Predict the performance of driverless car through the cognitive data analysis and reliability analysis based approach

Abstract

This research aims to predict the performance of driverless cars by employing a cognitive data analysis and reliability analysis-based approach. With the advancement of autonomous vehicle technology, there is a growing need to accurately assess and forecast the capabilities and reliability of these vehicles. To address this challenge, this study proposes a novel methodology that combines cognitive data analysis techniques with reliability analysis. The cognitive data analysis approach utilizes decision tree and confusion matrix and multi regression analysis to analyze complex sensory data collected from driverless cars, enabling the extraction of valuable insights regarding their performance. Furthermore, the reliability analysis-based approach assesses the robustness and dependability of the autonomous systems, taking into account various factors such as system failures, environmental conditions, and human-machine interactions. By integrating these two approaches, a comprehensive framework is developed for predicting the performance of driverless cars, aiding in the identification of potential issues and enhancing the overall safety and efficiency of autonomous driving systems. The proposed methodology contributes to the emerging field of autonomous vehicles and provides valuable insights for researchers, developers, and policymakers working towards the widespread adoption of driverless technology.

Keywords

• Confusion matrix
• Decision tree
• Descriptive analysis
• Reliability analysis