Hey guys and gals!

So the 'why' of the AiPi (that satisfyingly rhymes) is we're actually shooting for a final product which we hope will save bicyclists lives, and help to make bike commuting possible again for many.

Doing all the computer vision, machine learning, etc. work I've done over the past year, it struck me (no pun intended) that there had to be a way to leverage technology to help solve one of the nastiest technology problems of our generation:

A small accident as a result of texting while driving, a 'fender bender', often ends up killing or severely injuring/degrading the quality of life of a bicyclist on the other end.

This happened to too many of my friends and colleagues last year.  And even my business partner on this has been hit once, and his girlfriend twice.

These 'minor' accidents (were they to be between two cars) resulted in shattered hips, broken femurs, shattered disks - and then protracted legal battles with the insurance companies to pay for any medical bills.

So how do we do something about it?

Leverage computer vision + machine learning to be able to automatically detect when a driver is on a collision course with you - and stuff that functionality into a rear-facing bike light.  So the final product is a dual-camera, rear-facing bike-light, which uses object detection and depth mapping to track the cars behind you - and know their x, y, and z position relative to you (and the x, y, and z of their edges, which is important).

What does it do with this information?  

It's normally blinking/patterning like a normal bike light, and if it detects danger, it does one of two actions, and sometimes both:

1.  Starts flashing an ultra-bright strobe.

2.  Honks a car horn.

So case '1' occurs when a car is on your trajectory, but has plenty of time/distance to respond.  And example is rounding a corner, where their arc intersects with you, but their still at distance.  The system will start flashing, to make sure that they are aware of you.

And if the ultra-bright strobes don't work, then the system will sound the car horn with enough time/distance left (based on relative speed) for the driver to respond.  The car horn is key here, as it's one of not-that-many 'highly sensory compatible inputs' for a driving situation.  What does that mean?  It nets the fastest possible average response/reaction time.  

So with all the background out, we're happy to say that last weekend, we actually proved out that this is possible, using slow/inefficient hardware (with a poor data path):

What does this show?

1.  You can differentiate between a near-miss and a glancing impact.

2.  The direct impact fired a little late, mainly just as a result of the 3FPS that this prototype is running at (as a result of a poor data path).

What does it not show?

1.  Me getting run over... heh.  That's why we ran at the car instead of the car running at us.  And believe it or not, this is harder to track.  So it was great it worked well.

And the following shows what the system sees.  

Keep in mind that using the Myriad X hardware more completely (only the neural engine, which is 1 TOPS, is being used, the other 3 TOPS are unused in the NCS form-factor), the frame rate bumps to ~30FPS.  The Raspberry Pi is having to do video processing/resizing/etc. here and  - all of which that the Myriad X has dedicated hardware for, but can't be leveraged in the NCS formfactor.

So the AiPi data path (cameras directly to the Myriad X) allows all this hardware to be used, while completely removing the burden of the Pi having to massage/pass-around/etc. video feeds.  So it's a huge benefit in terms of speed.

Here's me initially testing it, figuring out what thresholds should be, etc.  

I wanted to do it on a super-strange vehicle, to prove how robust at the computer vision is at detecting objects.  So this is the 'strange' vehicle I used, and it worked great!

To learn more about CommuteGuardian, feel free to check out commuteguardian.com, and also be a first-poster on the forum if you have ideas/questions.  And bear with us, it's just going up now.

Best,

Brandon