Anais do Workshop Latinoamericano de Dependabilidade e Segurança em Sistemas Veiculares (SSV)
Visão Computacional para Detecção de Fadiga de Motoristas em Áreas de Pesquisa Mineral
Este trabalho apresenta um sistema inteligente para detecção de fadiga em motoristas com base em técnicas de Visão Computacional e aprendizado de máquina, aplicado em operações de pesquisa mineral da Vale S.A. O sistema utiliza modelos YOLO para detecção facial e técnicas baseadas em pontos faciais (MediaPipe e dlib) para extração de indicadores fisiológicos, como EAR, MAR e inclinação da cabeça. O treinamento foi realizado com imagens reais coletadas em operações de sondagem, permitindo maior adaptação ao ambiente. Os resultados mostram que a combinação de pré-filtragem facial com YOLO e extração de pontos faciais aumenta a precisão da detecção de sinais visuais de fadiga, como olhos semicerrados, bocejos e fechamento ocular.
A Context-aware Failure Detector for VANETs
Vehicular Ad Hoc Networks (VANETs) are complex and dynamic networks that provide communication between vehicles. Due to its dynamism, it is difficult to adequately manage the resources and services of vehicular networks and to execute ITS (Intelligent Transportation Systems) applications reliably. In particular, applications developed to provide safe driving have strong requirements for vehicular communication, and this communication is provided without guarantees. In this paper, we present a context-aware failure detector service. This service uses Context information and Quality of Context metrics to improve the quality of detections. The concept of context has been developed for pervasive systems. It is used in VANETs to represent the status of a vehicle and its environment, including other vehicles and their communication. The failure detector service has been implemented in a simulation environment based on the Veins framework. The results demonstrate the effectiveness of the failure detector in using context and context quality metrics.
Secure Authentication Protocol Implementation for V2X Communication: a Simulation Study-Case
Secure Vehicle-to-Everything (V2X) communication is a key for the development of Intelligent Transportation Systems (ITS). Compromising the sensitive data exchanged by vehicles, pedestrians and the infrastructure can lead to critical safety and privacy risks. Nevertheless, implementing robust security protocols that respect the real-time requirements of such critical applications is challenging due to the significant computational overhead of traditional PKI and the strict latency constraints of safety messages. This work investigates the application of a secure IIoT authentication protocol, proposed in previous work by the group, in the context of V2X communication. The secure protocol is implemented on a V2X simulation environment built over the Artery framework (which integrates OMNeT++ and SUMO) and accounts for vehicles and Roadside Units communication. We evaluated the implementation in a 100-vehicle simulation in terms of latency and jitter, with a mean end-to-end authentication latency of 24.15 ms. The results corroborate the suitability of the secure IIoT protocol implemented here for V2X communication, demonstrating its ability to provide robust security without introducing prohibitive delays.
GAIA-X 4 AGEDA: A Federated and Secure Cloud Framework with AI-Driven Traffic Compliance in Connected Vehicles
Connected and autonomous vehicles increasingly rely on secure cloud infrastructures to exchange perception, safety, and compliance information. Yet, the lack of standardized mechanisms for trustworthy data federation limits interoperability and regulatory validation across heterogeneous vehicle fleets. This paper presents GAIA-X 4 AGEDA, a federated and secure cloud framework that combines dynamic edge orchestration with AI-driven traffic compliance monitoring for connected vehicles. The architecture leverages the GAIA-X Trust Framework for credential-based authentication and sovereign data exchange, while integrating Orchestration for mixed-criticality workload management across a four-zone vehicle architecture. An edge-AI compliance module validates real-time adherence to legal road traffic regulations, with results securely transmitted through GAIA-X Data Spaces to enable cooperative awareness among connected vehicles. Using a CARLA-based proof-of-concept [1] deployed on Raspberry Pi 5 and Jetson Nano P3541 platforms, we demonstrate how compliance violations detected at the edge are authenticated, federated, and shared with Traffic Awareness Services (TAS) while preserving data sovereignty. The framework achieves V2X compliance alerts and this work establishes a practical pathway toward dependable, regulation-aware, and federated vehicle ecosystems aligned with European data sovereignty principles, enabling secure service deployment throughout the vehicle lifecycle.
SENSYNC: Instrumentalização de Plataforma Veicular para Testes de Sincronização Multissensor e Geração de Datasets
Multimodal perception systems used in autonomous vehicles rely on precise temporal synchronization between sensors such as cameras, radars, and LiDAR. Small misalignments of only a few milliseconds can compromise perception and decision-making in dynamic scenarios. This work presents the SENSYNC architecture, an entirely software-based solution built on ROS 2 for acquiring and synchronizing sensors in a Jeep Renegade equipped with three cameras, three FMCW radars, and a VLP-16 LiDAR. The platform is evaluated under real urban traffic conditions, quantifying temporal offsets between sensors and the robustness of multimodal recording. The results show average offsets close to 1 ms for radar/camera pairs and typically below 25 ms for radar/LiDAR pairs, values suitable for multimodal fusion in urban environments. Approximately 12 GB of multimodal data were collected, with an average recording rate of 29 MB/s (1.74 GB/min). These results confirm the feasibility of achieving fully software-based synchronization in real vehicles.
A computer vision application for automatic detection of defects in urban asphalt pavement in Brazil
This work aims to develop an automated system for detecting damage in asphalt pavements by leveraging computer vision and deep learning techniques. It addresses key challenges such as the accurate identification and classification of various types of pavement distress. As a central contribution, we present a publicly available dataset of high-resolution digital images of urban pavements, structured in accordance with Brazilian standards and accompanied by per-image annotations curated by a technical committee. Based on this dataset, the study also presents initial experiments that implement a methodology for the automated diagnosis of road conditions. The model trained with YOLOv5 achieved an mAP@50 of 84.4%. The proposed approach is intended to support public administration in decision-making processes related to road maintenance and intervention planning, ultimately contributing to improvements in the quality, efficiency, and safety of urban road infrastructure.
Neighborhood-Search-Based Path Planning Algorithm Applied to a Python Robotic Simulation Environment
Path planning has become one of the most important fields for mobile robots. It involves analyzing various parameters, including energy consumption, movement smoothness, travel time, and collision avoidance. However, to test these path planning algorithms, researchers are often limited by the need to use 3D simulators. In this context, our work focuses on developing a path planning algorithm with a neighborhood-search-based approach using a lightweight, user-friendly framework software based on Python. This conventional approach, combined with a simulator, allowed us to test our algorithm on different maps, finding successful paths between our initial and goal points, especially on simpler maps with fewer obstacles. The methodology was evaluated in several experiments using different parameters as intermediate points and noise force for disturbances. Our results indicate the feasibility of the proposed technique, which generated valid paths and optimized them towards better solutions.
Waypoint-Based Path Planning for Autonomous Robots with PSO
Autonomous robots are essential for numerous applications requiring high precision and involving significant risks. However, planning and optimizing paths for a single robot in a dynamic environment remains a highly complex task. This challenge necessitates a balance between obstacle avoidance, distance optimization, and efficiency. Traditional strategies often utilize a wide range of path planning algorithms, such as local avoidance, A*, and ACO. Nevertheless, an approach employing waypoints and a “danger zone” in conjunction with a Particle Swarm Optimization (PSO) algorithm can be an effective method for path optimization. Here, we demonstrate that this path planning algorithm provides an efficient and rapid means to generate a path to a goal in an environment with a single obstacle. Through simulations conducted in IR-SIM, a Python-based robotics simulator, we show that a customized PSO, equipped with a multi-objective fitness function, effectively analyzes both intersections with an area around the obstacle (the “danger zone”) and the total path distance. This approach yields paths with over a 97% success rate in guiding the robot to its goal and exhibits relatively low convergence times. Our results illustrate PSO’s effectiveness as a path planning algorithm, highlighting its adaptability to various types of obstacles and positions within a 2D environment. This strategy represents an advancement in the use of heuristic algorithms for autonomous robot path planning, leading to a faster, less computationally demanding algorithm with a high success rate, capable of avoiding diverse obstacle types and easily adaptable to a broad range of single-robot environmental problems.
Secure Data Transmission in ECU Data Acquisition Systems based on MQTT and TLS
Electronic Control Units (ECUs) play a critical role in modern automotive systems, providing data for performance analysis, calibration, and validation. Traditional commercial acquisition tools are widely used but often limited by high costs, dependency on high-performance computers, and restricted integration with modern cloud and edge-processing infrastructures. To overcome these limitations, we developed the Intelligent Acquisition and Analysis System for ECUs (IASE), an embedded platform designed for real-time data acquisition and calibration. This paper presents a secure communication architecture for the second generation of the system (IASE v2), enabling remote connectivity through 4G networks while ensuring confidentiality, integrity, and authenticity of transmitted data. The architecture integrates MQTT over TLS with per-device certificates and mutual authentication (mTLS). We validate the solution through experiments under real-world 4G conditions, demonstrating reliable performance, secure Over-The-Air (OTA) updates, and message delivery consistency. Results show that the proposed approach ensures robust data security without compromising scalability, making it suitable for modern automotive testing environments.
CACCga: Multi-Objective Optimization of CACC Controller Parameters Using NSGA-III
This study investigates the use of Non-dominated Sorting Genetic Algorith III to optimize the hyperparameters of Cooperative Adaptive Cruise Control (CACC) controllers, aiming to enhance performance in dynamic traffic scenarios. The proposed approach, identifies hyperparameter combinations on Pareto fronts based on the reduction of its accumulated error and overshoot, which are related to the system’s response time and stability, respectively. The proposed CACC controller, tuned with NSGA-III, achieves an accumulated error of 17.37 and an overshoot of 5.86%, outperforming other controllers in speed and accuracy when responding to variations in the leader vehicle’s speed. Moreover, it maintains a constant safe distance between vehicles, which is essential for passenger safety and comfort.
Towards a Cutting-Edge Criteria for Aggressive Driving Behavior Recognition
Driver behavior (DB) plays an important role in both road safety and vehicle energy management. Better understanding of how drivers respond to actual driving environments, as well as how their driving style affects road traffic safety, fosters the development of driver behavior monitoring systems that enhance Intelligent Transportation Systems (ITS). However, a literature review shows a lack of integrated and robust systems capable of providing reliable driving assistance in the field that accounts for the uniqueness in driving style and risk profiling assessments. This work aims to showcase a research taxonomy1 for aggressive driving behavior recognition, which relies on a context-aware, personalized, and real-time assessment. These findings will be used to highlight the importance of incorporating contextual and personalized assessments into driver behavior monitoring systems, paving the way for more intelligent and proactive driving assistance solutions.