Automotive

NAIAS: Using maps and IoT to find accessible destinations For Olli

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I had the opportunity to explore amazing map technology with an incredible Watson IoT technology partner called Mapbox at the North America International Auto Show this week in Detroit. Last year I had the change to ride Olli – a self-driving mini shuttle built by Local Motors in a 3-D printing auto micro factory. Olli features a cognitive rider experience with Watson, developed by the IBM Watson IoT AutoLAB team.

Last September in Berlin, I asked Olli where to go to enjoy a sushi dinner on my birthday. I was given several suggestions, and the restaurants were displayed on a map with route previews and Yelp reviews. I discovered this was Mapbox technology powering the visualization on screen, so that my dinner guests and I could choose a restaurant in discussion with Olli.

Maps that move you

Mapbox is the technology behind maps that move you. To get familiar with Mapbox, I spoke to Jeremy Stratman at the AutoMobili-D planet M exhibit hall in the Cobo Hall. Jeremy leads business development and strategy for automotive and fleet. Mapbox provides tools and services to enable developers and designers to integrate maps into web, mobile, embedded. Tools and services mean the data, ability and tooling to customize maps in every possible way, both from a look-and-feel and data-driven perspective.

The real value comes in when you have your own data and you want to give it spacial context. An example of this that social media super users like me would be familiar with is Foursquare. They used branded maps for the look of the map, and added in points of interest (POIs) that are customized by users with comments, photos, and their location-based gamification system and categories. Now, The Weather Channel uses Mapbox instead of Google so that they can insert a weather layer onto the map in a way unique to their custom data.

Mapbox can do blazing fast client-side buffering and analysis, to quickly surface points of interest based on conditions and data from a third party service with modular integration using an SDK. According to Jeremy, there are lots of interesting discussions around traffic and weather and other external forces, that are in essence IoT data from connected buildings and the city itself.

Adding real time vehicle data to maps

Speaking theoretically, it would be exciting to consider how to use data featuring patterns of movement, and even predicting patterns of movement, using historical and real-time data from vehicles. Watson IoT could process and learn from this data to teach Olli how to improve routes to recommended destinations along with data from external sensors based on weather. Imagine how this could help navigate an unknown city or visiting somewhere you haven’t been before. Olli could help those with limited mobility, impaired vision or a physical disability to find their way around unfamiliar territory safely.

mapbox displays data visually on an iPadMapbox data displayed visually on a tablet device

The visual data becomes the map

There is data about accessible entrances for people with limited mobility or disabilities that could be used by Mapbox in a way similar to what Jeremy showed us at NAIAS. There was a striking demo featuring a way to visualize building ages in Portland, where you could scan the matches for an age by swiping your finger across the landscape. Another demo showed weighted stacked columns to show the restaurants based on the worst reviewed restaurants in New York City.

The visual data becomes the map, instead of individual data points being laid over top of the map. In the autonomous space, many verbal interactions with the vehicles will inform decisions made behind the scenes that then tie back into a visualization to give spacial context using Mapbox. Within the space of IoT, how do you incorporate data from sensors like weather with wind patterns and have that inform the human-vehicle interaction to improve the rider experience by choosing more accessible destinations?

mapbox displays data maps visually on an iPadMapbox uses data to create interpretive visualisations

This is not just a question for the autonomous and self-driving auto environment. It is a massive opportunity to improve mobility for the elderly or those with disabilities, who need new ways to explore the world.

Get involved

At CES last week, IBM Accessibility Research and the Consumer Technology Association (CTA) announced that they are partnering with Local Motors to make Olli the most accessible vehicle in the world. The goal is to help aging people and those with disabilities gain and maintain mobility and independence. There is going to be a series of hackathons and a crowd-sourcing campaign to tackle the Accessbile Olli challenge in 2017. You can get involved by talking to the IBM Accessiblity Research team (@lahart), Watson IoT AutoLAB team on Twitter (@joespeeds) or Local Motors () using the hashtag #accessibleOlli and join the discussion on the Local Motors Olli project website.  

What do you think Olli could do with Watson voice interactions, IoT data sets and Mapbox visuals to help accessibility? Co-create with us!

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