Jonathan Ota

Project Aura

Transforming the relationship between cyclists and drivers.

Our Goal

Bridging the communication gap between drivers and cyclists.

In 2011, a friend of mine, Ethan Frier and I had an idea. As bike commuters in Pittsburgh, Pennsylvania, we recognized a critical gap in cycling safety lighting: communication. This simple insight sparked a six year journey to redefine how cyclists and drivers can better coexist in the modern city.

SECTION 01

Despite modern cycling advocacy movements, cycling in the city still inspires anxiety.

Potholes, Muni tracks, distracted ride share drivers and pedestrians. These anxieties heightened by the darkness of night transform them into very real dangers.

818

Deaths in 2015

Pedestrian and Bicyclist crash statistics

In 2015, 818 people lost their lives in bicycle/motor vehicle crashes

More than two people every day of the year in the U.S. This represents a 6 percent increase in bicyclist fatalities since 2006 and a 12.2 percent increase from the previous year (2014).

~1/3

Of all Injuries caused by cars

2012 National Survey on Bicyclist and Pedestrian Attitudes and Behaviors

Nearly a third of all injuries are caused when bicyclists are struck by cars.

7/10

People

San Francisco Statistics

7/10 people state safety concerns as a major impact on their decision to bike.

55%

People

San Francisco Statistics

55% don’t feel safe while riding their bike near traffic

Emerging Challenges

Self driving cars pose another threat

Bikes pose a complex detection problem because they are relatively small, fast and heterogenous. “A car is basically a big block of stuff. A bicycle has much less mass and also there can be more variation in appearance — there are more shapes and colors and people hang stuff on them.”

Bicycles are probably the most difficult detection problem that autonomous vehicle systems face.

- Steven Shladover, UC Berkeley research engineer

SECTION 02

Safety isn’t a one sided endeavor.

In order to understand how to make cyclists safer, we had to understand drivers.

Driver Research

Understanding the driver

I conducted a few “ride-alongs” with friends, driving a pre-planned route around the city that took them through mixed areas of pre-identified bicycling activity: marked bike lanes, protected bike lanes, shared lanes.

As long as [the cyclist] is in their own space, I don't care about them.

- Frankie Gaw, Car Driver

Key Insight

Cycling safety experts agree.

For cyclists riding in traffic, experts emphasize riding predictably as a key safety behavior. By riding predictably, cyclists communicate their intentions and the information drivers need to safely operate their vehicle near cyclists.

Rider Research

In some of these moments, cyclists used body gestures to indicate their intention.

Drivers can use these gestures, when available, to predict whether or not a critical moment is about to happen and then quickly take the most appropriate action for the situation.

Key Insight

Most of the time, cyclists failed to communicate their intention before they made an action.

Hand signalling was rare as most cyclists were concentrating on the complexities of weaving through dense traffic and avoiding obstacles and moving pedestrians.

SECTION 03

Our opportunity: Enhance cyclists’ intentions to be better understood.

Existing Analogs

An analog already exists: car lighting.

Car drivers rely on a set of lighting paradigms to communicate between each other: turn signals, brake lighting, etc. By utilizing a similar language we enable drivers to better understand a cyclist’s intentions.

Market Opportunity

Currently, there is a gap in the market.

Currently, there are two types of lights on the market: lights to be seen and to see. Both types of lights focus on the challenge of visibility. There are very few lights that address the challenge of communicating intentions. The few that are available are either low quality products from overseas or products that are distracting to use: requiring a user to press a button on the handlebar to activate.

Solution Criteria

The solution needs to perform for both the cyclist and the driver through these four design criteria.

Enhance: the solution must enhance a cyclist’s current ability to communicate. Inform: it must inform drivers of a safer course of action while driving around cyclists. Recognizable: the solution must build off communication paradigms drivers are already familiar with. Frictionless: the experience of using the product should build off the existing behaviors of the cyclist using limited equipment

Next Step

Create a gesture recognition solution to decode and broadcast a cyclist’s intention in real time.

SECTION 04

Turning insight into action

Through the process of prototyping, I sought to better understand the feasibility and desirability of the insight.

Technology

Choosing the right technology.

Understanding rider gestures already created a list of constraints: user wearable, low power, wireless communication. At the time, Intel Curie proved to be the most promising as it was designed for this very purpose: Arduino compatible for easy development, 3.3v low power, Nordic NRF51822 BLE SoC, 6 Axis IMU, Pattern matching engine with hardware accelerated KNN and RBF machine learning algorithms

Prototyping

Fake, cheat and steal (only when prototyping).

Prototyping basically means hacking existing products until they fit your needs. Fortunately for me, the Arduino community provided fantastic documentation and a number of algorithms I repurposed for my needs. There are also bluetooth enabled bike lights on the market I was able to hack by simply sending ASCII char’s over Nordic’s Serial BLE profile.

Iteration 01

Headturns as gestures

For the first prototype, I pursued an early hypothesis that the position of a rider’s head can give 75% of the gesture information I need. My goal was to connect the Curie to the See.sense lights and trigger them based on a head turn threshold.

Iteration 02

Improving recognition accuracy

For the second prototype, I sought to improve accuracy, utilizing the onboard pattern matching engine of the Curie. This meant developing a training protocol for the RBF kernel.

Where the Work is Now

Curie EOL

Gotta find a new module!