arXiv subjects

Cristina Olaverri-Monreal

Publications and source records attributed to Cristina Olaverri-Monreal.

At least 19 recordsLinked to original sources

arXiv2026

Multi-Modal Assessment of Road Roughness Using Smartphone Applications, Acceleration, and Passenger Ratings

This paper investigates a multi-modal and human-centric framework for low-cost road roughness assessment. The evaluation was based on three complementary data sources: smartphone-based International Roughness Index (IRI) estimates from two independent smartphone-based applications; in-vehicle GNSS-IMU Receiver (Global Navigation Satellite System Receiver with Inertial Measurement Unit) measurements, and passenger Present Serviceability Ratings (PSR). Data were collected over 1700 km across Austria, Hungary, and Romania under real traffic conditions. Inter-application agreement was evaluated using correlation analysis, Intraclass Correlation Coefficient (ICC), and Bland-Altman methods. While the two smartphone applications show strong correlation, systematic bias limits their interchangeability. A significant inverse relationship between IRI and PSR confirms perceptual sensitivity to roughness, and positive correlations between IRI and vertical acceleration validate the physical linkage between pavement irregularities and vehicle dynamics. The results demonstrate the challenges of integrating consumer-grade sensing and perception-based evaluation for road roughness monitoring as an alternative to high-cost specialized survey equipment.

cs.SE↗

arXiv2026

In-Vehicle Human-Machine Interface to Support Drivers in Conditionally Automated Platooning

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.

cs.HC↗

arXiv2026

On Improving Multimodal Pedestrian Trajectory Prediction with CVAE: A Study on Benchmark and Robot Data

Accurate pedestrian trajectory prediction is crucial for autonomous systems operating in complex environments, such as modular buses and delivery robots in suburban or semi-structured areas. Social Spatio-Temporal Graph Convolutional Neural Networks (Social-STGCNN) have shown strong performance by modeling social interactions; however, producing diverse and well-calibrated future trajectories remains challenging. In this work, we build on a Social-STGCNN backbone and introduce a Conditional Variational Autoencoder (CVAE)-based probabilistic formulation to explicitly model multimodal future trajectories. We evaluate the method on the ETH and UCY pedestrian trajectory datasets as well as on a real-world pedestrian dataset collected by a mobile robot. Results show moderate gains on public benchmarks, but more consistent endpoint accuracy and improved trajectory diversity across different crowd configurations. Evaluation on robot-collected data further demonstrates the approach's effectiveness beyond curated benchmarks and supports its applicability in practical deployments.

cs.RO↗

arXiv2026

Assessing Localization Technologies for Pedestrian Collision Avoidance

Robust pedestrian safety is crucial to the next-generation of intelligent transportation systems. Such systems rely on active pedestrian localization and predictive collision alerts. Pedestrian localization can be supported by Ultra-Wideband technology and Bluetooth 6.0, which offer high-precision ranging and low-latency communication, making them promising candidates for vehicular collision warning systems. This paper assesses the localization accuracy of these technologies for pedestrian alerting and benchmarks their performance against Global Navigation Satellite Systems. Experimental evaluations performed in this paper focused on key performance metrics, including localization accuracy and robustness to environmental conditions. Preliminary results suggest that Ultra-Wideband and Bluetooth 6.0 can serve as viable alternatives or complements to Global Navigation Satellite Systems in certain scenarios, improving situational awareness and enabling timely pedestrian alerts.

cs.RO↗

arXiv2026

Optimising CSRNet with parameter-free attention mechanisms for crowd counting in public transport

Occupancy estimation and crowd counting are critical tasks in designing smart and efficient public transport vehicles. Given that public transport loading can vary from sparse to crowded, classical models for occupancy estimation must be adapted to suit this purpose. Attention mechanisms have shown remarkable capability in enhancing the representational power of deep neural networks for crowd counting in congested scenes with occlusion, complex backgrounds, and perspective distortion. However, conventional approaches, often implemented as parameterized sub-networks within convolutional layers, inevitably increase model size and computational cost, limiting deployment on resource-constrained edge devices. This paper investigates the effectiveness of state-of-the-art parameter-free attention mechanisms for crowd counting and density map estimation in highly congested scenes. We evaluate channel-wise (PFCA), spatial-wise (SA), and 3-D (SimAM) modules and compare their performance with parameterized attention modules constrained to introduce no more than 1% additional parameters. Furthermore, we present a novel combination of attention mechanisms that combines the strengths of PFCA and SA (PFCASA) customized for analyzing video streams onboard public transport systems. Using CSRNet as the backbone, experiments on the ShanghaiTech dataset demonstrate that parameter-free attention mechanisms achieve comparable or superior accuracy without introducing additional model parameters. A detailed performance analysis further reveals that PFCASA outperforms other attention modules in scenes with fewer than 40 individuals, while PFCA shows greater effectiveness as crowd density increases, underscoring their potential applicability for integration into smart public transport modalities.

cs.CV↗

arXiv2026

Reinterpreting Safety Thresholds as Neuron Spiking Thresholds

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.

cs.NE↗

arXiv2026

A LiDAR-Driven Fallback Longitudinal Controller for Safer Following in Sudden Braking Scenarios

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.

eess.SY↗

arXiv2026

A Cost-Effective and Climate-Resilient Air Pressure System for Rain Effect Reduction on Automated Vehicle Cameras

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%.

cs.RO↗

arXiv2025

LiDAR Point Cloud Image-based Generation Using Denoising Diffusion Probabilistic Models

Autonomous vehicles (AVs) are expected to revolutionize transportation by improving efficiency and safety. Their success relies on 3D vision systems that effectively sense the environment and detect traffic agents. Among sensors AVs use to create a comprehensive view of surroundings, LiDAR provides high-resolution depth data enabling accurate object detection, safe navigation, and collision avoidance. However, collecting real-world LiDAR data is time-consuming and often affected by noise and sparsity due to adverse weather or sensor limitations. This work applies a denoising diffusion probabilistic model (DDPM), enhanced with novel noise scheduling and time-step embedding techniques to generate high-quality synthetic data for augmentation, thereby improving performance across a range of computer vision tasks, particularly in AV perception. These modifications impact the denoising process and the model's temporal awareness, allowing it to produce more realistic point clouds based on the projection. The proposed method was extensively evaluated under various configurations using the IAMCV and KITTI-360 datasets, with four performance metrics compared against state-of-the-art (SOTA) methods. The results demonstrate the model's superior performance over most existing baselines and its effectiveness in mitigating the effects of noisy and sparse LiDAR data, producing diverse point clouds with rich spatial relationships and structural detail.

cs.CV↗

arXiv2025

Machine Learning-Based Performance Evaluation of a Solar-Powered Hydrogen Fuel Cell Hybrid in a Radio-Controlled Electric Vehicle

This paper presents an experimental investigation and performance evaluation of a hybrid electric radio-controlled car powered by a Nickel-Metal Hydride battery combined with a renewable Proton Exchange Membrane Fuel Cell system. The study evaluates the performance of the system under various load-carrying scenarios and varying environmental conditions, simulating real-world operating conditions including throttle operation. In order to build a predictive model, gather operational insights, and detect anomalies, data-driven analyses using signal processing and modern machine learning techniques were employed. Specifically, machine learning techniques were used to distinguish throttle levels with high precision based on the operational data. Anomaly and change point detection methods enhanced voltage stability, resulting in fewer critical faults in the hybrid system compared to battery-only operation. Temporal Convolutional Networks were effectively employed to predict voltage behavior, demonstrating potential for use in planning the locations of fueling or charging stations. Moreover, integration with a solar-powered electrolyzer confirmed the system's potential for off-grid, renewable hydrogen use. The results indicate that integrating a Proton Exchange Membrane Fuel Cell with Nickel-Metal Hydride batteries significantly improves electrical performance and reliability for small electric vehicles, and these findings can be a potential baseline for scaling up to larger vehicles.

eess.SP↗

arXiv2025

Tensor-Based Self-Calibration of Cameras via the TrifocalCalib Method

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.

cs.CV↗

arXiv2025

Shadow Erosion and Nighttime Adaptability for Camera-Based Automated Driving Applications

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.

cs.CV↗

arXiv2025

Understanding Visually Impaired Tramway Passengers Interaction with Public Transport Systems

Designing inclusive public transport services is crucial to developing modern, barrier-free smart city infrastructure. This research contributes to the design of inclusive public transport by considering accessibility challenges emerging from socio-technical systems, thus demanding the integration of technological and social solutions. Using Actor-Network Theory (ANT) as a theoretical framework and a mixed-method approach, including shadowing and a focus group, this study examines the socio-technical networks that shape accessibility experiences for visually impaired passengers utilizing the tram in Linz, Austria. Key dimensions that influence public transport accessibility are identified: network configuration, mobility patterns, technology integration, and warning systems. The results show that accessibility emerges from complex interactions between human actors (passengers, staff) and non-human actors (assistive devices, infrastructure) rather than being an inherent property of transport systems. Digital technologies serve multiple functions, from navigational assistance to broader social inclusion, although users comfort with technology varies. Participants emphasized the importance of the two-sense principle for warning signals, with directional audio and tactile feedback particularly valuable.

cs.HC↗

arXiv2025

ClearLines - Camera Calibration from Straight Lines

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.

cs.CV↗

arXiv2025

Evaluating Pedestrian Risks in Shared Spaces Through Autonomous Vehicle Experiments on a Fixed Track

The majority of research on safety in autonomous vehicles has been conducted in structured and controlled environments. However, there is a scarcity of research on safety in unregulated pedestrian areas, especially when interacting with public transport vehicles like trams. This study investigates pedestrian responses to an alert system in this context by replicating this real-world scenario in an environment using an autonomous vehicle. The results show that safety measures from other contexts can be adapted to shared spaces with trams, where fixed tracks heighten risks in unregulated crossings.

cs.RO↗

arXiv2025

V2P Collision Warnings for Distracted Pedestrians: A Comparative Study with Traditional Auditory Alerts

This study assesses a Vehicle-to-Pedestrian (V2P) collision warning system compared to conventional vehicle-issued auditory alerts in a real-world scenario simulating a vehicle on a fixed track, characterized by limited maneuverability and the need for timely pedestrian response. The results from analyzing speed variations show that V2P warnings are particularly effective for pedestrians distracted by phone use (gaming or listening to music), highlighting the limitations of auditory alerts in noisy environments. The findings suggest that V2P technology offers a promising approach to improving pedestrian safety in urban areas

cs.HC↗

arXiv2025

Integrating Naturalistic Insights in Objective Multi-Vehicle Safety Framework

As autonomous vehicle technology advances, the precise assessment of safety in complex traffic scenarios becomes crucial, especially in mixed-vehicle environments where human perception of safety must be taken into account. This paper presents a framework designed for assessing traffic safety in multi-vehicle situations, facilitating the simultaneous utilization of diverse objective safety metrics. Additionally, it allows the integration of subjective perception of safety by adjusting model parameters. The framework was applied to evaluate various model configurations in car-following scenarios on a highway, utilizing naturalistic driving datasets. The evaluation of the model showed an outstanding performance, particularly when integrating multiple objective safety measures. Furthermore, the performance was significantly enhanced when considering all surrounding vehicles.

cs.RO↗

arXiv2025

Automated Vehicle Driver Monitoring Dataset from Real-World Scenarios

From SAE Level 3 of automation onwards, drivers are allowed to engage in activities that are not directly related to driving during their travel. However, in level 3, a misunderstanding of the capabilities of the system might lead drivers to engage in secondary tasks, which could impair their ability to react to challenging traffic situations. Anticipating driver activity allows for early detection of risky behaviors, to prevent accidents. To be able to predict the driver activity, a Deep Learning network needs to be trained on a dataset. However, the use of datasets based on simulation for training and the migration to real-world data for prediction has proven to be suboptimal. Hence, this paper presents a real-world driver activity dataset, openly accessible on IEEE Dataport, which encompasses various activities that occur in autonomous driving scenarios under various illumination and weather conditions. Results from the training process showed that the dataset provides an excellent benchmark for implementing models for driver activity recognition.

cs.RO↗