Energy Efficient Autonomous Driving

Energy-efficient autonomous driving aims to develop self-driving systems that minimize energy consumption while maintaining safety and performance. Current research focuses on optimizing model architectures, such as spiking neural networks and lightweight convolutional neural networks, to reduce computational demands and improve efficiency at the edge. This involves integrating techniques like model compression, approximation, and edge-cloud cooperation to process sensor data effectively. The development of such systems is crucial for the widespread adoption of autonomous vehicles, contributing to both environmental sustainability and the advancement of intelligent transportation systems.