A Context-Aware Artificial Intelligence-based System to Support Street Crossings For Pedestrians with Visual Impairments

Artificial intelligence has the potential to support and improve the quality of life of people with disabilities. Mobility is a potentially dangerous activity for people with impaired ability. This article presents an assistive technology solution to assist visually impaired pedestrians in safely crossing the street. We use a signal trilateration technique and deep learning (DL) for image processing to segment visually impaired pedestrians from the rest of pedestrians. The system receives information about the presence of a potential user through WiFi signals from a mobile application installed on the user’s phone. The software runs on an intelligent semaphore originally designed and installed to improve urban mobility in a smart city context. This solution can communicate with users, interpret the traffic situation, and make the necessary adjustments (with the semaphore’s capabilities) to ensure a safe street crossing. The proposed system has been implemented in Maringá, Brazil, for a one-year period. Trial tests carried out with visually impaired pedestrians confirm its feasibility and practicality in a real-life environment

Outdoor Localization Using BLE RSSI and Accessible Pedestrian Signals for the Visually Impaired at Intersections

One of the major challenges for blind and visually impaired (BVI) people is traveling safely to cross intersections on foot. Many countries are now generating audible signals at crossings for visually impaired people to help with this problem. However, these accessible pedestrian signals can result in confusion for visually impaired people as they do not know which signal must be interpreted for traveling multiple crosses in complex road architecture. To solve this problem, we propose an assistive system called CAS (Crossing Assistance System) which extends the principle of the BLE (Bluetooth Low Energy) RSSI (Received Signal Strength Indicator) signal for outdoor and indoor location tracking and overcomes the intrinsic limitation of outdoor noise to enable us to locate the user effectively. We installed the system on a real-world intersection and collected a set of data for demonstrating the feasibility of outdoor RSSI tracking in a series of two studies. In the first study, our goal was to show the feasibility of using outdoor RSSI on the localization of four zones. We used a k-nearest neighbors (kNN) method and showed it led to 99.8% accuracy. In the second study, we extended our work to a more complex setup with nine zones, evaluated both the kNN and an additional method, a Support Vector Machine (SVM) with various RSSI features for classification. We found that the SVM performed best using the RSSI average, standard deviation, median, interquartile range (IQR) of the RSSI over a 5 s window. The best method can localize people with 97.7% accuracy. We conclude this paper by discussing how our system can impact navigation for BVI users in outdoor and indoor setups and what are the implications of these findings on the design of both wearable and traffic assistive technology for blind pedestrian navigation

A Pilot Study of Pedestrians with Visual Impairments Detecting Traffic Gaps and Surges Containing Hybrid Vehicles

The increasing number of hybrid and quiet internal combustion engine vehicles may impact the travel abilities of pedestrians who are blind. Pedestrians who rely on auditory cues for structuring their travel may face challenges in making crossing decisions in the presence of quiet vehicles. This article describes results of initial studies looking at the crossing decisions of pedestrians who are blind at an uncontrolled crossing (no traffic control) and a light controlled intersection. The presence of hybrid vehicles was a factor in each situation. At the uncontrolled crossing, Toyota hybrids were most difficult to detect but crossing decisions were made more often in small gaps ended by a Honda hybrid. These effects were seen only at speed under 20 mph. At the light controlled intersection, parallel surges of traffic were most difficult to detect when made up only of a Ford Escape hybrid. Results suggest that more controlled studies of vehicle characteristics impacting crossing decisions of pedestrians who are blind are warranted