arXiv subjects

Alexandre Moreira Nascimento

Publications and source records attributed to Alexandre Moreira Nascimento.

3 recordsLinked to original sources

arXiv2026

Injecting Hallucinations in Autonomous Vehicles: A Component-Agnostic Safety Evaluation Framework

Perception failures in autonomous vehicles (AV) remain a major safety concern because they are the basis for many accidents. To study how these failures affect safety, researchers typically inject artificial faults into hardware or software components and observe the outcomes. However, existing fault injection studies often target a single sensor or machine perception (MP) module, resulting in siloed frameworks that are difficult to generalize or integrate into unified simulation environments. This work addresses that limitation by reframing perception failures as hallucinations, false perceptions that distort an AV situational awareness and may trigger unsafe control actions. Since hallucinations describe only observable effects, this abstraction enables analysis independent of specific sensors or algorithms, focusing instead on how their faults manifest along the MP pipeline. Building on this concept, we propose a configurable, component-agnostic hallucination injection framework that induces six plausible hallucination types in an iterative open-source simulator. More than 18,350 simulations were executed in which hallucinations were injected while AVs crossed an unsignalized transverse street with traffic. The results statistically validate the framework and quantify the impact of each hallucination type on collisions and near misses. Certain hallucinations, such as perceptual latency and drift, significantly increase the risk of collision in the scenario tested, validating the proposed paradigm can stress the AV system safety. The framework offers a scalable, statistically validated, component agnostic, and fully interoperable toolset that simplifies and accelerates AV safety validations, even those with novel MP architectures and components. It can potentially reduce the time-to-market of AV and lay the foundation for future research on fault tolerance, and resilient AV design.

cs.RO↗

arXiv2025

CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development

Autonomous Vehicles (AVs) aim to improve traffic safety and efficiency by reducing human error. However, ensuring AVs reliability and safety is a challenging task when rare, high-risk traffic scenarios are considered. These 'Corner Cases' (CC) scenarios, such as unexpected vehicle maneuvers or sudden pedestrian crossings, must be safely and reliable dealt by AVs during their operations. But they arehard to be efficiently generated. Traditional CC generation relies on costly and risky real-world data acquisition, limiting scalability, and slowing research and development progress. Simulation-based techniques also face challenges, as modeling diverse scenarios and capturing all possible CCs is complex and time-consuming. To address these limitations in CC generation, this research introduces CORTEX-AVD, CORner Case Testing & EXploration for Autonomous Vehicles Development, an open-source framework that integrates the CARLA Simulator and Scenic to automatically generate CC from textual descriptions, increasing the diversity and automation of scenario modeling. Genetic Algorithms (GA) are used to optimize the scenario parameters in six case study scenarios, increasing the occurrence of high-risk events. Unlike previous methods, CORTEX-AVD incorporates a multi-factor fitness function that considers variables such as distance, time, speed, and collision likelihood. Additionally, the study provides a benchmark for comparing GA-based CC generation methods, contributing to a more standardized evaluation of synthetic data generation and scenario assessment. Experimental results demonstrate that the CORTEX-AVD framework significantly increases CC incidence while reducing the proportion of wasted simulations.

cs.RO↗

arXiv2025

Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety

In recent years, there has been significant development of autonomous vehicle (AV) technologies. However, despite the notable achievements of some industry players, a strong and appealing body of evidence that demonstrate AVs are actually safe is lacky, which could foster public distrust in this technology and further compromise the entire development of this industry, as well as related social impacts. To improve the safety of AVs, several techniques are proposed that use synthetic data in virtual simulation. In particular, the highest risk data, known as corner cases (CCs), are the most valuable for developing and testing AV controls, as they can expose and improve the weaknesses of these autonomous systems. In this context, the present paper presents a systematic literature review aiming to comprehensively analyze methodologies for CC identifi cation and generation, also pointing out current gaps and further implications of synthetic data for AV safety and reliability. Based on a selection criteria, 110 studies were picked from an initial sample of 1673 papers. These selected paper were mapped into multiple categories to answer eight inter-linked research questions. It concludes with the recommendation of a more integrated approach focused on safe development among all stakeholders, with active collaboration between industry, academia and regulatory bodies.

cs.RO↗