Tesla is not a typical automaker. In most ways it closely resembles a Silicon Valley technology start-up. Traditional Auto companies, “the metal benders” are adept at making complex reliable products at massive scale, but the software experience in those products is still not as sophisticated as we experience every day in our mobile devices, gaming consoles, smart wearables etc.
Very soon, we may see well known Auto brands – the likes of VW, Toyota, FCA, GM join the list of Silicon Valley technology companies.
But why? Why can’t auto companies focus on building good cars and let their tech partners/suppliers build the software for the cars?
The reason for this paradigm shift in the auto industry is the competition from clean sheet companies who are changing the rule of the game – Tesla, Lucid, Rivian, Nio. They have embraced technology at their core. This has completely changed the relative importance of software vis-à-vis hardware in a vehicle. The customer and market acceptance of this new future paradigm is also reflected in the market valuation of some of these companies.
The Learning Car
We have been used to consider the car as a depreciating asset, where the car starts getting degraded from the day it is driven out of the showroom – it is a downhill road. But not anymore.
Companies like Tesla are improving the performance of their cars over time by intelligently using the data collected and through frequent over the air updates (OTA). One recent example is Tesla increasing the performance of its Autopilot system significantly over a period of 18 months (refer figure). The Autopilot performance almost doubled in the period of 18 months, whereas the performance of active safety features remained almost the same.
This is their true edge; the software core turns every Tesla car into a learning machine. Their global fleet of cars have collected data of 3bn+ miles, have identified multiple edge scenarios and done over 120 OTA updates since 2017, that’s on an average one update every 16 days. These updates have added new features like Smart Summon (valet service), added performance like additional 40 BHP to Model-S, added support for new content like Netflix and fixes and updates to systems like BMS, braking, Autopilot etc. This is a huge game changer.
For other car companies, these would have meant expensive and messy recalls and update or may not be simply possible in the existing vehicles.
This concept of products getting better over time is not at all new – we are so used to it in our smart phones, smart TVs, tablets, PCs – they add functionalities, content, bug fixes through updates. But the automotive industry was alien to this concept till recently – almost all car models used to deteriorate from the time they roll out of the show room. Also, these products were disconnected from our digital life, unlike our other smart devices.
The ship is turning
The ICE industry has grown by outsourcing subsystems, including software and the processing compute hardware. Some of the major OEMs have outsourced nearly 90% of the software which goes into their cars. To make software and data analytics as the core of the industry, it calls for a significant change in the way the existing OEMs function.
This is not lost on the major Auto players. Some of the major Auto giants like Volkswagen and others have made commitments to build software and data analytics to their core. Companies like VW, GM, Toyota, Hyundai, FCA are making large investments towards making this shift. There are multiple inorganic acquisitions which the OEMs are doing in related spaces to quickly ramp-up their capability. But it is a huge leap ahead for traditional Auto companies. There needs to be a huge shift in culture, skills, processes and many more of the legacy thinking.
The vehicle design and manufacturing process has generally been quite linear with a typical lead time of 2-3 years for a new product and there is not much learning built into the vehicle. Software development process is quite different than this.
The mechanism of collecting huge amount of data from the products on the field, to capture the edge cases, to better understand how the product is being used in the field, to map newer requirements and continually improve the product is not new. This process of continuous learning by collecting large sets of data and improving the product is baked into everything from a small mobile game to the largest software platforms on the planet, it is part of mobile phones, large industrial machines, smart TVs etc. For these gadgets and devices, we frequently get updates over the air for newer features, capabilities, contents, bug fixes or for improving performance.
Till recently, this was alien to the Auto industry, but now this is changing, and we are seeing software updates for cars being transmitted over the air (OTA) to do similar things. Earlier, we have seen that the software updates are being applied when the car visits the service centers. But, with the advent of EVs, the need for service visits have drastically reduced, hence OTA is the only viable way. OTA updates solve all the above problems by eliminating the need for software-related recalls and make software updates easy and seamless. OEMs simply send the updates and patches over the internet so that the cars can download and install them on their own.
OTA updates broadly target two major types of systems : infotainment and drive control/transmission.
The infotainment updates include patches for map upgrades, application enhancements, content addition etc. Drive control updates typically include security patches, feature upgrades or additions, updates to ADAS, drive train, chassis control etc. They are also used for activating features on demand – like BMW owners can now activate heated seats and steering wheels during winter months at a subscription payment and disable it when not needed.
The Data Conundrum and the Secret Sauce
All of this calls for appreciating a very fundamental issue. The scale, speed and complexity of data from the vehicles and the need for analysing that data in conjunction with the enterprise data of Bill of Material (BoM), customer master, parts & service masters, service history, data from multiple customer facing channels etc., as well as externally sourced datasets like weather, geo locations, government or municipal data on traffic, road conditions etc. A typical ADAS equipped car would generate roughly 1TB of raw data per day. A vehicle fleet of 100 thousand such cars would generate 100 petabyte of raw data per day! That’s a lot of data to store and analyse.
There are decisions on which data to extract and store, what is the cost of storage, who gets access to what data, what will be the design of the data transfer pipeline, what advanced analytical algorithms can be used, which architecture will perform at such scale, which platform has the ability to handle unstructured and semi-structured data and many more such.
There is a need to architect and build a data pipeline that can manage exabyte scale data. Which can collect and intelligently sift out important data elements and then link those with enterprise data for the analysts to work on using advanced pattern recognition and other machine learning and AI techniques. Managing this data deluge intelligently will be a critical capability for OEMs to succeed in this race.
Partnering with the right data and analytics partner is key to solve these and compete successfully in the software and data driven automotive industry of future.
Sr. Industry Expert – Automotive & Manufacturing for APJ
Udiptya brings more than two decades of diverse experience in the areas of consulting, Analytics, digital transformation, process re-engineering, strategy, business development & sales. He has spent significant time with leading automotive and engineering organisations in various leadership roles in Business Analytics, CRM, technology-led transformation projects and business development.
The views, thoughts, and opinions expressed in the article are done solely by the author in his personal capacity, and do not necessarily represent those of the author’s employer, organization, committee or other group or individual.
Published in Telematics Wire