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1. Deep Learning Architectures for Skateboarder–Pedestrian Surrogate Safety Measures

   https://doi.org/10.3390/futuretransp1020022  

   Shourov, Chowdhury Erfan ; Sarkar, Mahasweta ; Jahangiri, Arash ; Paolini, Christopher ( September 2021 , Future Transportation)

   Skateboarding as a method of transportation has become prevalent, which has increased the occurrence and likelihood of pedestrian–skateboarder collisions and near-collision scenarios in shared-use roadway areas. Collisions between pedestrians and skateboarders can result in significant injury. New approaches are needed to evaluate shared-use areas prone to hazardous pedestrian–skateboarder interactions, and perform real-time, in situ (e.g., on-device) predictions of pedestrian–skateboarder collisions as road conditions vary due to changes in land usage and construction. A mechanism called the Surrogate Safety Measures for skateboarder–pedestrian interaction can be computed to evaluate high-risk conditions on roads and sidewalks using deep learning object detection models. In this paper, we present the first ever skateboarder–pedestrian safety study leveraging deep learning architectures. We view and analyze state of the art deep learning architectures, namely the Faster R-CNN and two variants of the Single Shot Multi-box Detector (SSD) model to select the correct model that best suits two different tasks: automated calculation of Post Encroachment Time (PET) and finding hazardous conflict zones in real-time. We also contribute a new annotated data set that contains skateboarder–pedestrian interactions that has been collected for this study. Both our selected models can detect and classify pedestrians and skateboarders correctly and efficiently. However, duemore »to differences in their architectures and based on the advantages and disadvantages of each model, both models were individually used to perform two different set of tasks. Due to improved accuracy, the Faster R-CNN model was used to automate the calculation of post encroachment time, whereas to determine hazardous regions in real-time, due to its extremely fast inference rate, the Single Shot Multibox MobileNet V1 model was used. An outcome of this work is a model that can be deployed on low-cost, small-footprint mobile and IoT devices at traffic intersections with existing cameras to perform on-device inferencing for in situ Surrogate Safety Measurement (SSM), such as Time-To-Collision (TTC) and Post Encroachment Time (PET). SSM values that exceed a hazard threshold can be published to an Message Queuing Telemetry Transport (MQTT) broker, where messages are received by an intersection traffic signal controller for real-time signal adjustment, thus contributing to state-of-the-art vehicle and pedestrian safety at hazard-prone intersections.« less
2. 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 choicemore »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.« less
3. All-in-One Urban Mobility Mapping Application with Optional Routing Capabilities

   Thompson, R. ; Stovall, J. ; Valasquez, D. ; Anne, V. ; Samoylov, A. V. ; & Sartipi, M. ( December 2018 , Big data)

   To create safer and less congested traffic operating environments researchers at the University of Tennessee at Chattanooga (UTC) and the Georgia Tech Research Institute (GTRI) have fostered a vision of cooperative sensing and cooperative mobility. This vision is realized in a mobile application that combines visual data extracted from cameras on roadway infrastructure with a user’s coordinates via a GPS-enabled device to create a visual representation of the driving or walking environment surrounding the application user. By merging the concepts of computer vision, object detection, and mono-vision image depth calculation, this application is able to gather absolute Global Positioning System (GPS) coordinates from a user’s mobile device and combine them with relative GPS coordinates determined by the infrastructure cameras and determine the position of vehicles and pedestrians without the knowledge of their absolute GPS coordinates. The joined data is then used by an iOS mobile application to display a map showing the location of other entities such as vehicles, pedestrians, and obstacles creating a real-time visual representation of the surrounding area prior to the area appearing in the user’s visual perspective. Furthermore, a feature was implemented to display routing by using the results of a traffic scenario that was analyzedmore »by rerouting algorithms in a simulated environment. By displaying where proximal entities are concentrated and showing recommended optional routes, users have the ability to be more informed and aware when making traffic decisions helping ensure a higher level of overall safety on our roadways. This vision would not be possible without high speed gigabit network infrastructure installed in Chattanooga, Tennessee and UTC’s wireless testbed, which was used to test many functions of this application. This network was required to reduce the latency of the massive amount of data generated by the infrastructure and vehicles that utilize the testbed; having results from this data come back in real-time is a critical component.« less
4. 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 themore »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.« less
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)

   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-drivingmore »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.« less