With the rapid development of the social economy and industrialization, the
trend in autonomous vehicles (AV) is growing dramatically. This trend will
significantly change the car industry, making sensors indispensable. This chapter
addresses the challenges and opportunities in securely collecting, storing, and
processing AV data. AV technologies must make fast decisions based on diverse data,
including moral dilemmas, and optimize processing for systems like Advanced driverassistance systems (ADAS), Lane Keeping Assist (LKA), and Traffic Jam Assist
(TJA). Data security is crucial to avoid threats associated with AV and Edge Cloud
technology. The proposed solution involves Edge Cloud Assisted Data Management
using secure deep learning algorithms. Data from cameras and sensors in AVs is
processed in the cloud and with the help of an advanced deep learning algorithm, Faster
R-CNN, for accurate object detection and classification. These processed results notify
control systems and actuators for safe and efficient vehicle operation. Additional
sensors monitor vehicle behavior and can intervene in difficult situations. Edge Cloud
technology enhances data processing efficiency and optimization. Deep learning
algorithms accurately identify objects, including in blind spots, providing optimal
solutions in complex situations. Object detection and instance segmentation with Faster
R-CNN offer fast and accurate object location predictions. Data security is ensured
through novel encryption techniques. This chapter explores the data handling units in
AV technologies and emphasizes the importance of adopting edge cloud computing for
the safe and efficient operation of autonomous vehicles. The metrics considered in the
proposed object detection algorithm are intersection over union and mean average
precision. Combining efficient neural network architectures with techniques like
pruning, quantization, and edge computing significantly enhances performance while
maintaining safety and reliability.
Keywords: Autonomous vehicle, Cyber-physical system, Deep reinforcement learning, Edge cloud computing, Faster R-CNN, IoT.
