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1. Decentralized control of connected automated vehicle trajectories in mixed traffic at an isolated signalized intersection

   https://doi.org/10.1016/j.trc.2020.102846  

   Yao, Handong ; Li, Xiaopeng ( December 2020 , Transportation Research Part C: Emerging Technologies)

   null (Ed.)
2. Real-Time Traffic Pattern Collection and Analysis Model for Intelligent Traffic Intersection

   https://doi.org/10.1109/EDGE.2018.00028  

   Sreekumar, Unnikrishnan Kizhakkemadam ; Devaraj, Revathy ; Li, Qi ; Liu, Kaikai ( July 2018 , 2018 IEEE International Conference on Edge Computing (EDGE))

   The traffic congestion hits most big cities in the world - threatening long delays and serious reductions in air quality. City and local government officials continue to face challenges in optimizing crowd flow, synchronizing traffic and mitigating threats or dangerous situations. One of the major challenges faced by city planners and traffic engineers is developing a robust traffic controller that eliminates traffic congestion and imbalanced traffic flow at intersections. Ensuring that traffic moves smoothly and minimizing the waiting time in intersections requires automated vehicle detection techniques for controlling the traffic light automatically, which are still challenging problems. In this paper, we propose an intelligent traffic pattern collection and analysis model, named TPCAM, based on traffic cameras to help in smooth vehicular movement on junctions and set to reduce the traffic congestion. Our traffic detection and pattern analysis model aims at detecting and calculating the traffic flux of vehicles and pedestrians at intersections in real-time. Our system can utilize one camera to capture all the traffic flows in one intersection instead of multiple cameras, which will reduce the infrastructure requirement and potential for easy deployment. We propose a new deep learning model based on YOLOv2 and adapt the model for the traffic detection scenarios. To reduce the network burdens and eliminate the deployment of network backbone at the intersections, we propose to process the traffic video data at the network edge without transmitting the big data back to the cloud. To improve the processing frame rate at the edge, we further propose deep object tracking algorithm leveraging adaptive multi-modal models and make it robust to object occlusions and varying lighting conditions. Based on the deep learning based detection and tracking, we can achieve pseudo-30FPS via adaptive key frame selection. 

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3. Mixed-Traffic Intersection Management Utilizing Connected and Autonomous Vehicles as Traffic Regulators

   https://doi.org/10.1145/3566097.3567849  

   Chen, Pin-Chun ; Liu, Xiangguo ; Lin, Chung-Wei ; Huang, Chao ; Zhu, Qi ( January 2023 , ACM)
4. Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic approach

   https://doi.org/10.1016/j.trc.2018.11.010  

   Guo, Yi ; Ma, Jiaqi ; Xiong, Chenfeng ; Li, Xiaopeng ; Zhou, Fang ; Hao, Wei ( January 2019 , Transportation Research Part C: Emerging Technologies)
5. Smart City Traffic Intersection: Impact of Video Quality and Scene Complexity on Precision and Inference

   https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00226  

   Duan, Zhuoxu ; Yang, Zhengye ; Samoilenko, Richard ; Oza, Dwiref Snehal ; Jagadeesan, Ashvin ; Sun, Mingfei ; Ye, Hongzhe ; Xiong, Zihao ; Zussman, Gil ; Kostic, Zoran ( May 2022 , in Proc. 19th IEEE Int. Conf. on Smart City, 2021)

   Traffic intersections are prime locations for deployment of infrastructure sensors and edge computing nodes to realize the vision of a smart city. It is expected that the needs of a smart city, in regards to traffic and pedestrian traffic systems monitored by cameras/video, can be met by using stateof-the-art artificial-intelligence (AI) based object detectors and trackers. A critical component in designing an effective real-time object detection/tracking pipeline is the understanding of how object density, i.e., the number of objects in a scene, and imageresolution and frame rate influence the performance metrics. This study explores the accuracy and speed metrics with the goal of supporting pipelines that meet the precision and latency needs of a real-time environment. We examine the impact of varying image-resolution, frame rate and object-density on the object detection performance metrics. The experiments on the COSMOS testbed dataset show that varying the frame width from 416 pixels to 832 pixels, and cropping the images to a square resolution, result in the increase in average precision for all object classes. Decreasing the frame rate from 15 fps to 5 fps preserves more than 90% of the highest F1 score achieved for all object classes. The results inform the choice of video preprocessing stages, modifications to established AI-based object detection/tracking methods, and suggest optimal hyper-parameter values. Index Terms—Object Detection, Smart City, Video Resolution, Deep Learning Models. 

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