Propose a new neural network architecture that leverages the equivariance property of convolutional layers to improve translation invariance, while reducing memory usage and improving real-time performance. Explain how this design choice addresses specific challenges in object detection for autonomous vehicles.

Replace traditional dense matrix multiplication with convolutional layers that share weights across different locations, exploiting the equivariance property to achieve translation invariance. This design choice reduces memory requirements, computational resources, and improves real-time performance while addressing specific challenges in object detection for autonomous vehicles.