Search Results (5)

Augmented Reality Head-Up Displays (AR-HUDs) enhance driver perception and safety, yet optimal hazard warning design remains unclear. This study examines three AR-HUD crash warning icon types (BD, BR, BW) across various turning scenarios. Using a 360-degree video-based driving simulation with 36 participants, eye-tracking metrics were collected. Results show BW icons, dynamically linked to hazards, significantly improve drivers’ pedestrian risk awareness and visual attention allocation compared to BD and BR systems. BW consistently demonstrated longer gaze duration, higher fixation counts, and shorter time to first fixation across all turns. BD and BR icons were more susceptible to lane changes, potentially diverting attention from hazards. Findings suggest prioritizing dynamic tracking warning icons over fixed-position alternatives to minimize visual competition and distraction, providing crucial insights for AR-HUD optimization in automated vehicles. Full article

Numerous studies have emerged on the external human–machine interface (eHMI) to facilitate the communication between automated vehicles (AVs) and other road users. However, it remains to be determined which eHMI modality and location are proper for the pedestrian–AV interaction. Therefore, a video-based, eye-tracking study was performed to investigate how pedestrians responded to AVs with eHMIs in different modalities (flashing text, smiley, light band, sweeping pedestrian icon, arrow, and light bar) and locations (grill, windshield, and roof). Moreover, the effects of pedestrian-related factors (e.g., gender, sensation-seeking level, and traffic accident involvement) were also included and evaluated. The dependent variables included pedestrians’ clarity-rating scores towards these eHMI concepts, road-crossing decision time, and gaze-based metrics (e.g., fixation counts, dwell time, and first fixation duration). The results showed that the text, icon, and arrow-based eHMIs resulted in the shortest decision time, highest clarity scores, and centralized visual attention. The light strip-based eHMIs yielded no significant decrease in decision time yet longer fixation time, indicating difficulties in comprehension of their meaning without learning. The eHMI location had no effect on pedestrians’ decision time but a substantial influence on their visual searching strategy, with a roof eHMI contradicting pedestrians’ inherent scanning pattern. These findings provide implications for the standardized design of future eHMIs. Full article

This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection. Full article

Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best. Full article

This paper explores hourly automated pedestrian count data of seven locations in New York City to understand pedestrian walking patterns in cities. Due to practical limitations, such patterns have been studied conceptually; few researchers have explored walking as a continuous, long-term activity. Adopting an automated pedestrian counting method, we documented and observed people walking on city streets and found that unique pedestrian traffic patterns reflect land use, development intensity, and neighborhood characteristics. We observed a threshold of thermal comfort in outdoor activities. People tend to seek shade and avoid solar radiation stronger than 1248 Wh/m² at an average air temperature of 25 °C. Automated collection of detailed pedestrian count data provides a new opportunity for urban designers and transportation planners to understand how people walk and to improve our cities to be less dependent on the automobile. Full article