An Evidential Deep Network Based on Dempster-Shafer Theory for Large Dataset

International audienceWe introduce a novel deep neural network architecture based on Dempster-Shafer theory capable of handling large image datasets with numerous classes, such as ImageNet. Our approach involves analyzing images through multiple experts, composed of convolutional deep neural networks that predict mass functions. These experts are then merged using Dempster's rule, thereby returning a set of potential classes by selecting the best expected utility based on the previously computed mass functions. Our innovative algorithm can identify the best set of classes among the 2 K possible sets for K classes while maintaining a complexity of O(K log(K)). To illustrate our approach, we apply it to an out-of-distribution example search problem, demonstrating its efficiency

Sampled Data Radial Basis Function Neural Network Observer Design for Nonlinear Vehicle Dynamics

International audienceAccurately estimating the lateral velocity of automatic ground vehicles is a complex task, especially when faced with sensor sampled measurements and unfamiliar mathematical models. In order to overcome these difficulties, the study presented here proposes a novel approach that makes use of a sampled data neural network observer. In order to fill in the information gap between successive samples, a compensating injector is introduced to the continuous state observer on which the observer is based. In order to replicate unknown dynamic vehicle systems, a radial basis function neural network is also implemented. A special weight update mechanism is used to update the weights continually. The Lyapunov methodology is used to demonstrate the stability of the suggested method. Experimental findings validate the effectiveness of the sampled data neural network observer, providing promising insights for improving lateral velocity estimation and enhancing the control and stability of autonomous vehicle systems

Obstacle Avoidance, Path Planning and Control for Autonomous Vehicles

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Continuous–Discrete Time Neural Network Observer for Nonlinear Dynamic Systems Application to Vehicle Systems

International audienceThis paper proposes a novel continuous-discrete (sampled data) time neural network (NSNN) observer for nonlinear systems. It can therefore be applied to systems with a high degree of non-linearity with no prior knowledge of the system dynamics. The proposed observer is a three-layer feedforward neural network that has been intensively trained using the error backpropagation learning algorithm, which includes an e-modification term to ensure robustness of the observer. A structure of the output predictor with a corrective term is added in the structure of the NN observer to overcome the problem of discrete time measurement. Simulations using MATLAB and CarSim are illustrated to demonstrate the performance of the proposed state observer strategy to reconstruct the state variables and parameters of a vehicle system

A belief function-based forecasting link breakage indicator for VANETs

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