The present work approaches intelligent traffic evaluation and congestion detection using sound sensors and machine learning. For this, two important problems are addressed: traffic condition assessment from audio data, and analysis of audio under uncontrolled environments. By modeling the traffic parameters and the sound generation from passing vehicles and using the produced audio as a source of data for learning the traffic audio patterns, we provide a solution that copes with the time, the cost and the constraints inherent to the activity of traffic monitoring. External noise sources were introduced to produce more realistic acoustic scenes and to verify the robustness of the methods presented. Audio-based monitoring becomes a simple and low-cost option, comparing to other methods based on detector loops, or GPS, and as good as camera-based solutions, without some of the common problems of image-based monitoring, such as occlusions and light conditions. The approach is evaluated with data from audio analysis of traffic registered in locations around the city of São Jose dos Campos, Brazil, and audio files from places around the world, downloaded from YouTube. Its validation shows the feasibility of traffic automatic audio monitoring as well as using machine learning algorithms to recognize audio patterns under noisy environments.

Understanding decision‐making in complex and dynamic environments is relevant for designing strategies targeting safety improvements and error rate reductions. However, studies evaluating brain dynamics in realistic situations are scarce in the literature. Given the evidence that specific microstates may be associated with perception and attention, in this work we explored for the first time the application of the microstate model in an ecological, dynamic and complex scenario. More specifically, we evaluated elite helicopter pilots during engine‐failure missions in the vicinity of the so called “dead man's curve,” which establishes the operational limits for a safe landing after the execution of a recovery maneuver (autorotation). Pilots from the Brazilian Air Force flew a AS‐350 helicopter in a certified aerodrome and physiological sensor data were synchronized with the aircraft's flight test instrumentation. We assessed these neural correlates during maneuver execution, by comparing their modulations and source reconstructed activity with baseline epochs before and after flights. We show that the topographies of our microstate templates with 4, 5, and 6 classes resemble the literature, and that a distinct modulation characterizes decision‐making intervals. Moreover, the source reconstruction result points to a differential activity in the medial prefrontal cortex, which is associated to emotional regulation circuits in the brain. Our results suggest that microstates are promising neural correlates to evaluate realistic situations, even in a challenging and intrinsically noisy environment. Furthermore, it strengthens their usage and expands their application for studying cognition under more realistic conditions.

Handling the large amount of information from aircraft trajectories that are produced daily from air traffic control radar systems requires models for representing trajectories in a compact, easy to calculate, representative and distinctive form. These models should permit to perform clustering and classification operations efficiently and effectively. The Fourier descriptors have these characteristics and this article presents the results obtained on actual aircraft trajectories including approach and takeoff operations over a terminal area. Clustering and classification techniques in the feature space of Fourier descriptors were able to correctly separate the various types of operations. Additionally, based on the results of the clusters obtained, a method is presented for the classification of trajectories in progress based on kernel density estimation. An interesting result from the point of view of air traffic control for the detection of anomalous traffic is demonstrated.

The procedures to be performed after sudden engine failure of a single-engine helicopter impose high workload on pilots. The maneuver to regain aircraft control and safe landing is called autorotation. The safety limits to conduct this maneuver are based on the aircraft height versus speed diagram, which is also known as "Dead Man’s Curve”. Flight-test pilots often use subjective methods to assess the difficulty to conduct maneuvers in the vicinity of this curve. We carried out an extensive flight test campaign to verify the feasibility of establishing quantitative physiological parameters to better assess the workload endured by pilots undergoing those piloting conditions. Eleven pilots were fully instrumented with sensors and had their physiological reactions collected during autorotation maneuvers. Our analyses suggested that physiological measurements (heart rate and electrodermal activity) can be successfully recorded and useful to capture the most effort-demanding effects during the maneuvers. Additionally, the helicopter’s flight controls displacements were also recorded, as well as the pilots’ subjective responses evaluated by the Handling Qualities Rate scale. Our results revealed that the degree of cognitive workload was associated with the helicopter’s flight profile concerning the Height-Speed diagram and that the strain intensity showed a correlation with measurable physiological responses. Recording flight controls displacement and quantifying the pilot's subjective responses show themselves as natural effective candidates to evaluate the intensity of cognitive workload in such maneuvers.