## 7 Big Data in Autonomous Vehicles  
The use of large amounts of data is crucial to the successful operation of autonomous vehicles from both a safety and productivity perspective. The list that follows is an overview of crucial details discovered as a result of our study:* **Security Challenges due to big data utilization in AVs:** As autonomous vehicles (AVs) increasingly exploit big data to enhance their operations, they are simultaneously exposed to a range of security concerns, including cyber security vulnerabilities. One such difficulty is that autonomous vehicles (AVs) are more vulnerable to cyber-attacks. According to [71], the nature of these vulnerabilities is multifaceted, including data breaches, illegal access, and the manipulation of vehicle sensor data. According to the findings of this study, in order to further enhance the safety of the utilization of big data applications in autonomous vehicles, it is essential to create complex encryption strategies and to create algorithms based on machine learning for anomaly detection. * **Designing robust path-following functionality through big data analysis:** The ability of autonomous vehicles to navigate predetermined routes in a manner that is both secure and economical is one of the most important characteristics of these vehicles. According to [107], the processing of a significant amount of data on road conditions, vehicle dynamics, and environmental factors could potentially make it feasible to perform big data analysis by simplifying the process for scientists to develop robust path-following algorithms. By synthesizing these data, autonomous vehicles have the ability to generate appropriate motion profiles and dynamically change velocities. This ensures that tire-road contact remains intact under a variety of circumstances, which leads to a higher standard of safety. * **Environmental sensing through numerous sensing modalities:** In order for autonomous cars to be able to make intelligent decisions, it is crucial for these vehicles to have an extended understanding of their surroundings. As indicated by the research presented in [108], autonomous vehicles are equipped with a variety of sensors, including LiDAR, radar, ultrasonic sensors, and cameras, which continuously collect high-dimensional data on their surrounding area. Big data analytics could potentially be utilized for analysis of these sensor data in real-time, granting the vehicle the ability to generate a high-definition image of its surrounding environment, which is vital for both navigation and the avoidance of obstacles. * **Interconnected vehicle platform for enhanced driver convenience and safety:** In accordance with the hypothesis presented in [109], the incorporation of connected car technologies with big data analytics has the potential to ultimately bring about an innovative improvement in the services and functions that become available to drivers. A connected vehicle platform could provide customized services that are targeted to the driver's comfort and safety by analyzing large datasets that are derived from vehicular sensors, user preferences, and other sources of information. These services are aimed at improving the driver's experience. This includes things like real-time traffic information, adaptive ambient settings, predictive maintenance, and automated emergency response systems. Towards the final analysis, the collection and management of large amounts of data have become an essential component in the development of technology related to AI. In particular, the fields of automated machine learning, clustering, Gibbs sampling, and data structures [110, 111, 112, 113] have emerged in recent days due to their robustness. Particularly in managing big data on AVs, there is a vital part in the pipeline by which autonomous vehicles consume and process a vast amount and variety of data, which is essential for improving safety, security, efficiency, and the overall user experience.