The ongoing development of Autonomous Driving (AD) systems has resulted in an increased demand for perception models that combine high accuracy with computational and energy efficiency. Recent advancements include BEVFusion, a state-of-the-art multi-sensor fusion Neural Network (NN) framework that combines camera and LiDAR into a unified representation called Bird's-Eye View (BEV). This approach enables robust spatial reasoning and 3D object detection. In this scenario, BEVFusion reaches competitive performance on large-scale benchmarks such as NuScenes, a dataset for multi-modal AD NNs, providing training data from camera and LiDAR sensors with 3D object annotations. However, BEVFusion's computational and memory requirements make real-time deployment on embedded or resource-constrained devices extremely difficult despite its high accuracy.