Degraded Visual Environments Research Articles

Helicopter Pilot Performance and Workload in a Following Task in a Degraded Visual Environment.

BACKGROUND: In this study, we investigated the impact of a loss of horizon due to atmospheric conditions on flight performance and workload of helicopter pilots during a low-altitude, dynamic flight task in windy conditions at sea. We also examined the potential benefits of a helmet-mounted display (HMD) for this specific task.METHODS: In a fixed-based helicopter simulator, 16 military helicopter pilots were asked to follow a maneuvering go-fast vessel in a good visual environment (GVE) and in a degraded visual environment (DVE). DVE was simulated by fog, obscuring the horizon and reducing contrast. Both visual conditions were performed once with and once without an HMD, which was simulated by projecting head-slaved symbology in the outside visuals. Objective measures included flight performance, control inputs, gaze direction, and relative positioning. Subjective measures included self-ratings on performance, situation awareness, and workload.RESULTS: The results showed that in DVE the pilots perceived higher workload and were flying closer to the go-fast vessel than in GVE. Consequently, they responded with larger control inputs to maneuvers of the vessel. The availability of an HMD hardly improved flight performance but did allow the pilots to focus their attention more outside, significantly improving their situation awareness and reducing workload. These benefits were found in DVE as well as GVE conditions.DISCUSSION: DVE negatively affects workload and flight performance of helicopter pilots in a dynamic, low-altitude following task. An HMD can help improve situation awareness and lower the workload during such a task, irrespective of the visual conditions.Ledegang WD, van der Burg E, Valk PJL, Houben MMJ, Groen EL. Helicopter pilot performance and workload in a following task in a degraded visual environment. Aerosp Med Hum Perform. 2024; 95(1):16-24.

Bayesian inference of visibility in fog and smoke artificial conditions from 3D-LiDAR point clouds

3D-LiDARs are heavily impacted by a degraded visual environment (DVE) like rain, fog and smoke which limits their use for perception algorithms. The capacity to retrieve information about the environmental conditions from an embedded sensor can be an asset to improve autonomous driving performances. False positive artifacts in the point clouds caused by aerosols and hydrometeors particles tend to cause perception issues and thus need filtered out. However, those artifacts can also be used as valuable information to infer weather properties and maybe improve filters. This article proposes a Bayesian inference model which can classify discrete values of visibility using 3D-LiDAR point clouds. Gamma and Log-normal distributions are used to model the distance distributions of the noise points and the model is extended using the Random Finite Set (RFS) formalism with the Poisson and Binomial RFS models. Experiments in artificial fog and smoke conditions are presented and the classification model is trained and tested independently for each experiment. The used point clouds are extracted from specific parts of the field-of-view that can be used to generalize the proposed method to any outdoor scenario. The model shows good classification results with increased performances when the RFS extension is used.

The Impacts of Advanced Avionics on Degraded Visual Environments

Degraded visual environments (DVE) such as brownout, sand storms, and night conditions are responsible for accidents and hence losses in the aviation industry. There is a need to develop avionic systems to support pilots when they encounter environmental conditions that compromise their visual capacity. This report discusses the effects of DVEs on aviation, expounds on the advanced avionic systems available and further elaborates how these systems are implemented in countering DVE conditions. The report recommends on how advanced avionics systems have been and are being implemented in aircrafts to support decisions made by pilots flying under degraded visual environments.

A Multi-Purpose Realistic Haze Benchmark With Quantifiable Haze Levels and Ground Truth.

Imagery collected from outdoor visual environments is often degraded due to the presence of dense smoke or haze. A key challenge for research in scene understanding in these degraded visual environments (DVE) is the lack of representative benchmark datasets. These datasets are required to evaluate state-of-the-art object recognition and other computer vision algorithms in degraded settings. In this paper, we address some of these limitations by introducing the first realistic haze image benchmark, from both aerial and ground view, with paired haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene, and consists of images captured from the perspective of both an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). We also evaluate a set of representative state-of-the-art dehazing approaches as well as object detectors on the dataset. The full dataset presented in this paper, including the ground truth object classification bounding boxes and haze density measurements, is provided for the community to evaluate their algorithms at: https://a2i2-archangel.vision. A subset of this dataset has been used for the "Object Detection in Haze" Track of CVPR UG2 2022 challenge at https://cvpr2022.ug2challenge.org/track1.html.

Target discrimination in the extended shortwave infrared band (2 to 2.5μm) compared with visible, near-infrared, and SWIR in degraded visual environments

Long-range target identification is well studied in the visible (Vis) and near-infrared (NIR) bands and more recently in the shortwave infrared (SWIR). The longer wavelength of SWIR (1 to 1.7 μm) improves target detection for both long ranges and under challenging atmospheric conditions because it is less limited by scattering and absorption in the atmosphere. For these reasons, SWIR sensors are proliferating on military platforms. The extended shortwave infrared (eSWIR) band spanning from 2 to 2.5 μm is not typically limited by diffraction, and as a result, the band benefits target acquisition both at long ranges and for degraded visual environments (DVEs). Theoretical and experimental data compare eSWIR with Vis, NIR, and SWIR for atmospheric transmission, reflectivity, illumination, and sensor resolution and sensitivity. The experimental setup includes two testbeds, each with four cameras. The first is a wide field of view (FOV) testbed matching FOV at 20 deg for each camera. The second is a narrow FOV telescope testbed to match instantaneous FOV for consistent resolution across all four bands at long ranges. Both the theory and experiment demonstrate advantages of using eSWIR for long-range target identification under DVEs.

Driving in the Dust: Challenges and Lessons Learned from Field Testing in Degraded Visual Environments

Degraded visual environments (DVEs) pose significant safety and efficiency problems in military ground vehicle operations. As part of a larger research program, two field tests were conducted to evaluate driving aids while indirect driving in DVEs. The current paper presents the results of one of these field tests, and focuses on the challenges and lessons learned in designing a challenging test course and producing consistent dust clouds for assessing Soldier driving performance and workload in degraded visual environments.

Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments.

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

Spatial Audio Cueing Aids Pilot Navigation during Simulated Helicopter Flight in Degraded Visual Environments

Aviation mishaps resulting from degraded visual environments (DVEs) represent a significant loss in military personnel and aircraft every year. DVEs can be caused by any type of environmental condition (e.g., sand, snow, fog) that obstructs the pilot’s vision, and can cause spatial disorientation. The current study was a tri-service effort exploring the implementation of spatial audio cueing to aid pilot navigation in DVEs. Directional cueing (i.e., indicating the location of target waypoints) was achieved by spatializing an auditory stimulus. Pilots performed multiple flight maneuvers in a full-motion UH-60 Black Hawk flight simulator at the U.S. Army Aeromedical Research Laboratory. Performance was assessed by measures of error, completion time, and failure rate. Findings from this study provide information on sensory cueing display design for helicopter flight in DVEs.

First results of LiDAR-aided helicopter approaches during NATO DVE-mitigation trials

Landing a helicopter on unprepared sites can quickly become a challenging task in operational scenarios. Especially when environmental factors reduce the available visual cues for the pilot, the risk of disorientation increases. Motivated by avoiding accidents occurring in these Degraded Visual Environments (DVE), the NATO program for DVE-Mitigation (DVE-M) supports international efforts in the development of systems for enhanced situational awareness during DVE. A workgroup of the German Aerospace Center (DLR) and HENSOLDT Sensors GmbH participated at flight trials of the European NATO DVE-M campaign in Manching with DLRs highly modified research helicopter. The state of a system currently being under development was presented which combines eyes-out tunnel-in-the-sky symbology (SferiAssist®) with dynamic path updates based on a laser sensor. Details of four flights during the campaign in Manching performed in February 2017 are given and discussed in terms of a technical analysis and results of pilot evaluations.

Modeling macular pigment optical density effects on photopic and scotopic vision in degraded visual environments

The macular pigment (MP) is an accumulation of the carotenoids lutein, zeaxanthin, and mesozeaxanthin in the central retina. These are derived from dietary sources. MP absorbs light in the 400- to 520-nm range. Consequently, the MP is a spectral filter over the photoreceptors, reducing the effects of internally scattered light and attenuating the short wavelength component of natural sunlight. The average MP optical density (OD) is about 0.2 to 0.6 log units depending on the sample population, whereas the range of MPOD is reportedly 0 to 1.5 log units. Some people can increase their MPOD by increasing their consumption of lutein, zeaxanthin, and mesozeaxanthin, and this may be important for vision in degraded visual environments (DVE). Specifically, nutritional interventions and dietary supplements have produced statistically significant enhancements under laboratory conditions in visual tasks, such as visibility through haze, low contrast target detection, contrast sensitivity, glare resistance and recovery, photostress recovery, dark adaptation, mesopic sensitivity, and enhanced reaction times. The question is whether these enhancements are operationally meaningful or not. The present paper begins to address the question by modeling MPOD effects on the visibility to low contrast photopic and scotopic targets seen under a range of DVE over realistic distances that incorporate atmospheric filtering. Specific model parameters include luminance, target contrast, spectral content, and distance. The model can be extended to estimate the efficacy of MPOD effects on target detection, discrimination, and standoff distances.

Exploiting spatial audio cues for pilot navigation in degraded visual environments

Aviation mishaps resulting from degraded visual environments (DVEs) represent a significant loss in military personnel and aircraft every year. DVEs are considered any type of environmental condition (e.g., sand, snow, or fog) that visually obstructs the pilot and can cause spatial disorientation. The current study represents a tri-service effort exploring the implementation of spatial audio cueing techniques to aid pilot navigation in DVEs. Directional cueing (i.e., indicating the location of target waypoints) was achieved by spatializing an auditory stimulus using the SoundLab audio rendering package and convolving audio signals with (non-individualized) head-related transfer functions. Two spatial cue conditions were tested, either rendered dynamically in reference to the pilot’s head via head tracking or with respect to aircraft heading. Data were collected from pilots operating a full-motion UH-60 Black Hawk flight simulator at the U.S. Army Aeromedical Research Laboratory. Pilots performed multiple flight maneuvers, such as a “turn to target” localization task, a "side-step" maneuver positioning the aircraft over a stationary target, and an “approach to moving target” task. Performance was assessed by measures of localization error, completion time, and failure rate. Findings from this study provide information on sensory cueing display countermeasures for helicopter flight in DVEs.

Ground Vehicle Driving Aids: Assessing Driver Workload and Performance in Degraded Visual Environments

To enable indirect-driving maneuverability and threat detection in degraded visual environments (DVE), TARDEC’s ground DVE program is developing and testing a range of sensors and driver aid display systems. The current paper presents the first in a series of three simulator studies. It examined driver performance with two of the candidate driving aids, the Lane/Road Departure Warning System (LRDWS) and Optic Flow Enhancer (OFE), in three levels of degraded visual environment. Results indicated that the LRDWS best supported drivers in degraded visual environments, eliciting the lowest workload ratings and highest system usability ratings and facilitating faster driving in severe DVE conditions, with no apparent speed-accuracy tradeoff.

Impact of turbulence and degraded visual environment on pilot workload

Offshore helicopter operations are frequently conducted in both turbulent and degraded visual environments (DVE). This investigation assesses the combined influence of turbulence and DVEs on pilot workload to identify first limits for safe operations. Flight tests using a simulation model of the research helicopter ACT/FHS (active control technology/flying helicopter simulator) flight mechanics model were conducted in the air vehicle simulator (AVES) at DLR Braunschweig. Tests were completed using four pilots, and results show the effects on pilot workload, task performance and control input activity. It was found that DVE and turbulence increase the workload and reduce task performance, but each in a different manner. Furthermore, the impact on control activity and pilot-induced oscillation tendencies are shown to have dependency upon the environmental conditions.

Integration of a visibility graph based path planning method in the ACT/FHS rotorcraft

This work presents recent progress at the German Aerospace Center (DLR) in landing guidance for helicopter approaches to landing sites without prior reconnaissance. During the project ALLFlight, DLRs research rotorcraft Advanced Control Technology/Flying Helicopter Simulator (ACT/FHS) has been equipped with a sensor suite including a LIDAR sensor, a forward looking RADAR, an infrared camera and a TV camera to provide sensor-based situational awareness. Using the information acquired by the LIDAR, a method for planning an initial flight path from previously unknown landing entry points to the landing point is presented. It is based on a previously developed method using a vehicle point representation, geo-referenced flight surfaces and visibility graphs. A second mode to provide re-planning capabilities in-flight is described. First results of flight tests conducted in 2013 are presented and discussed. Using the data recorded during these approaches, an improved re-planning strategy for the final approach based on so called visibility hulls is presented. This work continues recent research at DLR’s Institute of Flight Systems helicopter department for flight under degraded visual environment (DVE) for a full-scale helicopter.

FBW 헬리콥터 모델 역변환 비행제어법칙 설계 및 검증

The Fly-By-Wire(FBW) flight control system is essential to improve the stability and flying quality of the helicopter. Advanced aerospace companies, such as Bell-Sikorsky (USA) and NHI (European Consortium), have already applied the FBW flight control system to manufacture V-22 and NH-90 helicopters, respectively. This paper addresses the development of control law design using model inversion method improve the hover and low speed handling qualities of helicopter based on BO-105 model in `Day` and `Degraded visual environments(DVEs)` in accordance with ADS-33E-PRF. Design parameters are optimized to satisfy the handling qualities specification using Control Designer`s Unified Interface (CONDUIT) commercial control law software. The result of the analysis based on CONDUIT and non-real time simulation in-house software, HETLAS (HElicopter Trim Linearization And Simulation) reveals that the provides an efficient mean to achieve Level 1 handling qualities.