Hello everyone this is Wenqiang Jin. I'm from the University of Texas at Arlington.
In this video, I'll present our paper Acousisst: an acoustic assistant tool for people with visual impairments to cross uncontrolled streets.
This is a joint work with Mingyan Xiao, Huadi Zhu,
Suchisnigdha Deb, Chen Kan, and Ming Li from The University of Texas at Arlington.
How to navigate vision impaired people to cross streets is a long lasting topic.
The state of art solution is to install accessible pedestrian signals (APS) at the intersections to assist visually impaired
in determining when it is safe to cross. However there are even more uncontrolled crosswalks
where no traffic signals or APS are present. In practice, the pedestrian leverage hearing
to discriminate between traffic sounds that are too far away to pose hazard to crossing and
those are within close proximity, nevertheless hearing based judgment is not always reliable.
It can be easily impacted by the environmental noises. We aim to develop a portable tool that
alerts the pedestrian with the presence of oncoming vehicles that may cause hazard.
Acoussist consists of speakers that are mounted on front of the vehicle to emit ultrasonic chirps
and an app on the pedestrian smartphone for signal analysis.
Whenever a pedestrian senses a clear street and tends to proceed to the crosswalk,
she turns on the app to double confirm her judgment. The app analyze the received chirps to
detect if an upcoming vehicle is too close to stop before the crosswalk. If yes, an alert is generated.
Thus, the critical challenge here is how to measure the relative distance
between the vehicle and crosswalk as well as calculating vehicle's stopping distance SSD.
We observe $d_0$ is determined by the relative distance and angle between the
vehicle and pedestrian while the stopping distance SSD is determined by the vehicle's motion velocity.
Thus, Acoussist consists of four major system components, i.e., velocity measurement,
angle measurement, distance measurement, and alert generation.
We first present the designs of the velocity measurement module.
Vehicles mounted with the external speakers that emit chirp signals ranging from 17KHz to 19KHz.
A straightforward solution of measuring the velocity is to analyze the doppler frequency shift of the received chirps.
The task is easy if only one vehicle is nearby. In our case, oftentimes several vehicles are present.
Their emitted chirps are overlapping rendering distinguish among
them are extremely challenging tasks. Let alone analyzing the frequency shift for each vehicle.
Thus, how to complete the measurements if there are multiple vehicles?
We perform stft transformation of received signals.
We find that received samples associated with each vehicle generates
a series of pulses in the time frequency domain. Lining up the pulses produces a t-f sweep line that
can identify the corresponding acoustic source due to the unique combination of its offset time
and the moving speed. Formal analysis reveals the slope of t-f sweep line is a function of vehilce's relative velocity.
We explore this relationship to derive multiple vehicles' velocities.
This module estimates the vehicle's directional arrival (DoA) with respect to the pedestrian.
The measurement is acheived by analyzing the time difference of arrival
(TDOA) at the receiver's two microphones, which can be calculated by applying the MGCC function over microphone's received signals
With all measured vehicle's motion status, including angle, speed, and distance.
We then calculating the safe stopping distance SSD for each vehicle to evaluate if it can cause collision hazards.
We develop a prototype to use it to improve the effectiveness of our design.
The alert is generated successful when vehicle is approaching to the pedestrian at a close distance.
As a proof of concept implementation, we developed the prototype of Acoussist with the portable speaker and a smartphone.
All testings are conducted at the campus parking lot as shown in the figure.
The testing results show that the Acoussists can successfully alert the oncoming vehicles
with different speeds while experiences the largest misdetection rate (MDR) of 11.3 percentage
and the false alarm rate (FAR) of 10 percentage, respectively.
We examine the detection performances of Acoussist with the presence of four vehicles. We notice that FAR slightly increases.
However it still maintains good performance with the largest FAR value as 12 percentage.
Acoussist also have been tested in different weathers and usage time. The performances are also relatively stable.
Thank you for your attention. For more details, please refer our paper.