This study introduces a hybrid sensor fusion approach that integrates Schreiber’s nonlinear filter with traditional filtering methods to enhance the performance of IMU-based systems in autonomous vehicles. As autonomous vehicles grow more dependent on Inertial Measurement Unit (IMU) data for real-time stability and control, the need for resilient and accurate sensor fusion becomes critical. This research addresses that need by introducing a method capable of maintaining robustness under highly dynamic and uncertain conditions. Accelerometer and gyroscope data from an IMU are first fused using a complementary filter. The fused signals are then refined by phase-space reconstruction and local manifold projection, improving noise resilience and maintaining system dynamics. Two datasets are used to assess the methodology: one was collected indoors with a smartphone, and another was captured outdoors using a Bosch sensor in various environmental settings. The proposed method demonstrates superior noise reduction, greater resistance to outliers, and improved signal consistency compared to conventional complementary and Kalman filters. The findings demonstrate how chaos-based nonlinear filtering may improve the reliability of sensor fusion on a variety of sensing platforms in highly dynamic environments. Given the importance of IMU data for maintaining vehicle stability, this study seeks to support the development of more stable autonomous transportation systems.

Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country. Besides the huge amount of data, another problem is the issue of validation and verification. For self-driving cars, this implies a level of complexity using traditional methods that calls into question the economics of the already existing system. Fuzzy Petri nets provide an alternative solution to both problems. They allow us to obtain a model that accurately describes the reliability of a node through its dynamics, which is essential in perception since the more reliable a node is, the smaller the deep learning mesh required. In this paper, we outline the analysis of a traffic node’s safety using Petri nets and fuzzy analysis to gain information on the reliability of the node, which is essential for the modeling of self-driving cars, due to the deep learning model of perception. The reliability of the dynamics of the node is determined by using the modified fuzzy Petri net procedure. The need for a fuzzy extension of the Petri net was developed by knowledge of real traffic databases.