Caliber: Road Quality Profiling

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1 Caliber: Road Quality Profiling Capstone Design Specification Samuel Quintana John Spencer James Uttaro Damien Hobday CSc : Senior Design Professor: Jie Wei

2 Brief Team Caliber wants to map the quality of roads in New York City using a cell phone s gyroscope and GPS data. Motivation behind this project Figure 1: Example of a poor quality road in NYC that we want to map All of us at Caliber are drivers. We own cars, drive to school most days, and spend a lot of time in our cars. So naturally, we care deeply about improving the driving lives of ourselves and our fellow drivers. New York City is a hard place to drive for many reasons, and one of those is the poor quality of the roads. It s impossible to drive anywhere in this city without driving over bad roads and potholes. What if we could map the quality of roads in NYC? This question got us all thinking. There would be so many uses for that information if it was accurate and readily available. For one, we could give it to city officials so they could quickly prioritize road repairs. Road quality data could be collected and processed continuously, so city officials could be notified immediately if a road falls into disrepair. In addition, this data

3 could be used by mapping services like Google Maps to route drivers around poor quality roads. When a road is being repaved, for instance, it can slow down driving times considerably. Knowing which roads are in poor condition would save time and money. In addition, driving on uneven roads can seriously wear out a car's suspension and tires, so there are financial gains to be had if drivers had road quality data. The potential to positively affect the lives of New Yorkers is exciting to us. The hard part is analyzing the raw gyroscope, accelerometer, GPS data and creating a mathematical model. This is something we will take extreme care in so that we can generate road quality conditions that are incredibly accurate. Once the model is in place and the mobile phone app created, we can continuously collect data and generate road quality reports indefinitely. This is the kind of infrastructure investment that NYC needs, and we are passionate about the idea of contributing to our city. Tools and Frameworks The two main data components of our project we will be focusing on is 6-axis sensor (accelerometer/gyroscope) and GPS. Firstly the accelerometer data will let use learn of instances of imperfections in road by measuring vertical acceleration. Secondly we will use the GPS data to map where these events are on a real road. The great thing about our tools is that currently they come built into modern phones but for the project we are only looking into the iphone 6 s accelerometer and gyroscope. The reason being is that not every phone has the same build or structure and we as a team should focus on one device for testing before making the application universal. The main stack we will be using will contain Apple ios Development SDK, Google Maps API, Amazon Web Services, Python SciKit, Relational Database( PostgreSQL).

4 Core Motion The Apple Development website has provided a detailed documentation of the tools that we will use which is labeled under Core Motion framework of the site. This will allow us to rather raw or processed data based on the actions of the device at the time. The three main classes that we will be focused on from the Core Motion framework are the CMAccelerometerData, CMGyroData, and CMDeviceMotion. This will greatly benefit our study of the road quality and allow the device to provide feedback for the user. Core Motion handles the orientation plane of the device and computation of acceleration in the 3-axes simplifying the collection of data. Figure 2: The iphone accelerometer measures changes in velocity along the x, y, and z axes Core Location In addition to Core Motion from Apple s ios development SDK, we will be using the Core Location Framework. This Framework will allow us determine the current location or direction associated with a device. The Framework provides number of useful classes, protocols and functions which will give accurate GPS data to correlate with the data collected from the 6-axis sensor.

5 Python SciKit Machine Learning Ultimately we would to implement this a standalone system which can accurately determine and handle to the interpretation of the imperfections in a road. Python s Scit kit will allow us to implement a supervised machine learning algorithm. The algorithm we intend to implement will be a classification based algorithm. We can view events(imperfections) as a discrete set and make our classification set based on a binary {1,0 } likewise {imperfection, no imperfection}. Using a clean road a basis and manually label points of imperfections, then give the machine a training set to train and test the accuracy against the labels we know but the machine does not. Google Maps API / PostgreSQL / AWS (Amazon Web Services) To tie all the major components together we will be incorporating the Google Maps API to make overlays of the road quality data, which has been collected through the iphone 6-axis and GPS coordinates. Google provides detailed documentation on overlays, which are objects on the map that are tied to latitude/longitude coordinates. PostgreSQL will be our database server object-relational database, which will be used to store and query our collected data. Lastly we will host our backend on Amazon Web Services which will handle all of the PostegreSQL data storage, and computations done by Python s SciKit.

6 Deliverables At the end of development, we plan to deliver two products: a road profile data set, and a Google Maps overlay of road quality. In today s world where everything is becoming automatized, self-driving cars are on the forefront of this automation of daily tasks a human must do. As it stands the self-driving car industry is doing very well with huge companies such as Tesla, Google and Uber funneling an enormous amount of money into research and development of these technologies. They way that these companies accomplish the difficult task of a self-driving car is by having sensors, and a lot of them. Take Tesla for example. On each car there is: a forward radar, a forward camera, and 12 long-range ultrasonic sensors placed around the car. All of these different sensors give the car a lot of data to work with, but with all this great data one thing is missing, priori data. All of these sensors are collection data in real time. This is very useful for a self-driving car but wouldn t it be nice if we had some prior knowledge on the roads the car will be traveling on? Caliber s first deliverable, the road profile data set aims to solve this problem. With the data that we collect a large data set on the individual roads will be collected. We plan on formatting this data into an easily readable format allowing self-driving cars to use this information as an extra input to their computers when driving. This will do things like allow the car to choose specific routes based on the quality of the road, making the overall experience safer. Our second deliverable will be a Google Maps overlay. Using the collected data, we will create a user friendly overlay that makes drivers aware of the condition of road they will be traveling on. Similar to Google s traffic overlay our overlay will have red, yellow and green sections. Red meaning bad quality, yellow is average quality and green would be high quality roads. An example of how our Google overlay will work can be seen below.

7 Figure 5: Potential Google Maps Overlay This overlay will allow everyday drivers to choose routes based on road quality. Let s say that a new driver is planning on taking a trip, they might want to avoid lesser quality roads at the expense of time because they are simply not comfortable driving yet. In addition, we want to integrate this overlay into Google navigation so that routes based on road quality can be picked when Google maps out your trip for you.

8 Timeline The current setup we have decided to go for in our timeline will be split into four parts over the upcoming semester. The following parts art broken down as such: research, modeling/collection, implementation, and presentation. The research section will mostly be reserved for the month of January as it is the only downtime from the actual semester to collect our thoughts on this project and not have to limit our time limit when it comes to school related work. This will be conducted at everyone s personal time and leading to the first week of February everyone should have some basic knowledge of how to approach the project. We also plan on making sure everyone is on the same page during the research and will use messengers apps such as Slack to communicate on what the other members has looked into for the project topics.

9 The modeling/collection portion will start towards mid-february towards the end of March. This will be the time to put our research to the test and make our app using the iphone s ios. This will be the main part of our project as we will be working on a more hands on approach to start our data collection, processing, and modeling. This will be the time where we start setting up our iphone device and cars to get actual test cases on the road and store our data on a database for further fine tuning. This will consume most of our time and will essentially be the main portion of the project. The implementation part of the project will be reserved for the month of April and will be the time to get feedback from how we should handle the final stages of our app. This is where we will decide as a team to see which of our deliverables can be finalize without consuming too much of our time. Finally, we have our presentation in which we will conduct a demonstration of our project over the course of the semester. This might be slightly challenging as we will be in a classroom but the idea of having a video recording the application in action is an idea that has been tossed around. This is the current summary of our milestone of our project that we plan on working on during the Spring 2017 semester.

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