Mohamed Sabry

Mohamed Sabry, Enrico Del Re, Walter Morales-Alvarez et al.

Adaptive Cruise Control has seen significant advancements, with Collaborative Adaptive Cruise Control leveraging Vehicle-to-Vehicle communication to enhance coordination and stability. However, the reliance on stable communication channels limits its reliability. Research on reducing information dependencies in Adaptive Cruise Control systems has remained limited, despite its critical role in mitigating collision risks during sudden braking scenarios. This study proposes a novel fallback longitudinal controller that relies solely on LiDAR-based distance measurements and the velocity of a follower vehicle. The controller is designed to be time-independent, ensuring operation in the presence of sensor delays or synchronization issues. Simulation results demonstrate that the proposed controller enables vehicle-following from standstill and prevents collisions during emergency braking, even under minimal onboard information.

Enrico Del Re, Mohamed Sabry, Cristina Olaverri-Monreal

Surrogate Safety Measures (SSMs) are extensively utilised in the evaluation of traffic risk in automated driving contexts. However, the majority of SSM-based evaluations employ fixed thresholds that fail to capture the human response to sustained borderline conditions or the reaction to brief, high-risk peaks. The present work proposes a biologically inspired reinterpretation of SSM thresholds. This is modelled as spiking thresholds of leaky integrate-and-fire (LIF) neurons, with multiple SSM inputs combined into a spiking neural network (SNN). The SNN is trained to emit spikes that are aligned with human braking onsets. The training data was recorded in a controlled car-following experiment using the 3D-CoAutoSim platform with CARLA/Unreal and a 6-DOF motion platform, where induced critical events were generated. The results demonstrate that the learned spiking activity qualitatively aligns with braking behaviour across scenarios and captures reactions that are not consistently explained by threshold crossings alone. Analysis across participants further indicates that learned input thresholds remain relatively consistent, while learned decay factors encode different temporal sensitivities for the SSMs. The findings of this study indicate that spiking dynamics may serve as a mechanism to facilitate the convergence of objective SSMs with subjective human safety perception.

Mohamed Sabry, Joseba Gorospe, Cristina Olaverri-Monreal

Recent advances in automated vehicles have focused on improving perception performance under adverse weather conditions; however, research on physical hardware solutions remains limited, despite their importance for perception critical applications such as vehicle platooning. Existing approaches, such as hydrophilic or hydrophobic lenses and sprays, provide only partial mitigation, while industrial protection systems imply high cost and they do not enable scalability for automotive deployment. To address these limitations, this paper presents a cost-effective hardware solution for rainy conditions, designed to be compatible with multiple cameras simultaneously. Beyond its technical contribution, the proposed solution supports sustainability goals in transportation systems. By enabling compatibility with existing camera-based sensing platforms, the system extends the operational reliability of automated vehicles without requiring additional high-cost sensors or hardware replacements. This approach reduces resource consumption, supports modular upgrades, and promotes more cost-efficient deployment of automated vehicle technologies, particularly in challenging weather conditions where system failures would otherwise lead to inefficiencies and increased emissions. The proposed system was able to increase pedestrian detection accuracy of a Deep Learning model from 8.3% to 41.6%.

Anna-Lena Hager, Mohamed Sabry, Walter Morales-Alvarez et al.

Vehicle platooning enables close-gap driving and offers potential benefits for traffic efficiency and safety. In conditionally automated platooning, drivers remain responsible for supervising the system and intervening when necessary, making effective Human-Machine Interfaces (HMIs) critical for maintaining situational awareness and stable driver-automation coordination. This paper investigates whether an in-vehicle HMI providing continuous system-state and inter-vehicle distance information improves supervisory behavior, safety, and platoon stability. We conducted a simulation-based experiment integrated with a 6-degree-of-freedom motion system to enhance scenario realism. Dependent variables included collision occurrence, response latency following platoon disconnection, and the number of manual interventions during intact platooning. Results showed significantly fewer manual interventions when the HMI was active, with intervention rates about 80% higher without it. No significant effects were found for collision occurrence or response latency, indicating that additional information improves supervisory stability during platooning but does not substantially affect emergency reactions or collision rates.

Gregory Schroeder, Mohamed Sabry, Cristina Olaverri-Monreal

Estimating camera intrinsic parameters without prior scene knowledge is a fundamental challenge in computer vision. This capability is particularly important for applications such as autonomous driving and vehicle platooning, where precalibrated setups are impractical and real-time adaptability is necessary. To advance the state-of-the-art, we present a set of equations based on the calibrated trifocal tensor, enabling projective camera self-calibration from minimal image data. Our method, termed TrifocalCalib, significantly improves accuracy and robustness compared to both recent learning-based and classical approaches. Unlike many existing techniques, our approach requires no calibration target, imposes no constraints on camera motion, and simultaneously estimates both focal length and principal point. Evaluations in both procedurally generated synthetic environments and structured dataset-based scenarios demonstrate the effectiveness of our approach. To support reproducibility, we make the code publicly available.

Gregory Schroeder, Mohamed Sabry, Cristina Olaverri-Monreal

The problem of calibration from straight lines is fundamental in geometric computer vision, with well-established theoretical foundations. However, its practical applicability remains limited, particularly in real-world outdoor scenarios. These environments pose significant challenges due to diverse and cluttered scenes, interrupted reprojections of straight 3D lines, and varying lighting conditions, making the task notoriously difficult. Furthermore, the field lacks a dedicated dataset encouraging the development of respective detection algorithms. In this study, we present a small dataset named "ClearLines", and by detailing its creation process, provide practical insights that can serve as a guide for developing and refining straight 3D line detection algorithms.

Mohamed Sabry, Gregory Schroeder, Joshua Varughese et al.

Enhancement of images from RGB cameras is of particular interest due to its wide range of ever-increasing applications such as medical imaging, satellite imaging, automated driving, etc. In autonomous driving, various techniques are used to enhance image quality under challenging lighting conditions. These include artificial augmentation to improve visibility in poor nighttime conditions, illumination-invariant imaging to reduce the impact of lighting variations, and shadow mitigation to ensure consistent image clarity in bright daylight. This paper proposes a pipeline for Shadow Erosion and Nighttime Adaptability in images for automated driving applications while preserving color and texture details. The Shadow Erosion and Nighttime Adaptability pipeline is compared to the widely used CLAHE technique and evaluated based on illumination uniformity and visual perception quality metrics. The results also demonstrate a significant improvement over CLAHE, enhancing a YOLO-based drivable area segmentation algorithm.