Week 6: Storyboard & Gathering more information

This week at the weekly meeting in the Lab we had a workshop of Brainstorming and we got 20 minutes for each assignment.

The first was to write an "answer" which is supposedly be our solution to the question

How might we ?... Keep Dotan focused on the road and aware to distractions.

The solutions should not have been viable, we had to go far creatively and not necessarily think about the most feasible things.

In the second assignment, we were asked to choose the most relevant solution for what we have done in the first assignment, and show as a Story Board. Here we specifically tried to show that there is no separation and change of mind state, we deal with things through our day and when we go into the car, we continue our business. From the moment we start driving, we immediately enter a state of automatic driver and continue our normal business we were doing before starting our drive.

We tried to simulate a vehicle in which the driver has a lot of distractions, endogenous (i.e. thoughts, fatigue, flying) and exogenous, external (such as the sound of a message from the phone, update on the internet, etc.). We arranged four chairs to simulate a car in the lab. We showed this by having one of the members of the group serve as a driver and behind him sat other member of the group, who served as "distractions" that constantly popped up. He noted the disturbances, for example: "girl from yesterday," "homework," "the message from the friend about the party," etc. At that time, another member of the group represented our solution and tried to raise awareness when he sat across in front of him on the "dashboard" and showed the driver the risk level throughout the trip, trying to make him aware that he was not concentrating on the trip and was risking himself.

In general, our idea is converging more and more throughout the sessions and is now aimed at a solution that is raising awareness to the dangers we are causing in the car. In fact, creating a very wide ranged machine-learning (i.e., teaching our product lots and lots of data related to distractions), and this is aimed only for inside of the vehicle. In the end, the user receives feedback in which he sees the level of danger he is causing himself. It can also interface with the app and show averages of travel 1, week, month and so on. At first, we thought the product would be physically on the dashboard and later thought that maybe something more innovative in the form of HUD.

This week we met with psychologist Dr. Hadas Erel, who oversees research in the miLAB, and we used her help knowing the right direction for us to search for articles relevant to our idea. She guided us into 3 main directions:

1. Read about choosing the sound for Mobileye.

2. Read about endogenous and exogenous attention in the context of distractions.

3. Read about driving attention.

And so, we did. We found that there are a lot of studies on fatigue in driving, in addition info about understanding body positions in the chair using picture analysis and eye movements( if closed, semi closed, or directed to the road- for the purpose of waking the driver when necessary by sound). We also saw that research was done also about choosing the correct sound to wake the driver and driving attention. Some talk about the classification of levels of attention in driving and how it affects driving itself at different levels and Some talk about the differences between types of distractions (phone / radio / people in the car), We also read that a phone requires the driver to physically move the hand and also think about what is happening inside it and therefore takes a lot of attention and cognitive effort than just listening to the radio in which you are passive... In general, we found that there is a great difficulty in dealing with these problems.

After all this reading, we realised that regarding driving, one must be careful not to distract but still get your attention, as well as not to introduce things that are difficult to get used to and to go to things familiar to the driver so as not to confuse him. For example in our case, using something in the form of a speedometer will be used as a danger gauge, so the driver does not have to get used to anything new, just to adapt to change (such as updating IOS on the iPhone).

In addition, we arranged a meeting with Yair from Mobileye, who was in charge of the company's machine-learning, who also lectured us during the second week of the course. Meeting with him, we wanted to understand two main things. The first is in the field of machine-learning, and the second is the Interface (feedback, icons, sound alerts, etc.).

We all came to his office last Thursday. First, we presented him with our findings to this day and introduced him to our idea. Later on, we talked about the field of machine-learning.

He explained to us about the preliminary research that was done and the gathering of information, as well as about the final product that continues to "study" the user all the time. We shared our thought about collecting a lot of information that actually exists and connect it to one product for it to get the added value we are talking about, raising awareness to dangers in the vehicle. He made us think a lot about the issue of moving to autonomous cars and about features that are down the road that will help in the various stages of fully switching to autonomous vehicles (stages 1 through 5).

After all this information gathering we have prepared our visual STORYBOARD :