• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Car Insurance Of The Future: Determining Culpability Between Self-Driving Vehicles

June 30, 2018   BI News and Info

Despite a tremendous amount of progress on the autonomous vehicle front in the past year, the self-driving car industry can’t seem to get out of its own way.

2018 started with multiple accidents from Tesla, whose vehicles are in the purgatorial “stage 3” of self-reliance. Stage 3 vehicles technically require human interaction, but they are autonomous enough to lull drivers into complacency.

On March 18th, an Uber test vehicle struck and killed a pedestrian despite the fact that a “safety driver” was present in the car. On May 4, a Chrysler Pacifica powered by Waymo was involved in an accident in Arizona.

Much has been written about how to improve self-driving vehicles and whether or not they are safer than human drivers, but two issues remain largely below the surface:

  1. Why can’t we use blockchain technology to determine insurance rates for safe drivers?
  1. How do we determine culpability when a self-driving car is involved in an accident?

The answers lie with blockchain technology.

Determining culpability between software

Until recently, the at-fault party in an accident was usually one driver or another. Only in rare (and widely publicized) situations was a vehicle malfunction to blame. Insurance agencies would work with the drivers they insure and local authorities to determine who was at fault, and once that was determined they would pay accordingly.

However, a recent beta test between RiskBlock and Nationwide showed that using blockchain could make traffic stops faster, insurance payments distributed sooner, and fraud rendered nearly obsolete.

As a guide by Charlotte car accident law firm Dewey, Ramsay & Hunt said, insurance is a time-sensitive issue; blockchain technology could streamline the process and help both drivers and insurance agencies resolve issues faster and without fraud.

But with self-driving cars, human drivers will eventually not be at fault in any way; culpability will fall to the software programs tasked with driving the vehicle.

As in the latest autonomous vehicle accident, pundits are quick to blame Uber at large. This is naive. With so many sensors and vehicle components at work and an equal number of vendors responsible for those components, any one of them could be responsible for an accident.

The only way to accurately see who or what is at fault is to track each sensor and software component in a blockchain framework. While it wouldn’t be ethical to require Uber or different vendors to share their code with one another, a government’s transportation administration should have access to this log of activity so that when an accident occurs they (along with an insurance company) could see which system went wrong in the moments leading up to the crash.

Insuring safe drivers with blockchain technology

When it comes to recovering funds after an accident, safe drivers often get the short end of the stick, so to speak.

Even when a driver has a safe driving history, their insurance may not provide sufficient accident coverage. “While they often will cover medical bills that are necessary for your recovery, they might have policy limits that prevent recovery for all of these injuries and other financial burdens,” claims a study by Baltimore car accident attorney Bennett & Heyman.

The DAV Foundation has proposed a blockchain framework that would track the micro-movements of drivers over time (how fast they drive, for instance) and, depending on how safe they are or how low their mileage is, they would receive significantly discounted insurance.

One could argue that the only individuals opposed to such an insurance system would be high-risk drivers; while it could lead to privacy concerns, it may be the only way to efficiently and accurately determine insurance payments and payouts.

For more on how digitalization is changing the insurance industry, see Resilient No More: Insurers Face A New Reality Of Digital Disruption.

Let’s block ads! (Why?)

Digitalist Magazine

between, Culpability, Determining, future, Insurance, selfdriving, Vehicles
  • Recent Posts

    • We were upgraded to the Unified Interface for Dynamics 365. Now What?
    • Recreating Art – the unexpected way
    • Upcoming Webinar: Using Dynamics 365 to Empower your Marketing and Sales Teams with Digital Automation
    • Center for Applied Data Ethics suggests treating AI like a bureaucracy
    • Improving Dynamics 365 Data Integrations with Alternate Keys
  • Categories

  • Archives

    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited