## 1 Introduction  
The development of vehicular edges represents a major technological leap in the automotive industry, driven by advances in sensor technology, deep learning, and computational capabilities. Vehicle edges rely on sophisticated data processing units to interpret complex environmental information and support real-time decision-making. To function effectively, vehicular edges are equipped with multiple sensors, such as cameras, LiDAR, radar, GPS, and IMUs, which collectively generate massive amounts of data each hour [1]. These data are critical for essential tasks like navigation and ensuring vehicle safety. To handle this data volume, vehicular edges use advanced deep learning models that process a wide range of details and parameters to support perception, planning,and decision-making [2]. However, the large data volume and the complexity of these models require considerable storage capacity. Flash-based storage, especially solid-state drives (SSDs), has become the preferred choice for vehicular edges due to its durability, speed, and energy efficiency [3]. However, the performance of these storage systems can be affected by environmental conditions, particularly high temperatures, which demand thorough investigation [4]. High temperatures can significantly impact the reliability of flash devices [5], which is especially concerning for vehicular edges. For instance, elevated temperatures can cause \"bit flips\"--small but critical data errors. The storage system might also slow down, or \"throttle\", to protect itself under these conditions. Such issues are critical in vehicle edges, where rapid and accurate data processing is essential for safe operation. Sluggish I/O could translate directly into slower reaction times for autonomous driving tasks, leading to degraded maneuverability or a higher risk of collisions. For vehicular edges, where reliable data storage is vital for both safety and functionality, it is crucial to understand and mitigate the effects of high temperatures on flash-based storage systems [6]. This paper investigates how temperature variations affect data storage capacity and the efficiency of machine learning tasks in flash-based vehicle storage systems [7]. We use a detailed experimental approach, testing various flash storage devices in a high-temperature, controlled environment. Our experiments are designed to simulate the real operating conditions of vehicular edges, focusing specifically on how flash storage performs in handling sensor data storage and machine learning tasks. This approach allows us to examine and understand how temperature changes impact these critical workloads, providing valuable insights into the performance and reliability of flash storage under thermal stress in vehicle edge applications. Our findings reveal connections between temperature fluctuations and key performance metrics of flash storage, such as throughput and latency, especially in automotive contexts. These results highlight potential weaknesses in current systems and suggest areas for possible optimization. In Section 4, we also discuss how these weaknesses may affect autonomous vehicle characteristics, such as timely obstacle detection or reliable map updates. Understanding these temperature-related effects is crucial for enhancing the resilience and adaptability of automotive storage systems, ensuring they can better withstand diverse environmental conditions. We address lower temperature ranges in Section 5, noting that sub-freezing conditions (e.g., \(-20\)\({}^{\circ}\)C) are also common in automotive settings but left for future work. The paper is organized as follows: Section 2 reviews related research on flash storage in automotive applications and its performance under temperature variations. Section 3 describes the methodology, including the flash storage devices tested and the experimental setup tailored to vehicular edge requirements. Section 4 presents and analyzes the experimental results. In Section 5, we add a discussion part.