, the sensors used for rotary variable displacement transducers (RVDTs) are analog electronic sensors, hence an electronic interface is needed for demodulation and digitization of analog signals. As a result, long twisted wires are drawn from the sensor to the flight control computer (FCC) interface which are heavy and susceptible to electromagnetic interference (EMI) and lightning strike. By proposing smart optical sensors, we aim to reduce the aircraft weight to decrease the fuel usage towards a greener environment, reduce EMI, and protect the aircraft against a lightning strike by using fewer metallic parts. The encoders of the optical rotation sensors (ORS) are designed based on three important requirements of reliability, linearity, and accuracy. In addition, they must be smart sensors to be integrated into the smart sensor system needed in FBW aircraft. For reliability requirements, the designed sensor response is the ratio of two optical powers or the ratio of the difference to the sum of two optical powers. For linearity requirement, the sensor response must be a linear relation with the measurand which is the rotation angle. For accuracy requirement, the error should be less than 1% over the full range. In addition, for a smart sensor, the analog sensor and the electronics for digitization and demodulation have to be packaged into a single housing.In the first design, an optical, analog, self-referencing, ratio-metric, smart displacement sensor is proposed for avionic applications. The position of rotation is determined by an encoder by the ratio of the transmitted and reflected light powers, which makes the sensor independent of power fluctuations. A single multi-gradient encoder design compensates for the use of a non-uniform source. An anti-reflection coated glass window with the outer diameter of 27mm is used with an encoder pattern mapped on it using aluminum deposition. The experimental results show that the ratio of the transmitted and reflected powers has an accuracy of 0.53% over the full range, matching the specifications for avionic applications