David Julian is the CTO and cofounder of Netradyne.
Fleet safety managers have to juggle various tasks on the job to protect their fleets, including sifting through complex data to identify risks and opportunities for improvement.
While fleet management technology has evolved over the years, many solutions today still present limitations. However, natural language interfaces powered by agentic LLMs can overcome these limitations. They can give fleet safety managers a greater degree of analysis, customization and flexibility, enabling them to use data more strategically to protect everyone’s safety on the road.
Limitations Of Current Fleet Management Technology
Current fleet management technology can help fleet safety managers get the job done, but it has drawbacks.
Notably, these tools usually offer minimal options for customization and flexibility. Additionally, their analytical capabilities are limited to predeveloped report formats. They tend not to be very interactive either, making it difficult or impossible for fleet safety managers to interact with them beyond the confines of their existing UX. For instance, a fleet management tool might alert a manager every time drivers don’t stop at a red light, but it might not make it possible for the manager to drill down and see what other factors might be at play when drivers end up running red lights, such as weather, traffic density and stale green lights. Such information is useful for training purposes and coaching decisions.
How Natural Language Interfaces Powered By Agentic LLMs Work
However, emerging technology—natural language interfaces powered by agentic LLMs—can give fleet safety managers more options for analysis, customization and flexibility. Agentic LLMs formed with pretrained LLMs are more sophisticated than traditional artificial intelligence interfaces because they can plug into other applications, databases, etc., to evaluate multi-modal information (such as GPS data, dash cam footage and vehicle telemetry) and provide insights and make recommendations as prompted. LLM assistants enable fleet safety managers to go beyond the existing point-and-click UX and tap into the potential of the underlying data, not just what’s visible in standard reports. Full disclosure: My company is working on one such solution.
Where a point-and-click interface might limit managers to predefined views, an agentic LLM can parse complex, multi-source information, connect disparate data points and generate nuanced insights that might otherwise remain hidden within vast amounts of fleet data. Moreover, an LLM can enable managers to iteratively interact with the data—specifically, by giving them the opportunity to ask follow-up questions.
Some questions a fleet safety manager might ask an LLM assistant include:
• “Could you please create a list of all my drivers who have run at least five red lights in the last month?”
• “Which of my vehicles have low tire pressure?”
• “Where in the route is driver Jane Doe currently?”• “Based on your analysis of individual drivers, which are most in need of coaching?”
• “What are the top three risky behaviors exhibited by driver John Doe in the past month, and what are the top three areas of good driving?”• “We received a customer complaint that one of our drivers may have hit a mailbox at 123 Main Street in the last 24 hours. Could you identify any potential vehicles it might have been?”
• “Which of my vehicles are most prone to flooding, based on their location, terrain they frequently drive on and history of water-related incidents?”
The answers to these questions and more can assist fleet safety managers with coaching, route assignments and other calls they need to make to increase safety and efficiency. They can also help fleet safety managers identify systemic issues, such as tricky intersections, that need to be addressed. For instance, based on the response to the question, “Which of my vehicles are most prone to flooding, based on their location, terrain they frequently drive on and history of water-related incidents?” an LLM can identify the vehicles most at risk, and on rainy days, a fleet manager can avoid putting those vehicles on the road. Or based on the response to the question, “What are the top three risky behaviors exhibited by driver John Doe in the past month, and what are the top three areas of good driving?,” a fleet safety manager can determine what feedback to give John Doe.
The iterative, interactive nature of agentic LLMs enables fleet safety managers to ask follow-up questions and drill down into data beyond what’s possible with a tool that leverages a traditional UX. So, in this case, the fleet safety manager could ask, “What are the key points I should emphasize when giving feedback to John Doe?” or “May you help me put together a comprehensive training program for John Doe based on these identified areas?” After the coaching begins, to gauge the effectiveness of the program, the fleet safety manager can request that the LLM tackle follow-up tasks via directives such as, “Please analyze John Doe’s current performance, compare it to what it was three months ago, and identify which areas need the most improvement.”
Moreover, since fleet managers have more flexibility in how they interact with agentic LLMs and don’t have to manually connect the dots in the data, they can streamline processes at their company, leading to quicker decisions. A fleet safety manager can create generic triggers, building on small scripts that instruct an agentic LLM to track and generate types of alerts. For instance, a fleet safety manager could create a trigger for tire pressure and tell the agentic LLM, “Please track the tire pressure of my semi trucks, and if the pressure drops below 85 PSI, send an alert to the maintenance shop and include the vehicle’s number and its tire pressure.” Additionally, if agentic solutions are built on pretrained LLMs and LGMs fused with driving data, fleet safety managers can swiftly access other data, such as DMV manuals and severe weather safety tips.
Drawbacks Of This New Technology—And How To Mitigate Them
While this new technology enables fleet safety managers to do more with data, it poses some challenges.
For one, natural language interfaces powered by agentic LLMs often need to retrieve substantial amounts of information from various databases, which can take 10 to 30 seconds—a slower response time than tools that leverage a traditional UX. Fleet safety managers should be mindful of these delays. Additionally, as with any AI solution, there’s the risk of inaccurate outputs. To safeguard against this, it’s important that the LLMs provide source references for their statements that allow users to validate the responses. Additionally, it’s important that these systems have processes in place to identify and address the potential for inaccurate information. For instance, during the pre-prompt design stage, engineers can dictate that an LLM states, “This is out of scope,” for questions that are out of scope, and says “I don’t know” if it’s not sure what to say.
As this technology becomes more available and adopted by the industry, fleet safety managers will be able to work faster and more strategically to protect everyone on the road.
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