This dissertation presents novel approaches to optimizing convolutional neural network (CNN) architectures for connected autonomous vehicle (CAV) workload on edge, tailored to surmount the challenges inherent in cooperative perception under the stringent resource constraints of edge devices (an endpoint on the network, the interface between the data center and the real world). Employing a modular methodology, this research utilizes the insights from granular examination of CAV perception workloads on edge platforms, identifying and analyzing critical bottlenecks. Through memory contention-aware neural architecture search (NAS), coupled with multi-objective optimization (MOO) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this work dynamically optimizes CNN architectures, focusing on reducing memory cost, layer configuration and parameter optimization to reach set hardware constraints whilst maintaining a target precision performance. The results of this exploration are significant, achieving a 63% reduction in memory usage while maintaining a precision rate above 80% for CAV relevant object classes. This dissertation makes novel contributions to the field of edge computing in CAVs, offering a scalable and automated pipeline framework for dynamically obtaining an optimized model for given constraints, thus enabling CAV workloads on edge. In future research, this dissertation also opens multiple different venues for areas of integration. The modular aspect of the pipeline allows for security, privacy, scalability, and energy constraints to be added natively. Through detailed layer by layer analysis and refinement, this dissertation can ensure that CAVs can fully utilize any suitable edge device for the workload requested to realize autonomous driving for everyone.