By Ken Fulginiti
There was a time when trucking crash investigations began with skid marks and eyewitness accounts. Today, they begin with a data download.
Modern commercial trucks are rolling servers—equipped with cameras, sensors, and telematics that record nearly every movement. Steering angle, brake pressure, fatigue warnings, GPS speed, even pre-crash micro-corrections that suggest distraction or drowsiness. For lawyers representing victims of catastrophic crashes, this flood of information is reshaping the very nature of proof.
Artificial intelligence, predictive analytics, and always-on “black boxes” have not just changed how we learn what happened. They’ve changed who we can hold accountable.
The Smart Black Box
Under 49 C.F.R. §395, the Federal Motor Carrier Safety Administration requires most interstate carriers to use electronic logging devices (ELDs) that record a driver’s hours, rest periods, and duty cycles. These systems—originally meant to ensure compliance—have evolved into real-time analytics platforms that monitor risky behavior across entire fleets.
If an algorithm repeatedly flags a driver for fatigue, late braking, or erratic lane correction, and the company takes no corrective action, that’s no longer a paperwork oversight. That’s direct corporate negligence.
Traditionally, trucking companies could be sued under respondeat superior, a theory that holds employers vicariously liable for their employees’ negligence. But when data proves the company itself ignored digital warnings, the case shifts from vicarious to independent corporate fault—opening access to the company’s assets and insurance beyond the driver’s policy limits.
The Predictive Negligence Problem
AI has given carriers the ability to predict which drivers are most likely to crash. Internal dashboards can identify “high-risk” drivers by merging telematics, weather data, and route histories.
The question is: what happens when those warnings are ignored?
A company that continues to deploy a flagged driver or a malfunctioning truck is effectively gambling with human life. And under Pennsylvania’s Fair Share Act, that gamble has measurable consequences.
If we can prove that a defendant’s negligence exceeded 60%, the company becomes jointly and severally liable for the entire verdict. That’s why our litigation strategy focuses not only on the driver’s final decisions but on the corporate decisions made weeks or months earlier—the failure to act on predictive data that screamed “danger” in real time.
When Data Vanishes
The challenge is not just obtaining this evidence—it’s keeping it from disappearing.
Most telematics systems automatically overwrite old data within 30 to 180 days. That’s why, in every major trucking case, our first move is an immediate spoliation letter. Under Federal Rule of Civil Procedure 37(e), once a company is on notice of potential litigation, it must preserve all electronically stored information relevant to the crash.
When they don’t? The court can issue adverse inference instructions or even default judgments.
We’ve seen cases where “missing” ELD data conveniently vanished after a fatal crash, only for a cloud backup to reveal dozens of prior fatigue alerts. That’s not just a discovery dispute—it’s a window into systemic misconduct.
Strategic Discovery: Connecting Doctrine to Data
Trucking litigation succeeds when doctrine, strategy, and evidence align. Each theory of negligence corresponds to a specific category of data that proves it.
| Negligence Theory | Target Evidence | Strategic Objective |
| Hours-of-Service Violation (49 C.F.R. §395.3) | ELD logs, dispatch records | Establish fatigue, frame corporate profit-over-safety narrative |
| Negligent Hiring / Retention | Employment files, background checks | Show company knew driver posed risk |
| Negligent Maintenance | Repair logs, inspection reports | Prove mechanical failure was foreseeable |
| Predictive Negligence (AI analytics ignored) | Risk dashboards, driver scorecards, email alerts | Demonstrate deliberate corporate indifference |
When discovery is mapped this way, every subpoena serves a tactical purpose. Every exhibit tells the story of corporate decision-making. The process itself becomes proof of negligence.
The Algorithm as a Witness
In traditional trials, we relied on human witnesses. Today, we rely on machines that can’t lie—but can be misinterpreted.
Telematics data, lidar mapping, and sensor logs can now recreate entire crash sequences with precision that no eyewitness could match. Yet both plaintiffs and defendants increasingly use AI to generate competing “truths.” One side may simulate driver fatigue; the other may argue the AI over-interpreted shadows or weather conditions.
That’s where the law must evolve. As courts weigh algorithmic evidence, judges and juries must ask: who programmed the model? Who validated it? Was the system itself reasonably safe, or was it a defective product in disguise?
When AI makes a decision that causes harm—say, a braking system misreads a hazard—the line between human negligence and software negligence blurs. It’s no longer “what did the driver see?” but “what did the code decide?”
Litigation Strategy Spotlight
Spoliation & Evidence Preservation:
Every ELD system is designed to delete. Every plaintiff’s lawyer must be ready to stop the clock. Our firm issues spoliation letters within 24 hours of engagement, demanding preservation of all digital records—vehicle telematics, in-cab video, dispatch logs, and AI risk assessments. The moment data is lost, leverage shifts. Courts recognize that deletion equals deception.
That’s not procedural housekeeping. That’s litigation strategy.
AI’s Promise—and Its Legal Reckoning
There’s no denying AI can make trucking safer. Predictive models can flag drowsy drivers, prevent collisions, and identify maintenance risks before parts fail. But every innovation introduces new accountability gaps. When a company ignores its own technology’s warnings, or when the system itself fails due to bad code, the consequences belong in court.
The law is clear: the duty to act on known danger doesn’t disappear because the danger came from an algorithm. It grows stronger.
Where Law Meets Accountability
At the end of every case, behind every dataset and reconstruction, there’s a family asking one question: Could this have been prevented?
When we prove a company ignored the signs—digital, human, or both—we don’t just win compensation. We expose choices that endangered everyone on the road. And when those choices push fault above 60%, we unlock full recovery under the Fair Share Act.
Technology can help us find the truth faster. But it can’t replace the principle at the heart of every verdict: that those who knew, or should have known, must be held accountable.
About Ken Fulginiti
Ken Fulginiti, of Philadelphia’s Fulginiti Law, is dedicated to helping individuals and families affected by catastrophic injuries, securing significant results, including some of the largest general liability settlements for minors in Pennsylvania history. His practice focuses on cases involving motor vehicle and trucking accidents, construction accidents, product defects, premises liability, negligent security, medical malpractice, clergy sexual abuse, and other serious injuries.
