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1. Smart Traffic Management System using Deep Learning for Smart City Applications

   https://doi.org/10.1109/CCWC.2019.8666539  

   Lingani, Guy M. ; Rawat, Danda B. ; Garuba, Moses ( January 2019 , 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC))

   Already known as densely populated areas with land use including housing, transportation, sanitation, utilities and communication, nowadays, cities tend to grow even bigger. Genuine road-user's types are emerging with further technological developments to come. As cities population size escalates, and roads getting congested, government agencies such as Department of Transportation (DOT) through the National Highway Traffic Safety Administration (NHTSA) are in pressing need to perfect their management systems with new efficient technologies. The challenge is to anticipate on never before seen problems, in their effort to save lives and implement sustainable cost-effective management systems. To make things yet more complicated and a bit daunting, self-driving car will be authorized in a close future in crowded major cities where roads are to be shared among pedestrians, cyclists, cars, and trucks. Roads sizes and traffic signaling will need to be constantly adapted accordingly. Counting and classifying turning vehicles and pedestrians at an intersection is an exhausting task and despite traffic monitoring systems use, human interaction is heavily required for counting. Our approach to resolve traffic intersection turning-vehicles counting is less invasive, requires no road dig up or costly installation. Live or recorded videos from already installed camera all over the cities can be used as well as any camera including cellphones. Our system is based on Neural Network and Deep Learning of object detection along computer vision technology and several methods and algorithms. Our approach will work on still images, recorded-videos, real-time live videos and will detect, classify, track and compute moving object velocity and direction using convolution neural network. Created based upon series of algorithms modeled after the human brain, our system uses NVIDIA Video cards with GPU, CUDA, OPENCV and mathematical vectors systems to perform. 

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2. Intelligent Intersection: Two-stream Convolutional Networks for Real-time Near-accident Detection in Traffic Video

   https://doi.org/10.1145/3373647  

   Huang, Xiaohui ; He, Pan ; Rangarajan, Anand ; Ranka, Sanjay ( February 2020 , ACM Transactions on Spatial Algorithms and Systems)

   Camera-based systems are increasingly used for collecting information on intersections and arterials. Unlike loop controllers that can generally be only used for detection and movement of vehicles, cameras can provide rich information about the traffic behavior. Vision-based frameworks for multiple-object detection, object tracking, and near-miss detection have been developed to derive this information. However, much of this work currently addresses processing videos offline. In this article, we propose an integrated two-stream convolutional networks architecture that performs real-time detection, tracking, and near-accident detection of road users in traffic video data. The two-stream model consists of a spatial stream network for object detection and a temporal stream network to leverage motion features for multiple-object tracking. We detect near-accidents by incorporating appearance features and motion features from these two networks. Further, we demonstrate that our approaches can be executed in real-time and at a frame rate that is higher than the video frame rate on a variety of videos collected from fisheye and overhead cameras. 

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3. 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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4. On-Bicycle Vehicle Tracking at Traffic Intersections Using Inexpensive Low-Density Lidar

   https://doi.org/10.23919/ACC.2019.8814442  

   Xie, Zhenming ; Rajamani, Rajesh ( July 2019 , 2019 American Control Conference)

   This paper explores the challenges in developing an inexpensive on-bicycle sensing system to track vehicles at a traffic intersection. In particular, opposing traffic with vehicles that can travel straight or turn left are considered. The estimated vehicle trajectories can be used for collision prevention between bicycles and left-turning vehicles. A compact solid-state 2-D low-density Lidar is mounted at the front of a bicycle to obtain distance measurements from vehicles. Vehicle tracking can be achieved by clustering based approaches for assigning measurement points to individual vehicles, introducing a correction term for position measurement refinement, and by exploiting data association and interacting multiple model Kalman filtering approaches for multi-target tracking. The tracking performance of the developed system is evaluated by both simulation and experimental results. Two types of scenarios that involve straight driving and left turning vehicles are considered. Experimental results show that the developed system can successfully track cars in these scenarios accurately in spite of the low measurement density of the sensor. 

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5. Online predictive connected nd automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions

   https://doi.org/https://doi.org/10.1016/j.trb.2020.12.009  

   Zhao, S ; Zhang, K. ( March 2021 , Transportation research)

   null (Ed.)

   For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project. 

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