Autonomous Semis – The Answer to Supply Chain Woes?


Supply chain issues have been a hot topic ever since Evergreen plugged up the Suez Canal and one of the biggest supply chain issues is the lack of truck/delivery drivers. This market pressure is speeding the development of autonomous trucks. 

Now is the time for a conversation about what autonomous vehicle accident claims will look like. The big issue is data: who owns the data, Is it possible to suppress this data in a suit, and who is considered an expert witness for interpreting the data and indicating fault

Autonomous vehicles have sensors and cameras in more locations than any other kind of vehicle. The Waymos autonomous semi tractor trailer has a GPS dome above the cab, 19 individual cameras, two radar sensors, and short-, medium- and long-range rotating Lidars mounted above the cab and above the mirrors as well as a sensor suite that includes microphones for listening to approaching emergency vehicle sirens. The programming goal for the vehicle on the road is described as “Safely Assertive.”

In an accident scenario, this detailed and comprehensive data could provide an iron clad defense or very clearly expose liability. In a liability scenario, trucking defense firms may start to question who actually owns the data; is it the vehicle manufacturer, the cloud provider where the data lives, or the vehicle owner. The court system has yet to define ownership of data and conditions under which autonomous vehicle data becomes discoverable.

Then an accident scenario becomes even more complex: How does defense identify the circumstances under which camera data can be suppressed and identify the party with the duty to produce or the right to suppress the data. And it matters -- just because a truck is autonomous, does not mean it will be immune to a swoop and squat fraud scheme.  However, just because a car cuts in front of a truck and slams on the breaks, does not automatically mean the accident was a result of fraud. 

Defendants will bear the burden of proving that the software running the truck correctly interpreted the real-world data inputs or and plaintiffs will continue to argue software “glitch” scenarios. Defense will need to work in the context of comparative negligence and the potential for the claimant to exaggerate their injuries in the interests of secondary gain.

Even after data ownership and suppression has been figured out, the industry faces one more challenge: Who is expert enough to interpret autonomous vehicle data and determine liability? In an eight hour drive an autonomous vehicle could generate terabytes of data between all the obstacle sensor, software, video and audio recordings. Will software engineers or physicists be the next expert witness needed to successfully argue liability in the absence of a human vehicle driver? 

The Insurance carriers covering risk for autonomous vehicle companies will need some of the questions represented here resolved before a policy can be underwritten correctly. Producers writing risks find it challenging to set effective policy limits when there is no case law or previous verdicts to indicate potential exposure.  

We are headed for a brave new world that is evolving quickly due to current supply chain constraints.  While the concept of constant data recording may seem like it would eliminate the need for an initial claim investigation, the opposite is true.  Comprehensive investigations specifically regarding the scene exam, claimants, and witnesses are now more critical than ever as they are the only tool insurers and defendants will have available to contest the vehicle data. A team that understands autonomous vehicle complexity will be key to successful defense as the industry evolves. 

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Marci is the CEO of Fraud Sniffr, Inc., a software company that locates, downloads & distributes social media content about WC/liability claimants to their adjusters. Her online technical and linguistic algorithm skills predate Google, which has allowed her to launch and sell two search technology companies prior to starting Fraud Sniffr. She was also an adjunct professor of Marketing & Social Media for six years at The Johns Hopkins University Whiting Engineering School. Fraud Sniffr has been one of the 50 fastest growing companies in Maryland for the past three years. 


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