# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 4 Edge Computing: Opportunities in ITS Sensing Challenges
### State of the Art  
In this subsection, we summarize state-of-the-art models in edge computing for ITS sensing. The benefit of edge computing lies in the improvement in computation efficiency, network bandwidth usage, response time, cyber security, and privacy [1]. However, the resource constraints on edge devices are the key bottleneck for the implementation of high intelligence. [PERSON] et al. conducted a comprehensive survey on edge AI and considered edge computing is paving the last mile of AI [2]. In terms of AI model optimization at the edge, the compression of deep neural networks using pruning and quantization techniques is significant [222; 223]. In ITS applications, there have not been many pioneering studies that explore the design of both system architectures and algorithms for certain transportation scenarios using edge computing. It is widely known that edge computing with machine learning is a trend for ITS. [PERSON] et al. introduced a new ITS architecture that relies on edge computing and deep learning to enhance computation, latency, and reliability [224]. They investigatedthe potential of using edge deep learning to solve multiple ITS challenges, including data heterogeneity, path planning, autonomous vehicle and platoon control, and cyber security. Crowdsensing with the Internet of Vehicles (IoV) is one category of research using edge computing for ITS. Vehicles are individual nodes in the traffic network with local data collection and processing units. As a whole, they form the IoV network that can perform crowdsensing. One example is monitoring urban street parking spaces with in-vehicle edge video analytics [225]. In this work, smart phones serve as the data producer; the edge unit detects cars, signs, and GPS and uploads the ego-vehicle location and road identifier to the cloud for data aggregation. Another study uses a crowdsensing scheme and edge machine learning for road surface condition classification. A multi-classifier is applied at the edge to recognize road surface type and anomaly situation [226]. [PERSON] et al. proposed SafeRNet, a safe transportation routing computation framework that utilized the Bayesian network to analyze crowdsensing traffic data to infer safe routes and deliver them to users in real time [227]. Some other studies focus on managing and optimizing the resources of the system to ensure efficient message delivery, computation, caching, and so on for IoV [6; 7; 228; 229; 230]. [PERSON] et al. [7] exploited reinforcement learning (RL) to formulate a new architecture that dynamically optimizes edge computing and caching resources. [PERSON] et al. [229] proposed a two-level edge computing architecture for efficient content delivery of large-volume, time-varying, location-dependent, and delay-constrained automated driving services. [PERSON] et al. [6] developed a relay cooperative transmission algorithm of IoV with aggregated interference at the destination node. Another group of research on this topic focuses on developing machine learning methods for certain ITS tasks with edge computing, instead of for resources management in crowdsensing. Real-time video analytics, as the killer app for edge computing, has generated challenges and thereby huge interests for research [5; 231]. Microsoft Research has explored a new architecture with deep learning and edge computing techniques for intersection traffic monitoring and potential conflict detection [5]. [PERSON] et al. [4] designed a new architecture that splits the computation load into cloud part and edge part for smart parking surveillance. On the edge device Raspberry Pi, background subtraction, and an SSD vehicle detector were implemented, and only the bounding boxes related information was sent back to the cloud for object tracking and occupancy judgment. The proposed work improved efficiency, accuracy, and reliability of the sensing system in adverse weather conditions. Detecting parking space occupancy by lightweight CNN models on edge devices has also been investigated by different researchers [135; 143; 232].