SDV Telemetry Project - On Hold
With Kraig Angel, Vice President of Automotive at MAVI.io
What does your company do? What services, or products, do you provide?
Mavi.io’s OnMyWay™ Commerce brings easy, curated shopping to the dashboards of customers’ vehicles. OnMyWay payment-enabled Connected Cars meet the needs of “immediate purchase” customers integrating location, ordering, product recommendation, payment, and pickup. Mavi.io’s ‘OnMyWay’ Shopping To Be Enabled By New Smart Car Dashboards (Forbes.com) MAVI’s Mobile Retail Network™ HUB middleware marketplace connects retailers’ existing eCommerce platforms for inventory, order, timing, loyalty, and curbside orchestration to the car’s dashboard interface. Eliminating the need for retailers to integrate with each car’s unique environment. Our revenue is transaction and promotion driven, with SaaS fees as well as a B2B Fleet option that drives early revenue while consumer adoption in passenger cars grows.
Why did you join COVESA, and how long has your company been a member?
The intensity for which COVESA members are driven to improve the on-the-road and in-vehicle experience of our customers is the reason Mavi.io joined COVESA. COVESA is the premier collaboration network in the automotive industry and the only place to reach the most important thought leaders in the connected car world. Speed of innovation-to-market is essential to today’s automotive industry. COVESA is the catalyst. Mavi.io has enjoyed the immediate and vast benefits of COVESA membership since June 2022.
What benefits does your participation in COVESA bring to your company and business?
Mavi.io has been fortunate to be a key highlight of two COVESA events: a live in-vehicle demonstration at the event at CES in January 2023, and a live in-vehicle demonstration at the afterparty event of AutoTech in Novi in June 2023. COVESA teammates and leadership have directly engaged to foster connections with key decision makers at Automotive OEMs, Tier 1s, and potential investors. As a result, POCs have been launched and investments made… and this is only the first 12 months of membership with COVESA.
Which COVESA collaborative project(s) or Birds of a Feather (BoF) is your company engaged in, and why is that beneficial to your business?
Mavi.io is very proud to be leading the creation of the new In-vehicle Wallet Charter Project within COVESA. Furthermore, with the intent to improve the quality of life for millions of drivers around the world by providing everyday convenience from an in-vehicle experience, Mavi.io is also tracking the following collaborative projects:
● Data Expert Group – Interface Definition
● Electric Vehicle Charging Expert Group
● Android Automotive SIG
● Vehicle Experience and Content
As an integrated App within the vehicle with the capability to connect users with retailers and make purchases from the vehicle - including EV charging locations – Mavi.io’s OnMyWay Commerce will require two-way connected vehicle communication. As a result, nearly all COVESA collaborative projects have deep appeal and alignment with our organizational goals. This is why the COVESA membership is of such great value to Mavi.io.
The entire Mavi.io team wishes to show immense gratitude and appreciation to the COVESA member organizations and their engaged leadership as well as the leaders within COVESA itself. Without all of you, our remarkable success and growth would not be possible.
By Peter Winzell, Lead Software Engineer, Volvo Cars
During the Fall All Member Meeting in Dearborn last year, I presented some ideas around vehicle signal set matching. This is the problem we face when we need to have proprietary vehicle signals translated into Vehicle Signal Specification. This matching/mapping is a challenge the industry faces today and can take a significant amount of time to do manually. By using vehicle signal attributes such as name, description, unit, and datatype we proposed an algorithm that would be used to estimate just how close two signals from two disjunctive sets are. The algorithm can be summarized just by a simple formula like this:
p is a number between 0..1, where the closer you get to 1, the more likely you will have signals that match.
To improve on this (original paper: Ideas for Vehicle Signal Set Matchingt) I have some new ideas presented in a paper: Dynamic Weight Generation for Vehicle Signal Set Matching. Here we are replacing static weights that are used to value attributes with some functions that increase the weight depending on the result of a more highly ranked attribute. I decided to value the description attribute higher and use that value for the weight generation. We want the weight to reflect when we believe we have a match, but also at the same time reflect when we believe we do not have a match. This will make sure that units such as m/s are dynamically valued and should prove a better result when we rank matching candidates – at least that is the theory.
The paper explores 3 different mathematical one-variable functions with different complexity. I added a test to see how the different functions handle the comparison where we have signals that match and those that don’t although they share the same unit.
I welcome discussion and would like engage with others in solving this problem and turn it into a COVESA project. Please reach out, via COVESA Community Director if interested.