Shaimaa K. El-Baklish


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El-Baklish

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Shaimaa K.

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0000-0001-6027-3439

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2018 - 201912020 - 202510
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Publications 1 - 10 of 11
  • Consistent vehicle trajectory extraction from aerial recordings using oriented object detection
    Item type: Journal Article
    Riehl, Kevin; El-Baklish, Shaimaa K.; Kouvelas, Anastasios; et al. (2025)
    Scientific Reports
    Vehicle trajectories offer valuable insights for a wide range of road transportation applications. Due to the rise of drone technology, a growing branch of literature explores optical vehicle trajectory extraction from aerial videos, where object detection using neural networks is an important component. Horizontal bounding box object detection struggles to differentiate well between rotated vehicles, especially when dealing with complex backgrounds or densely packed vehicles. This work proposes a generalizable computation pipeline that leverages angular information to extract high-quality trajectories starting from video recordings and ending in trajectories in Cartesian and lane coordinates. A trajectory reconstruction algorithm is designed to be vehicle- and driver-informed and to maximize the physical consistency of the reconstructed trajectories both on the individual vehicles’ and platoon levels. A comprehensive benchmark of 18 object detection models on a real-world video dataset demonstrates how oriented object detection and the use of angular information can be used to significantly improve the consistency of extracted trajectories (15% better internal, and 20% better platoon consistency), and that orientation-informed trajectories can be reconstructed to lane coordinates of higher quality. The reconstructed vehicle trajectories better capture car-following and traffic dynamics, thereby improving their usability for traffic flow studies.
  • A variable time gap feedback policy for string stable adaptive cruise control
    Item type: Conference Paper
    El-Baklish, Shaimaa K.; Kouvelas, Anastasios; Makridis, Michail (2024)
    In vehicle platooning, time gap settings of Adaptive Cruise Control (ACC) systems have a significant impact on car-following dynamics, traffic capacity and road safety. Traffic capacity increases with the reduction of the average time headway; however, this raises concerns of safety and string stability. This work presents a variable time gap feedback control strategy to balance following a minimum time gap setting under equilibrium car-following conditions for increased traffic capacity; and guaranteeing string stability to attenuate disturbances away from the equilibrium flow. This is achieved using nonlinear H ∞ control; where a variable time gap component is set as the manipulated control signal. Also, a constant time gap component is present which dominates during car-following equilibrium and is prescribed to the minimum value. Numerical simulations demonstrate that the proposed scheme yields less perturbations in space headway compared to its constant time-gap ACC baseline; showcasing the potential benefits of better road utilization and increased capacity from a traffic perspective.
  • Driving towards stability and efficiency
    Item type: Working Paper
    El-Baklish, Shaimaa K.; Kouvelas, Anastasios; Makridis, Michail (2024)
    arXiv
    Automated vehicle technologies offer a promising avenue for enhancing traffic efficiency, safety, and energy consumption. Among these, Adaptive Cruise Control (ACC) systems stand out as a prevalent form of automation on today's roads, with their time gap settings holding paramount importance. While decreasing the average time headway tends to enhance traffic capacity, it simultaneously raises concerns regarding safety and string stability. This study introduces a novel variable time gap feedback control policy aimed at striking a balance between maintaining a minimum time gap setting under equilibrium car-following conditions, thereby improving traffic capacity, while ensuring string stability to mitigate disturbances away from the equilibrium flow. Leveraging nonlinear $H_\infty$ control technique, the strategy employs a variable time gap component as the manipulated control signal, complemented by a constant time gap component that predominates during car-following equilibrium. The effectiveness of the proposed scheme is evaluated against its constant time-gap counterpart calibrated using field platoon data from the OpenACC dataset. Through numerical and traffic simulations, our findings illustrate that the proposed algorithm effectively dampens perturbations within vehicle platoons, leading to a more efficient and safer mixed traffic flow.
  • A variable time gap feedback policy for string stable adaptive cruise control
    Item type: Conference Poster
    El-Baklish, Shaimaa K.; Kouvelas, Anastasios; Makridis, Michail (2025)
  • Vehicle trajectory from aerial video using oriented object detection
    Item type: Conference Poster
    Riehl, Kevin; El-Baklish, Shaimaa K.; Kouvelas, Anastasios; et al. (2025)
  • Oriented object detection for aerial vehicle trajectory extraction
    Item type: Conference Paper
    Riehl, Kevin; El-Baklish, Shaimaa K.; Kouvelas, Anastasios; et al. (2025)
    Vehicle trajectories offer valuable insights for a wide range of road transportation applications and research fields. A growing branch of literature explores vehicle trajectory extraction from aerial videos, where object detection using neural networks is an important component. Horizontal bounding box object detection struggles to differentiate well between rotated vehicles, especially when dealing with complex backgrounds or densely packed vehicles. In this work, we demonstrate how oriented object detection and the use of angular, directional information can be used to significantly improve the quality of extracted trajectories. The benchmark of 18 object detection models on a real world video dataset shows, that oriented object detection achieves 0.20m (15%) better internal, and 0.75m (20%) better platoon consistency; REDET and S2A from the openmmlab family count amongst the best performing detection models. Additionally, the analysis of synthetic trajectories with different levels of noise and coverage highlights, that improvements with angular information can be achieved when positional noise is high, coverage is low. At the presence of very noisy angular information however, these improvements diminish.
  • Practical hybrid graph-based formation control architecture for leader-follower trajectory tracking problem
    Item type: Conference Paper
    Elias, Catherine M.; El-Baklish, Shaimaa K.; El-Ghandoor, Nada N.; et al. (2018)
    2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)
    This paper extends the traditional single vehicle trajectory tracking problem to develop a control approach for a group of N-vehicles which are capable of converging to a desired formation while moving on a predefined trajectory. A novel hybrid control algorithm is introduced to handle the two aforementioned problems separately. The proposed controller integrates three different techniques; graph theory, Lyapunov stability and leader-follower approach. The controller structure consists of two main subsystems. In the first subsystem, a leader virtual robot is responsible for the trajectory tracking problem, actuated via a Lyapunov-based controller. Simultaneously, a graph-based controller is used to guarantee the convergence of the follower mobile robots to the desired formation using local positioning information; thus, ensuring the stability of the formation of the follower robots with respect to the moving leader. This new hybrid approach handles the problem of impractical velocities providing control inputs within acceptable ranges that can be practically implemented. Furthermore, it eliminates the oscillations in the motor inputs. Several scenarios are implemented to verify the proposed hybrid control algorithm. The simulations show significant and promising results which prove the effectiveness of this hybrid approach.
  • Koopman mode decomposition for short-term traffic prediction
    Item type: Conference Paper
    El-Baklish, Shaimaa K.; Makridis, Michail; Kouvelas, Anastasios (2025)
    Traffic flow data exhibits temporal and spatial correlations and a dynamic sequential structure; therby making short-term prediction challenging. This study explores the application of the Koopman operator for traffic prediction, which transforms a nonlinear system into a linear one in an infinite-dimensional space. The data-driven nature of Koopman mode decomposition (KMD) enables it to effectively capture these spatio-temporal correlations, making it well- suited for traffic prediction. Specifically, we incorporate known spatio-temporal inter-dependencies in traffic flow to develop a physics-informed modeling pipeline. A comparison between KMD and its linear counterpart, dynamic mode decomposition (DMD), demonstrates that KMD yields more accurate predictions and handles a wider range of traffic scenarios. Future research focuses on improving the robustness of KMD by addressing challenges related to missing and noisy data, further enhancing its applicability in real-world traffic prediction.
  • Great GATsBi
    Item type: Working Paper
    Riehl, Kevin; El-Baklish, Shaimaa K.; Kouvelas, Anastasios; et al. (2025)
    arXiv
    Accurate prediction of road user movement is increasingly required by many applications ranging from advanced driver assistance systems to autonomous driving, and especially crucial for road safety. Even though most traffic accident fatalities account to bicycles, they have received little attention, as previous work focused mainly on pedestrians and motorized vehicles. In this work, we present the Great GATsBi, a domain-knowledge-based, hybrid, multimodal trajectory prediction framework for bicycles. The model incorporates both physics-based modeling (inspired by motorized vehicles) and social-based modeling (inspired by pedestrian movements) to explicitly account for the dual nature of bicycle movement. The social interactions are modeled with a graph attention network, and include decayed historical, but also anticipated, future trajectory data of a bicycles neighborhood, following recent insights from psychological and social studies. The results indicate that the proposed ensemble of physics models -- performing well in the short-term predictions -- and social models -- performing well in the long-term predictions -- exceeds state-of-the-art performance. We also conducted a controlled mass-cycling experiment to demonstrate the framework's performance when forecasting bicycle trajectories and modeling social interactions with road users.
  • The fundamental diagram of autonomous vehicles: Traffic state estimation and evidence from vehicle trajectories
    Item type: Journal Article
    Makridis, Michail; El-Baklish, Shaimaa K.; Kouvelas, Anastasios; et al. (2025)
    Communications in Transportation Research
    The fundamental diagram (FD) is a key tool in traffic flow theory, describing the relationship between traffic flow and density at the link level. Traditionally, FD estimation relies on data from static sensors, although vehicle trajectory data provides an alternative approach. Driver heterogeneity strongly influences the shape and scatter of the FD and is crucial for traffic management. Autonomous vehicles (AVs), exhibiting distinct driving behavior from human drivers, are expected to alter the FD. However, limited observations of AVs in stationary conditions have constrained research in this area. This study addresses this gap by introducing the platoon fundamental diagram (PFD), a simple method to infer empirical FDs from platoon trajectory data. PFD derives pseudo-states from vehicle trajectories and aggregates them to capture consistent relationships between flow, density, and speed—without requiring stationary conditions or backward wave speed estimation. The results highlight the impact of AVs on traffic flow capacity, driver heterogeneity, and oscillation propagation. Comparative analysis with human-driven experiments provides additional insights. Furthermore, the PFD's potential as a practical tool for traffic state estimation in mixed traffic conditions is demonstrated through real-world applications using NGSIM and I–24 Motion datasets.
Publications 1 - 10 of 11