The future of connected vehicle data and its impact across industries

It’s predicted that by 2030, 95% of vehicles will be connected to the internet. Most directly, this means an increase in capabilities for vehicles, including a multitude of sensors such as radar, ultrasonic sensors (an instrument that measures the distance to an object using ultrasonic sound waves) and beyond. Some of the most exciting possibilities envision an immersive experience inside the car, including that of extended reality (XR) technology, says David Burns, CTO at Wejo.

But taking it a step further, this effectively creates an enormous increase in user data from said vehicles. This data could encompass basic information such as vehicle speed, when and where drivers are braking, and how many people are in the vehicle – but that’s just the tip of the iceberg. As the technology matures and we move toward more autonomous driving, we can expect vehicles to record and transmit advanced data such as how the vehicle is interacting with other vehicles throughout its journey.

Naturally, automotive and mobility are only two of the industries affected by this influx of data, with the following experiencing a definitive ripple-effect as well:

  • Insurance protocol and claims processing/management
  • Parking and fuel logistics (particularly for electric vehicles)
  • Emergency services routes and practices

Read on for more information on how these sectors will be affected and what both decision-makers in these sectors and consumers need to know.

Insurance protocol and claims processing/management

Connected vehicle data is certain to impact both insurance companies and consumers more generally. As it relates to agencies, said data provides deep knowledge that informs risk and underwriting models. It replaces lesser quality, patchy data from mobile and aftermarket devices with highly accurate and granular data; in this case, real-world knowledge of anonymous driving behavior and events can be used to better inform risk models based on areas known for dangerous driving, unsafe road design or traffic conditions, and journey profiles. It can also provide useful, relevant driver coaching to proactively improve safety, reduce risk and make journeys less stressful for policyholders. And last, it can reduce fraud and its effects by recovering stolen vehicles faster, identifying and minimising insurance fraud, and validating customer supplied details, policy adherence and coverage.

As far as consumers are concerned, it may provide additional opportunities to highlight “good driver” behavior as it relates to their premiums (something some insurance agencies already do if you largely stay within the speed limit and practice safe driving habits).

Parking and fuel logistics (particularly for electric vehicles)

“Networked parking” isn’t a common phrase used across the masses yet, but it will be soon. With connected car data, and smart city parking infrastructure, drivers will be able to receive up-to-the-minute data on parking spots where they are, how long they have been available, pricing and even local discounts readily applicable to drivers. This even has the potential to connect directly to your in-car GPS, saving yourself driving distractions while behind the wheel. Naturally, this type of connected car data could also be attributed to electric vehicle charging stations, their distance and availability, and beyond a subset of data that will become increasingly popular as electric vehicles increase in popularity as well (this will be particularly relevant taking into consideration President Biden’s push for electric vehicles to make up half of auto sales by 2030).

Emergency services routes and practices

Arguably the most important result of connected car data is that related to emergency services routes and practices. Ultimately, connected car data enables services to respond faster, more efficiently and more effectively. It helps improve resolution times and repairs to reduce stress and get drivers on their way as soon as possible. Furthermore, service providers can proactively support drivers by understanding what’s happening in the vehicle remotely, reducing the likelihood of in-journey breakdowns and offering self-serve fixes and repairs.

In addition, connected vehicle data will allow the use of historical traffic and incident data to inform decisions on where to position recovery and emergency service vehicles to enable faster response times and predict where and when roadside incidents are most likely to occur. Data will also provide details on crash severity, vehicle resting position, such as inverted or in a ditch, vehicle type, status and occupancy to alert emergency services and first responders.

As you can see, the above highlights only a microcosm of the full-fledged effects connected vehicle data will have on the world; looking at smart cities alone creates a nearly endless web of where this data can be used and distilled down to create intel and efficiencies across industries. Moving forward, the key will be harnessing this data in a way that doesn’t just create more work for leaders in said industries, but rather creates solutions to problems they have in a quicker, more streamlined, informed manner. This can largely be achieved through edge processing technology that uses machine learning to filter vast amount of data before transmitting only essential information to the cloud, resulting in reduced processing time, storage required and costs; or, better known as, the way of the future.

David Burns

The author is David Burns, CTO at Wejo.

About the author

David Burns is chief technology officer at Wejo where he is charged with driving and executing on the company’s vision and managing technology platforms, partners and external relationships. David brings to Wejo his proven expertise in matching cutting-edge innovation with revenue opportunities. He most recently served as CTO at Key Travel, a global market provider for specialty online travel services, where he was focused on expanding the company’s portfolio and driving growth through acquisitions. He also has held senior management technology roles at Capita, CGI, Vertex Data Science and HP Enterprise Services. David is a graduate of the University of Cumbria in the UK.

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