Bandwidth-Efficient Image Transmission in Autonomous Vehicles Using Super-Resolution Techniques

The rapid advancement in autonomous vehicle technology promises significant improvements in driving efficiency, safety, and comfort. A crucial aspect of these systems is the real-time processing of visual data to ensure accurate object identification and environmental awareness. However, transmitting high-resolution visual data from vehicles to central server encounters challenges due to bandwidth limitations and network latency. Super-resolution methods, leveraging machine learning, present a viable solution by enhancing image resolution without increasing data transmission bandwidth. This study explores the application of super-resolution techniques for bandwidth optimization in image transmission within autonomous vehicles. By transmitting lower resolution images and enhancing them upon reception, the system reduces network load, ensuring efficient data transfer. The analysis demonstrates the trade-off between image quality and transmission efficiency, emphasizing the potential of super-resolution in bandwidth-limited environments. Implementing the SRCNN architecture shows promising results in maintaining image quality with minimal bandwidth usage, contributing to the development of more efficient and reliable autonomous vehicle systems.