Browse Topic: Safety
ABSTRACT Over time, the National Institute of Standards and Technology (NIST) has refined the 4Dimension / Real-time Control System (4D/RCS) architecture for use in Unmanned Ground Vehicles (UGVs). This architecture, when applied to a fully autonomous vehicle designed for missions in urban environments, can greatly assist in the process of saving time and lives by creating a more intelligent vehicle that acts in a safer and more efficient manner. Southwest Research Institute (SwRI®) has undertaken the Southwest Safe Transport Initiative (SSTI) aimed at investigating the development and commercialization of vehicle autonomy as well as vehicle-based telemetry systems to improve active safety systems and autonomy. This paper will discuss the implementation of the 4D/RCS architecture to the SSTI autonomous vehicle, a 2006 Ford Explorer.
SCOPE IS UNAVAILABLE.
Deep learning (DL) models have attained state-of-the-art performance in numerous fields. Nevertheless, for certain real-world applications, existing models encounter diverse challenges, ranging from a lack of generability to new data to issues of scalability and overfitting. In this context, integrating information extracted from different modalities holds promise as a potential solution to alleviate these challenges. This paper introduces MAVEN, a multimodal deep-learning framework for long-range atmospheric visibility estimation. Using multimodal deep learning, MAVEN fuses various modalities to estimate long-range atmospheric visibility. These modalities include RGB imagery, Edge Map, Entropy Map, Depth Map, and Normal Surface Map. Results show that in contrast to single-modality RGB, which achieves only 87.92% accuracy, multimodal deep learning models achieve an accuracy of over 96%. This significant improvement highlights the potential of multimodal approaches to enhance the
This study evaluates the operational impact of multiple concurrent spatialized auditory cues during high-workload rotorcraft missions. A controlled, within-subject flight simulation experiment was conducted in which military-qualified rotorcraft pilots completed continuous multi-objective missions including formation flying, visual asset detection, collision avoidance, and emergency landing tasks. Each mission was flown under spatialized (3D) and non-spatialized (2D) audio rendering conditions while cue composition remained constant. Preliminary results indicate that under complex, formation-dominant workload conditions, pilots consistently prioritized visually anchored tasks and largely deprioritized auditory cue information regardless of spatial rendering. Collision avoidance cues did not produce observable evasive responses, and reported cue trust remained low without prior training. Although limited performance improvements were observed in isolated conditions, participants
This paper details comprehensive analysis modeling and analysis supporting the development of the Research Aircraft for eVTOL Enabling techNologies (RAVEN). An isolated rotor model was developed in CAMRAD II, and predictions of rotor performance and rotor aeroelastic stability were generated. The rotor stability predictions are part of assessing airworthiness of the RAVEN vehicle. The performance predictions were used to calibrate the surrogate model for the NASA Design of Rotorcraft (NDARC).
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