Author: fakpabio

This week, we made finished the integrating all our hardware pieces together. We have encasings for the raspberry Pi which is holding our GPS, battery, fan, and our distance sensor. We have the encasing for our wristband, which we can feel vibrations through when the sensor detects something in it’s field of view. We also decided to pivot from having a speaker + microphone attached to the raspberry Pi. Instead we are using a wireless headset which is acting as both a microphone and a listening device for the instructions. We were able to integrate this headset with the Raspberry Pi, using a bluetooth connection. After we got the encasings for all the devices we attached the connection to the gopro mount, and it is now able to be put on our bicycle for wholistic testing.

In terms of testing, we did more testing with our speech to text and our text to speech because we decided to use another speech model that was better for our bluetooth headset. This model also seemed to have more accuracy than the previous API we were using. We also tested our distance sensor to make sure it was good for detecting the motion of cars and when the user is in motion with the sensor.

RISKS:

There is potential strength of the connection piece for the mount not being strong enough to hold our device, so we need to see how it is riding quickly different terrain. There is also the risks of bluetooth connection not being the best with devices moving and every device being in encased, which could lead to choppy connection at times, but we will see with testing.

NEXT WEEK DELIVERABLES: 

Continuous testing for the device still needs to be done, testing with actual cars and different journeys. We also need to get varying user feedback on the practicality of the device. We will be working on final presentations and getting working demo videos out.

In the beginning of the week we spent time discussing the ethics assignment. Ethics is an important factor in our project, and we wanted to make sure we were all on the same page. The majority of our time this week was spent on developing the three separate areas of our project. We started off in the beginning of the week working on our object detection. We had to configure the RPi to use the RT and TX pins for the sensor.  A script was created to detect the distance between an object and the sensor. We tested this script in different scenarios, with the sensor moving as well as the object moving back and forward. However, we’re planning on testing the field of view more to see if the accuracy of the sensor is enough for our project. Towards the latter part of the week we spent time on the navigation part of the project. We spent time testing the capabilities of different models for Google Speech-to-Text AI for extracting the destination for a journey from the user’s voice commands.  We ended up choosing the Chirp 2 model, due to it’s accurate speech recognition. We also worked on the GPS tracking, as it was something we wanted to have working by this week ( see below, sample output and code ). We were able to do some testing outside and were able to receive longitude and latitude measurements to be used for our navigation system ( see below, map image capture). 

In terms of risks that we’re looking at after this week, we’re considering a couple of factors. Since we’re unable to change our distance sensors range, we believe that theres a possibility for our sensors to be detecting other objects that are outside of the range that we’re worried about. This would slow down our processing time, so we’re currently testing to ensure that this isn’t the case. Another risk we’re worried about is the accuracy of our GPS information displaying our longitude and latitude. As we were testing this week, we were seeing longitude and latitude information being sent, however depending on the mode ( triangulation or GPS location), the location being displayed wasn’t as accurate as we wanted it to be. We’re doing continuous testing to see if this is only an issue in certain areas, and are considering using a different GPS system or better antennas.

Code + Sample output for Speech to Text system

Longitude and Latitude information being sent to notehub and displayed on notehub Map.

NEXT WEEK DELIVERABLES:

Mini-integration of all of our parts to see how they work together. We’re getting to the latter end of our project, so we’re attempting a mini integration to see how they fit together, incase we need to change things up. We are also considering ordering new parts, so doing research on different types of distance sensors as-well as new GPS modules.

Our current progress on the Rid3 device is going well. The main tasks for this week were to complete the setup for raspberry pi, finish setup for the blues starter kit, and get basic object detection with sensors. We were able to accomplish most of these goals since our last progress report. We have the raspberry Pi set up and have made it compatible with the blues starter kit by using ssh for programming on the Pi. In addition, we worked on configuration for converting speech to text using the Google Speech API and were able to see sample outputs. We also ordered materials for a new mount for our bicycle which we designing a component that is compatible with the mount ( see below ).

One of the risks that we are currently facing is the fact that the GPS information is not being sent properly through to the Raspberry Pi, this could be a problem as we need accurate GPS data for the proper directions to be sent.

NEXT WEEK DELIVERABLES:

Continue testing for speech-to-text translation and beginning implementation of R-Tree algorithm. Set up circuit for wristband + establishing basic object detection for sensors. Fixing GPS issues and storing GPS data to be used by the RPi.

.

component compatible with mount.

sample output for Google speech API

ADDITIONAL QUESTIONS:

Part A: … with consideration of global factors. Global factors are world-wide contexts and factors, rather than only local ones. They do not necessarily represent geographic concerns. Global factors do not need to concern every single person in the entire world. Rather, these factors affect people outside of Pittsburgh, or those who are not in an academic environment, or those who are not technologically savvy, etc.

There is a global need for increased safety and accessibility in urban mobility. Our device Rid3 can help meet this increased need for bicyclists’ safety by enhancing phone-less navigation in increasingly crowded urban settings. Bike lanes and road support for micro-mobility are being expanded in many major cities worldwide, however riders are often still at risk due to poor visibility, car blind spots, and distractions from checking navigation devices. This technology lowers the risk of accidents and increases traffic safety by enabling cyclists to receive crucial blind spot alerts and clear directions without taking their eyes off the road by fusing voice navigation with a haptic feedback wristband. This solution is particularly impactful in regions where cycling infrastructure is still developing or where road conditions are less predictable. Additionally, our device is intended to work in various climates expanding beyond what typically occurs in Pittsburgh, like high dust environments. The usability of our device is also simplistic and meant to be intuitive to promote use for all people regardless of technological expertise. By enhancing safety in diverse environments, the product contributes to broader global efforts to promote sustainable transportation, reduce urban congestion, and improve public health. A was written by Emmanuel.

Part B: … with consideration of cultural factors. Cultural factors encompass the set of beliefs, moral values, traditions, language, and laws (or rules of behavior) held in common by a nation, a community, or other defined group of people.

Within the context of our project, the main cultural factor for consideration is the fact that different countries have different road laws hence it is important that the Rid3 devices adhere to these cultural norms. Specifically, it is important that our device functions in a way that it is intuitive to a biker on the road. Consequently, it is essential that the audio feedback for navigation instructions adheres to road safety rules. Consequently, research will have to be done to ensure that the system’s feedback adheres to the rules of the region. B was written by Akintayo 

Part C : … with consideration of environmental factors. Environmental factors are concerned with the environment as it relates to living organisms and natural resources.

Environmental factors play an important role in our project. We want to make sure that we’re using resources that do not pollute the environment and are safe for the environment. It is battery powered and does not release any toxins into the air when it is running, so the environmental concerns are limited. One thing to take note of is the potential for coming across different animals in the environment. If we detect animals in our blind spot, we want to notify the user so that they don’t hit the animal coming across. C was written by Forever.

This week we started receiving materials for our project and began testing their functionalities. We received our sensors, blues starter kit, microphone, batteries, and our raspberry pi hat. Our current risk is  stable mounting of our device to bikes. Our current implementation relied on a velcro strap which we realized after using bikes this week will be unstable.. Most bicycles use a mounting clamp to add accessories so we’ll do the same. We’re redesigning our encasing to be compatible with the NiteRider Tail Light Strap Mount. 

Since last week we decided to eliminate the web server aspect and will have all of the server processing happen on the raspberry pi. Two main reasons for this: sending audio to blues cloud is very inefficient due to the round trip latency from sending back and forward through the cloud to an internet-hosted server.  Thus not using the Blues cloud will reduce latency and it’ll be cheaper since we won’t be charged for each block of data being sent to the Raspberry Pi.

Sample Design of Encasing for Navigation/Audio System

 

Sample Design for Wristband  

New framework for navigation portion

NEXT WEEK DELIVERABLES:

Main tasks for upcoming week: complete setup for raspberry pi, finish setup for blues starter kit, get basic object detection with sensors.