# Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
## 2 Background and Motivation  
As the world increasingly embraces the next generation of sensor-equipped vehicles which has great potential to enhance urban planning and transportation management [20], the supporting infrastructure must evolve accordingly. Modern vehicles, whether fully autonomous or semi-autonomous, demand sophisticated and extensive computational resources to process the data necessary for ensuring passenger safety. To guarantee this safety, computations must be conducted in real-time and be relevant to the vehicle's spatial context. Studies such as [14] explore the future landscape of connected vehicles and clarify the associated challenges, notably the issue of offloading computational tasks away from the vehicle itself. In recent years, the definition of computing has become less distinct, as many edge devices, like proprietary solutions from Nvidia or even mobile phones, can perform similar tasks. Edge computing finds applications across various domains, including instant diagnosis in healthcare [21], predictive maintenance in manufacturing [22], smart farming in agriculture [23], unmanned aerial vehicles (UAVs) [24], and enhancing Android-based device capabilities [25]. Gradually, the concept of edge Artificial Intelligence [26] emerges. Likewise, edge computing is vital for connected autonomous vehicles (CAVs). Research such as [27] highlights the inefficiencies of onboard computation in CAVs and demonstrates how edge computing can alleviate issues like network congestion and limited processing resources. Recent studies, including [18; 28; 29], have investigate and validate the potential of using edge computing for sensor fusion in CAVs. Further works, such as [27; 30], examine real-world applications of offloading computations to the edge, revealing a range of possibilities. The discussion around Deep Neural Networks (DNNs) has emerged as a significant focus area in this context as indicated by [14]. Most AVs rely on DNN inferencing for their self-driving capabilities, utilizing state-of-the-art models for object detection, such as YOLO [4], Faster R-CNN [6], Mask R-CNN [31], SSD [5], DETR [7], Deformable DETR [8], and PointPillars [9]. YOLO [4]pioneers single-shot real-time object detection, balancing speed and accuracy, but struggles with detecting small objects and overlapping objects, as the grid division could lead to imprecise localization in those cases. Faster R-CNN [6] introduces the Region Proposal Network (RPN), improving object detection by generating region proposals. Moreover, Mask R-CNN [31] expands it by adding instance segmentation, allowing both object localization and segmentation. SSD [5] improves single-stage detection by using multi-scale feature maps and default boxes for handling objects at different scales. Both DETR [7] and Deformable DETR [8] try to use and enhance the transformer-based approach to object detection, eliminating region proposals and anchor boxes for a simpler end-to-end pipeline, but both of them are slower than traditional methods like YOLO [4], Faster R-CNN [6], and SSD [5] due to the complexity of the transformer model. PointPillars [9] revolutionizes 3D object detection by applying a CNN to LiDAR point clouds, enabling real-time detection in autonomous driving. While these models perform well, there are trade-offs and optimization opportunities to consider. For instance, to facilitate deployment on lower-end computational hardware, techniques such as quantization, pruning, and architectural modifications are often employed [32; 33; 34]. Deploying machine learning models on edge devices, especially those with limited resources, presents several challenges [35]. One significant hurdle is the constrained computing and memory resources available on these devices, which restrict the complexity and size of deployable models. Additionally, power limitations on edge devices further constrain computational energy use [36].