Plymouth, Minnesota - Automotive journalist Joel Feder and his wife were detained by multiple police officers in a coordinated stop while driving a Jaguar Land Rover press vehicle, after Flock Safety's automated license plate recognition (ALPR) cameras flagged the car based on a flawed database entry.
According to Feder's detailed account in The Drive, officers boxed in the $155,000 Range Rover in a Kohl's parking lot after the vehicle triggered alerts via Flock's network. Police had been tracking it for days, believing the New Jersey manufacturer plate (34 10 DTM) was stolen. Officers approached with hands on their weapons, ordered the couple out of the vehicle, and conducted pat-downs before verifying the car's legitimacy through Jaguar Land Rover. Feder subsequently obtained and published the body camera footage of the encounter.
The incident stemmed from an incomplete report of a similar plate (34 03 DTM) lost during a photo shoot in California, which was entered into the National Crime Information Center (NCIC) database simply as "34 DTM." Flock's AI system matched Feder's plate - ignoring the smaller middle digits - and generated alerts. Local officers did not fully verify the complete plate visible in Flock's own images.
The problem was not confined to one vehicle. Last Wednesday, fellow auto journalist Tim Esterdahl, publisher of Pickup Truck + SUV Talk, was pulled over by two officers in Scotts Bluff, Nebraska, while driving his 14-year-old child in a $105,000 Range Rover Sport loaned to him by Jaguar Land Rover for review. Its plate: New Jersey 34 08 DTM. Jaguar Land Rover has been working to correct the underlying reports.
Flock Safety maintains that its cameras performed as designed, matching partial plates per law enforcement preferences for hotlist alerts. Chief Communications Officer Joshua Thomas told The Drive the system was asked whether those characters were present and correctly answered that they were - it simply was not built to flag that additional characters existed. He conceded that for alerts originating from NCIC rather than an individual agency's custom list, the system arguably should test for an exact match rather than mere presence, and called that fair feedback to take back to his team.
Thomas said Flock is working to get the original police report corrected and is meeting with the FBI officials who curate NCIC to develop a way for incomplete data to be flagged as such for officers seeing automated alerts in the field. He emphasized that a camera alert "does not equal probable cause," comparing it to an alarm going off, and stressed that the system depends on both valid inputs and humans verifying outputs.
But the scale is what makes the error rate consequential. Thomas said the system is roughly 99 percent accurate while performing approximately 20 billion reads per month - arithmetic that leaves on the order of 200 million misreads every month. How many of those escalate into armed stops is unknown.
Plymouth police acknowledged shortcomings in verification but pointed to the challenges of varying license plate formats nationwide. According to the department's Flock transparency portal, the city operates 18 cameras that read more than 580,000 license plates in a recent 30-day period, generating over 14,800 hotlist hits - one of which was Feder.
This case adds to a growing list of incidents underscoring the privacy implications of Flock Safety's widespread ALPR deployment. As we have previously reported on ZeroHedge, these camera networks - now operating in thousands of communities across dozens of states - create detailed movement logs of vehicles with minimal oversight, raising serious questions about unwarranted surveillance of law-abiding citizens.
Critics, including privacy advocates, have long warned that reliance on partial matches, inter-agency data sharing, and integration with other surveillance tools can lead to false positives, chilling effects on daily movement, and potential misuse. While proponents highlight their value in recovering stolen vehicles and aiding investigations, the aggregation of location data over time effectively enables broad tracking without individualized suspicion.
