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Revolutionising mobility: seven data trends driving automotive futures

Joerg Zimmer

Connected vehicle technology is increasingly achieving what seems to be ‘the impossible’. New innovations across the automotive world are able to track pedestrians hidden behind buildings, allow parents to monitor when their children are texting while driving, or stop a car from being attacked by cyber criminals. The significance of data in the automotive sector’s future appears to be growing by the minute.

Dependence on data for all aspects of the automotive development process is increasingly clear. Looking to the benefits of future mobility, it’s no overstatement to say that an inability to truly comprehend data and put it to best use in cars, electric vehicles and infrastructure, connectivity and new car-sharing models will hinder the future of this entire industry. With millions of pieces of data produced by each car, what are the most effective ways to harness it for the driving experience of the future?

Capturing customer data

Customer experience (CX) is fast becoming the largest differentiator between brands—in any industry. It’s no different for the automotive sector. Yet, it can’t be done in halves simply because of the historically impersonal relationship between automaker and driver. In a modern world of perfect, algorithmically-generated experiences from Netflix, Uber and the like, automakers have their work cut out to effectively capitalise on the wealth of data gleaned from the newfound connectivity of their vehicles.

It is essential that data is used as a pathway for carmakers wanting to be part of the connected future

The answer lies in capturing the data behind customer interactions and acting upon what is learned. With this information behind the cause and timing of customer problems, the automaker could introduce “moments of delight” by fixing issues in real time or proactively offering solutions—anything from streamlining vehicle servicing to intuitively suggesting parking spots. This continuously improves CX by stopping potential customer frustration in its tracks. The same platforms that use this data can also keep it safe from hacks. This balance between using data flexibly, and keeping it safe, builds up the relationship of long-term trust with the vehicle user.

Automotive app ecosystems

Customers also have the opportunity to seize the potential of their car’s data by customising their drive through automotive apps. For automakers, this means the complexity of an app ecosystem within the vehicle. Building a standardised and stable in-vehicle application platform and attracting a developer community are lengthy and costly processes with high risk. However, with a safe, broad ecosystem in place, a diverse group of developers from automakers, suppliers and third parties can all work their magic to create brilliant apps, whether they have automotive experience or not. To top it off, if the customer has multiple vehicles from different brands, the same apps can be used across them if all vehicle-independent service portability is in place.

Harnessing electrical infrastructures

One of the biggest consumer worries with electric cars as their mileage slowly increases is the fear of being stranded far from a charging point. However, by capturing usability and navigation data, automakers can discover where to put charging points, how much capacity they’ll need, and how to let drivers know about them. By capturing usability and navigation data from thousands of drivers, automakers can understand how to best keep their customers happy through predictive maintenance services. This could manifest itself in offering customers advance warning of when to rotate tyres, change bulbs, top up lubricants, or replace batteries.

Bolt energy app
The myChevrolet mobile app provides access to a nationwide network of public charging stations and real-time station data (Photo by Dan MacMedan for Chevrolet)

Looking at the bigger picture of society’s electrical infrastructure, automakers can also analyse the data of individual usage patterns to help optimally distribute car charging cycles, helping utility companies cope with larger loads on the power grid introduced by sizable numbers of EVs being charged overnight.

Automotive Big Data

Although autonomous, electrification and CX programmes are all aided by data-driven processes and analysis, the systematic creation, collection, and organisation of this underlying data by automakers is a trend in itself. Automotive Big Data is an invaluable tool for automaker analysis. Through the data they provide, connected cars enable analytics to understand where to open new dealerships or service centres, as well as a host of other business-critical decisions. The data could even be sold to governments or business franchises who want to understand vehicle routes or locations.

Yet, storing these huge amounts of data is costly. Automakers need to manage it effectively by ensuring the data from older vehicles can be blended with modern data for easier analysis, and also through intelligence at the edge. To sift the raw data as it’s generated makes much better use of bandwidth, offering very real financial and ecological benefits.

Mobility -as-a-service in and around the city

Buying a car offers freedom of mobility, sure, but it comes with huge costs. Yet, today, it’s possible to purchase mobility without buying a car. Mobility-as-a-service (MaaS) offers pay-per-ride, pay-per-day, short-term contractless rentals, subscriptions and other innovative business models that start-ups are seizing.

Data is at the heart of MaaS

Data will be the key to the MaaS industry booming on the main automotive stage. By determining the patterns that underlie the use of shared vehicle assets (such as usage patterns, sharing hot-spots and information that guides timing- and capacity-based pricing), automakers can maintain the perfect balance between service and ownership-oriented transportation options.

In addition, when it comes to MaaS in the city, automakers can provide detailed information about traffic flows without the costly camera or road surface infrastructure, paving the way for how MaaS will reroute the vehicular lifeblood of a city.

Staying cyber secure

The reliance of modern vehicles upon software is great news not only for drivers seeking modern experiences, but also for cyber criminals. The more software, the more there is to hack. Compromised vehicles could be unthinkably dangerous, so prevention of such attacks is paramount. Automakers must be conscious of four critical factors in their cybersecurity approach: securing vehicle software, securing cloud infrastructure, recognising vulnerabilities, and patching vulnerabilities.

But how does a security team recognise vulnerabilities that have never been seen before (zero-day exploits)? Continuous monitoring of vehicle activity gives systems a chance to spot anomalous behaviours, which could reveal software vulnerabilities that are then quickly patched. Equally, teams could act on real-time intelligence provided by cyber security experts, before speedily disabling the vehicle’s software remotely, to prevent any further reverse engineering by the cyber attackers.

Self-driving vehicles

It’s fair to say that the entire automotive industry is working towards the goal of autonomous vehicles. While SAE Level 5 vehicles are still further away we are on a good track with adding partly autonomous functions that will eventually lead to Level 5. Data is at the heart of this pursuit, put to work to train self-driving algorithms by “showing” the car potential situations and “teaching” it to react in certain ways. But to make the vehicle’s performance consistently reliable, this training process must have access to a huge amount of driving data captured under a wide variety of conditions.

Automakers have their work cut out to effectively capitalise on the wealth of data gleaned from the newfound connectivity of their vehicles

This data includes raw sensor data (which captures the car in its environment), vehicle data (which comes from internal status updates), and metadata. The latter looks at the first two data streams and assigns labels which give them human-level meaning. This complex system is decipherable by automakers through advanced processing that produces analytical insights into how the autonomous system is coping, and where it could make improvements. Machine learning can also add new synthetic sensors, to generate new insights on any number of autonomous events, from understanding speed limit sights to seeing in low visibility.

A step in the right direction for future automotive technology

It is essential that data is used as a pathway for carmakers wanting to be part of the connected future. To make tomorrow’s transportation possible, the huge amounts of data modern cars generate must be understood. Capturing and analysing such data efficiently and with a focus on innovation is an imperative part of this process.

In-vehicle software, security and data technologies will be critical for this pursuit. Automakers should get ahead of the curve and embrace them now, moulding strategies in the light of in-depth automotive knowledge and expertise, as they continue writing the future of mobility.

About the author: Joerg Zimmer is Vice President, EMEA Sales, BlackBerry Technology Solutions, the division of BlackBerry that includes the QNX product portfolio


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