The FMCSA has updated the Crash Preventability Determination Program (CPDP). One of the changes significantly affects dash camera users. Here’s what you need to know.
Tom Bray
The Federal Motor Carrier Safety Administration (FMCSA) has updated the Crash Preventability Determination Program (CPDP). This update is big news because FMCSA is expanding the types of crashes they review. One of the changes sifnificantly affects dash cam users.
What is the CPDP?
The CPDP allows a carrier to request a preventability review of a crash. If it is found not preventable, the crash will not be scored in the carrier’s Crash BASIC in the Compliance, Safety, Accountability (CSA) program,. “Not-preventable” means the carrier’s driver did not cause the crash and could not have avoided it.
For a crash to be reviewed, it has to fall into specific categories (struck in the rear, struck in the rear at the side, struck by a motorist going the wrong direction, etc.).
To request a preventability review for a crash that meets eligibility criteria, the carrier must:
- Initiate a DataQs request for data review (RDR) asking for a preventability determination.
- Provide a copy of the police accident report and any other documents to support their argument.
- Submit post-crash drug and alcohol test results if there was a fatality.
Additional Crash Types Eligible for Review Added
With this update, FMCSA is accepting preventability RDRs for more types of crashes. For example, one of the new categories is a crash where dash camera video provided by the carrier demonstrates the crash was not preventable.
Dash Camera Footage Could Make Any Crash Reviewable
This is a game-changer – any crash can be reviewed and determined to be not preventable, not just the ones that fall into specific categories. If video footage from a dash camera proves the crash was not preventable, FMCSA will review it and make a preventability determination.
Adding to the List of Reasons to Have Dash Cams
Many carriers have experienced the advantages of dash cameras. First, there is the exoneration power of dash cameras. Having video footage that clearly shows what happened when the crash occurred allows a carrier to settle accident claims quickly.
Second, there is the reduction in crashes many carriers using dash cameras have experienced. This is because dash cameras allow the carrier to locate problem behaviors and counsel, coach, and retrain the drivers involved. The benefit of fewer crashes is a reduction in claims losses and an improvement in the carrier’s Crash BASIC score in CSA.
This update to the Crash Preventability Determination Program provides an additional advantage to having dash cameras.
Timing
The FMCSA announced in a Federal Register, notice that the new eligibility criteria went into effect for crashes occurring on or after December 1, 2024. Therefore, if you had a crash that occurred on or after that date, you can ask for a preventability determination based on dash camera video footage alone.
Road Legends
Artificial Intelligence (AI) has emerged as a game-changer in various sectors, and the trucking industry is no exception. From optimizing routes and fuel efficiency to enhancing safety and improving fleet management, AI is revolutionizing the way trucks operate on the road. Let’s find out the power and impact of AI in the trucking industry by exploring the benefits it brings, the challenges it addresses, and the future possibilities it unlocks.
Enhancing efficiency and optimizing operations with AI
AI is revolutionizing the trucking industry by enhancing operational efficiency. Intelligent route planning and optimization algorithms minimize travel time and maximize fuel economy. Fuel efficiency and cost reduction are achieved through AI-powered systems that analyze various factors influencing fuel consumption. Predictive maintenance utilizes AI to monitor vehicle health, reducing breakdowns and optimizing fleet management. AI is also streamlining supply chain and logistics operations, ensuring timely deliveries and inventory management. Let’s talk in detail below.
Intelligent route planning and optimization
AI-powered route planning algorithms consider real-time traffic data, weather conditions, and other variables to optimize truck routes. By analyzing historical and current data, AI systems can identify the most efficient routes, reducing travel time and fuel consumption. These intelligent systems can adapt to dynamic situations, providing drivers with real-time updates and alternative routes to avoid traffic congestion or road closures. Intelligent route planning and optimization not only improve efficiency but also enhance customer satisfaction by ensuring timely deliveries.
Fuel efficiency and cost reduction
AI plays a crucial role in improving fuel efficiency and reducing operational costs in the trucking industry. Advanced AI algorithms analyze various factors such as load weight, road conditions, and driving behavior to optimize fuel consumption. AI systems can provide real-time feedback to drivers, promoting fuel-efficient driving techniques. Additionally, AI-powered predictive analytics can identify patterns and anomalies in fuel usage, enabling companies to implement strategies to reduce fuel waste. By leveraging AI for fuel efficiency, trucking companies can significantly reduce their operational costs and minimize their environmental impact.
Predictive maintenance for fleet management
AI-driven predictive maintenance is transforming fleet management by optimizing vehicle maintenance schedules. Using sensors and data analytics, AI systems monitor key parameters such as engine performance, tire condition, and fluid levels in real-time. By analyzing historical data and detecting patterns, AI algorithms can predict potential maintenance issues before they escalate into major breakdowns. This proactive approach enables companies to schedule maintenance and repairs at convenient times, minimizing downtime and improving fleet availability. Predictive maintenance not only enhances operational efficiency but also prolongs the lifespan of vehicles, reducing overall maintenance costs.
Streamlining supply chain and logistics
AI technologies are streamlining the supply chain and logistics operations in the trucking industry. AI-powered systems analyze vast amounts of data related to inventory levels, customer demand, and delivery routes. By optimizing these variables, AI can identify the most efficient routes, minimize empty truck miles, and improve load consolidation. Additionally, AI-driven demand forecasting and inventory management systems enable companies to optimize stock levels and reduce inventory holding costs. With improved supply chain visibility and efficiency, trucking companies can enhance customer service, reduce costs, and maintain a competitive edge in the market.
Revolutionizing safety and risk management
AI is revolutionizing safety and risk management in the trucking industry by leveraging advanced technologies. Real-time monitoring and early warning systems equipped with AI algorithms can detect potential risks such as driver health issues, fatigue, distraction, and hazardous road conditions. These advancements in safety technologies are improving road safety and reducing the likelihood of accidents in the trucking industry.
Real-time monitoring and early warning systems
Real-time monitoring and early warning systems utilize AI technologies to detect and mitigate potential risks on the road. Advanced sensors and AI algorithms monitor driver behavior, vehicle performance, and environmental conditions. By analyzing data in real-time, these systems can detect signs of driver fatigue, distraction, or erratic driving. Early warning alerts are provided to the driver and fleet managers, enabling timely interventions and reducing the risk of truck driver accidents. Real-time monitoring and early warning systems enhance safety by proactively addressing potential risks and promoting safe driving practices.
Driver assistance and safety features
AI-powered driver assistance features are improving safety in the trucking industry. These features include collision avoidance systems, adaptive cruise control, and lane departure warnings. By utilizing sensors and AI algorithms, these systems can detect potential hazards, provide alerts, or take corrective actions.
For example, collision avoidance systems can automatically apply brakes in emergency situations, reducing the risk of rear-end collisions. Adaptive cruise control adjusts the vehicle’s speed to maintain a safe distance from other vehicles. Lane departure warnings alert drivers when they unintentionally drift out of their lane. Driver assistance features enhance safety by assisting drivers in avoiding potential accidents.
AI-enabled video analytics for accident prevention
AI-enabled video analytics systems analyze video footage from cameras installed in trucks to identify risky driving behaviors such as tailgating, aggressive maneuvers, or distracted driving. By detecting these behaviors, AI algorithms can provide valuable insights for driver training and behavior modification. Additionally, these systems can assist in accident investigations by providing accurate data and visual evidence. AI-enabled video analytics contribute to accident prevention by identifying high-risk behaviors, promoting safe driving practices, and improving overall road safety.
Transforming driver experience and well-being
AI is revolutionizing the trucking industry by transforming the driver experience and prioritizing driver well-being. Intelligent driver assistants, fatigue and distraction monitoring systems, and training and skill development programs are enhancing driver performance, safety, and overall job satisfaction.
Intelligent driver assistants
Intelligent driver assistants provide real-time feedback, guidance, and support, improving driver performance and safety on the road. From collision avoidance systems to adaptive cruise control, these AI-enabled assistants enhance situational awareness and assist in making critical driving decisions.
Fatigue and distraction monitoring
Fatigue and distractions pose significant risks to truck drivers. AI-based fatigue and distraction monitoring systems utilize advanced technologies such as facial recognition and eye tracking to detect signs of fatigue or distraction in real time. These systems issue alerts to drivers, reminding them to take breaks or refocus their attention, thus preventing accidents caused by driver fatigue or distractions.
Training and skill development
AI-driven training and skill development programs are empowering truck drivers to enhance their capabilities and stay updated with industry trends. Virtual reality (VR) simulations, online training modules, and personalized learning platforms equipped with AI algorithms enable drivers to receive targeted training, practice challenging scenarios, and improve their skills. Such programs contribute to professional growth, job satisfaction, and increased confidence among truck drivers.
Advancing autonomous trucking with AI
AI is propelling the advancement of autonomous trucking, revolutionizing the industry by introducing various levels of automation. From driver assistance systems to fully autonomous trucks, AI is reshaping the future of transportation.
Level 1-5 automation: understanding the spectrum
Autonomous trucking operates on a spectrum that encompasses five levels of automation. Level 1 involves basic driver assistance features, while level 5 signifies complete autonomy without human intervention. Understanding this spectrum is crucial for evaluating the capabilities, limitations, and potential risks associated with different levels of autonomous trucking.
AI-powered sensors and perception systems
AI-powered sensors and perception systems are the eyes and ears of autonomous trucks. These advanced technologies, including lidar, radar, and cameras, gather real-time data about the surrounding environment, enabling the truck to perceive and analyze its surroundings accurately. By interpreting this data, AI algorithms facilitate decision-making and ensure safe navigation for autonomous trucks.
Data-driven decision-making for autonomous trucks
Autonomous trucks heavily rely on data-driven decision-making. AI algorithms process vast amounts of sensor data, including road conditions, traffic patterns, and vehicle dynamics, to make real-time decisions. These decisions encompass navigation, route planning, speed adjustments, and responding to unexpected situations. By leveraging data-driven decision-making, autonomous trucks can operate efficiently, optimize fuel consumption, and ensure a smooth and safe transportation experience.
Overcoming challenges and ethical considerations
While AI offers numerous benefits, it also poses challenges and ethical considerations that need to be addressed to ensure responsible and safe implementation in the trucking industry.
Data privacy and security
The use of AI in the trucking industry involves the collection and analysis of vast amounts of data. Ensuring data privacy and security is crucial to protecting sensitive information and preventing unauthorized access or misuse. Robust cybersecurity measures, data encryption, and strict data governance policies are essential for maintaining the privacy and integrity of driver and operational data.
Liability and legal implications
As AI takes on more responsibilities in autonomous trucking, questions regarding liability and legal implications arise. Determining accountability in the event of accidents involving autonomous trucks, establishing regulations, and defining the legal framework are complex issues that need to be addressed to ensure a smooth transition to a future with AI-powered trucks.
Human workforce transition
The integration of AI and autonomous trucks raises concerns about their impact on the human workforce. As automation increases, there will be a need to manage the transition for truck drivers, whose roles may evolve or be replaced. Supporting retraining programs, facilitating job transitions, and addressing potential socio-economic impacts are critical to ensuring a smooth transition for the human workforce.
The future possibilities and implications of AI in trucking
The future possibilities of AI in the trucking industry are vast and exciting. From fully autonomous trucks to AI-enabled predictive maintenance and advanced logistics optimization, the potential for AI to revolutionize the industry is immense. However, careful consideration of implications such as job displacement, regulatory frameworks, and ethical guidelines will be crucial to harnessing the full potential of AI in a responsible and sustainable manner.
The artificial intelligence boom is poised to expand into 2025, bringing with it exciting new solutions for fleet operations. Improved large language models will be able to tackle significantly more complicated tasks, boosting safety and efficiency.
Jeremy Wolf
Artificial intelligence—especially machine learning—has already transformed the transportation industry in a short few years. As carriers look ahead to 2025, they can expect the information revolution to continue.
The nature of AI’s usefulness for transportation, however, will remain the same: quicker, simpler data and insights.
“The biggest benefit of AI—applied to fuel purchasing, route optimization, equipment utilization, and safety—is the immediacy of information,” Adam Kahn, chief business development officer at Netradyne, told FleetOwner. “Can you get more data-driven decisions around what’s happening with your operation?”
AI tools today already help carriers process and utilize vast amounts of information. For 2025, AI’s capabilities and influence will likely grow even further.
AI in transportation today
What is AI, machine learning?
The term artificial intelligence is vague. Any machine that uses perception, learning, or decision-making can be called AI. This means nearly every computer built in the last several decades contains programs that fit some loose definition of AI.
A more precise term is machine learning, a form of AI where computers learn from existing data to make inferences without explicit instructions. Autonomous vehicles, large language models, and predictive analytics are all examples of machine learning applications.
Machine learning solutions make up a vast majority of the AI market, causing many people to use the terms interchangeably.
The greatest uses of AI in transportation today are for data processing and analysis. Carriers can leverage machine learning to automatically draw inferences from existing data—or to quickly digitize existing paperwork.
“AI is the aggregation of a lot of data and creating useful workstreams around that data,” Kahn explained. “If you can take a lot of information and apply some reasoning against it, then there should be growing opportunities around where to buy fuel or what routes to assign to a vehicle.”
When technology leaders discuss the potential of AI applications for carriers, there are six areas that stand out: data analysis, fuel efficiency, route optimization, asset management, safety, and autonomous driving.
The number of industry solutions with the word “AI” attached to them are too many to count. Here are just a few examples of recent AI solutions for each area:
Netradyne
How AI will shape trucking in 2025
Artificial intelligence will continue its boom next year. The chip manufacturing market has a bright future, and large language models show a steady rate of improvement. What’s more, AI might unlock new solutions for fleets in 2025.
Improving chip market, AI capability
The AI hype cycle
Only a few years ago, people commonly discounted AI applications as “not real intelligence” once the applications became popular. This changed, however, after extremely charismatic generative AI tools entered the scene. Image generation with DALL-E and text generation with ChatGPT skyrocketed the popularity of AI in 2022.
Now, AI is a fashionable marketing buzzword sometimes used to mislead and deceive customers. Some firms predict AI is at the peak of its hype cycle and about to burst. Others predict the valuation of AI will continue to grow at a breakneck pace.
Valuations of chip manufacturing exploded with the AI boom, skyrocketing the worth of companies like Nvidia and AMD. The semiconductor market is soaring and projected to continue growing with demand for AI.
A booming semiconductor market suggests greater access to—and demand for—AI tools over time. However, some businesses predict a global chip shortage could bottleneck access to AI solutions.
Large language models are still improving at an exponential rate. They are continually able to process more complex tasks as their context windows grow.
AI tools also face a slimmer chance of government regulation next year. An incoming Trump administration means AI regulation is less likely than under a Democratic administration, Kahn suggested.
“Restrictions around regulating AI will maybe open up to allow for more independent commerce to figure out what it really means—versus having the government tell you what it’s not going to be,” Kahn told FleetOwner.
New AI applications for fleets next year
AI technology will likely continue to improve in 2025, and AI solutions will become more widely available. But what improvements are important for fleet operations?
Paul Pallath, VP of applied AI for Searce, pointed to three areas where AI could change transportation in 2025—AI agents, multimodal data management, and video generation.
AI agents
Large language models ((LLMs) are one of the most potent AI developments in recent years. LLM developers are looking to make the technology even more impactful.
Agentic AI, sometimes called robotic process automation, may be the next major development for large language models. This technology places a single AI chatbot in front of the user for commands—but behind the chatbot lies an intricate stack of multiple AI tools.
Users can ask the agent to perform a data-centric task in a similar conversational way that they would ask an employee. Layers of LLMs could perform, review, and automate the processes required for the overall task. Getting an invoice number or reconciling multiple invoices could be done much more quickly—and simply—with the help of agentic AI.
AI giants like Google with Gemini 2 and Nvidia with Blueprints are already developing agentic AI to solve more complicated tasks.
“The premise of Gemini 2 is that it enables agentic AI at scale that allows us to now have autonomous bots … that manage multiple different intelligent bots that are task-specific large language models,” Pallath said.
Multimodal data management
One application already growing in popularity is using AI to consolidate disparate forms of data. Paper forms and video feeds contain helpful data—but joining them in a single spreadsheet for a fleet’s TMS can involve a lot of manual labor. AI-powered image and language processing could automate the process.
“LLMs are multimodal, which means that it can connect with data, which is textual information, audio information, and video information, and process it in one go,” Pallath said. “If I’m asking for something, and if this information is in three disparate sources, LLMs now have the capability to summarize all of these, take the entire context, and give me a response.”
AI is already turning video feeds into automated truck logging and driver distraction detection.
“There are pockets where AI is thriving. Video-based telematics is moving very aggressively in that space,” Pallath said. “There has been a monumental shift in the results of that, where you see increased accident reduction and increased driving risk reduction.”
In addition to videos, AI tools could also consolidate paperwork such as bills of lading or audio recordings of customer transactions into more accessible formats. Paired with AI agents, using and accessing several types of information could become much easier.
Video generation
Pallath also suggested that AI video generation could become much more useful in 2025.
Similar to large language models, AI video generators have entered an arms race. Companies like Google and OpenAI continue to launch competing AI video generators that appear more and more convincing.
Paired with technical documentation, Pallath said AI video generation could soon create training videos for maintenance operations.
ID 324969265 © Doberman84 | Dreamstime.com
AI image generation fails to make accurate depictions of sophisticated technology, like engines. Depicted here is what AI thinks an engine bay looks like.
AI-generated video content is still far from accurate, especially when it comes to technical subjects. Even AI-generated images fail to depict advanced machines accurately, but Pallath believes the technology will continue to improve quickly.
“It is weak, but the rapid pace at which it is becoming better is absolute,” Pallath said. “I’m sure that in the first half of the year, this technology will be robust enough for us to leverage towards the second half of the year.”
Ultimately, however, Pallath stressed that companies should keep ethics top-of-mind when they consider implementing AI.
“When we think about creating these AI interventions across businesses, thinking through responsible and ethical use of AI—and what should I as a company do or not do with AI—is going to be paramount,” Pallath said.
Jeremy Wolfe
Donald Trump resumed control of the nation’s federal agencies this week. With a change in administration, what will agency management look like for 2025?
This is the first part of a three-part series on 2025’s regulatory outlook. You can read part two here. Part three will be linked here when it is published.
Agency rulemakings will likely slow down under a Trump administration, but there are still several key regulations that may impact carriers this year.
Industry experts from Scopelitis Law Firm and the Truckload Carriers Association shared their outlook on how the new administration will manage commercial carriers.
Fewer agency rulemakings
Agency rulemakings in general will likely slow down through 2025 and the rest of Trump’s term. EPA and the U.S. Department of Transportation—including its subsidiaries the National Highway Traffic Safety Administration and Federal Motor Carrier Safety Administration—will likely see fewer new regulations.
One of the most significant parts of another Trump administration is the return of agency deregulation. In the first month of his first term, Trump issued an executive order requiring a “two-for-one” deregulation effort. For every one new regulation with significant costs, two others needed to be removed.
Agencies move slower between presidents
The leadership transition should also slow down rulemaking processes, particularly in early 2025. Each new president involves widespread replacement of top agency officials.
“Somebody comes into the seat to lead FMCSA and they have to be caught up to speed on where they are on the rulemakings, brought up to speed on the rulemaking process and where they stand right now, as well as understanding all the background of those rules that were going on in the Biden administration that may or may not move forward in the Trump administration,” Heller said.
Trump already issued a hiring freeze and regulatory freeze on the first day of his administration. If the freezes mirror those of his first term, they may remain in effect for months.
“We are wondering if something like that still comes into play,” David Heller, VP of government affairs for TCA, told FleetOwner. “It was not necessarily agency specific. It could be two DOT rules that could be rolled back, that may not necessarily have anything to do with trucking, in order to institute a new rule that did pertain to trucking.”
The “two-for-one” order had a small impact on new regulatory costs, and Trump might pursue a more extreme measure this time. In December, he suggested a “ten-for-one” order for his next term.
Rulemakings carriers should watch
Transportation agencies will continue to develop new regulations, despite the slowdown. Regulators have several major new pending rules that could affect commercial carriers. Fleet managers should watch the following rules in 2025.
Broker transparency
FMCSA may issue a final rule on broker transparency in 2025. The agency issued a notice of proposed rulemaking in November 2024, “Transparency in Property Broker Transactions,” which makes it more difficult for brokers to avoid their obligation to disclose records to carriers.
Carriers have a right to the records of freight brokers’ transactions by law, but brokers use several workarounds to avoid real disclosure. This includes contract waivers, slow and manual paperwork, and an implied threat of retaliation. The NPRM proposed four steps to weaken brokers’ paperwork approach but did not address contract waivers or retaliation.
Scopelitis’s Sharma noted that broker transparency is a uniquely intrusive regulation but is likely supported under the second Trump administration.
“FMCSA’s recent proposed rulemaking is a case of regulatory intrusion into a transportation marketplace that Congress deregulated and intended to leave up to the markets. No analogous government mandate to service cost disclosure comes to mind, but that is what the FMCSA has proposed mandating,” Sharma said. “That said, it’s worth recalling that the first Trump administration elected to publish the petitions submitted by OOIDA and the Small Business Trucking Coalition, thus essentially initiating the rulemaking process.”
FMCSA will likely publish a final rule on broker transparency within the year.
Independent contractor rule
The U.S. Department of Labor’s independent contractor rule, which lays out how the department differentiates between employee and contractor under the Fair Labor Standards Act, is a contentious issue in trucking. Independent contractor classification is a key policy issue for most trucking industry groups.
The first Trump administration’s DOL issued a final rule outlining its interpretation of FLSA in 2020. The definition departed from precedent established by case law in favor of an agency standard that was simpler and erred toward contractor status. The Biden administration issued a rule overriding that interpretation in 2024. The new rule mostly coincided with the original but moved closer to existing case law and, because of this, erred closer to employee status.
The Trump DOL now has the opportunity to issue another overriding rule, returning to an interpretation that resembles the 2020 final rule.
“We expect the president-elect to revert back to what he did in his previous administration and support the independent contractors as they are today,” Heller said.
However, this Trump term is different from the first. Labor issues may not be as easily predicted.
Trump’s pick for Secretary of Labor, Lori Chavez-DeRemer, was one of only three Republicans who voted against party lines in favor of the PRO Act. Trump also played a surprise pro-labor role weeks before his inauguration when he supported the International Longshoremen’s Association union in their negotiations with dock employers.
Despite the pro-labor twists in Trump’s ramp-up to presidency, Sharma also expects the 2020 rule to return.
“We anticipate that rule to be reinstated via the current multiple litigations challenging the Biden 2024 IC Test Rule,” Sharma said. “People were surprised by the nomination of former Rep. Lori Chavez-DeRemer, one of three Republican House members to vote for organized labor’s PRO Act, but we still believe a California-like ABC test (requiring new legislation) for federal wage and hour purposes is unlikely.”
CSA scores
FMCSA is working on an overhaul to its Compliance, Safety, Accountability program. The agency proposed major revisions to CSA scores in early 2023 and announced further changes in December 2024. FMCSA may formally publish the revisions as a final rule in 2025.
CSA scoring through the Safety Measurement System is a major component of modern fleet operations, influencing everything from client relations and insurance rates to federal investigations.
Trucking industry stakeholders have criticized CSA scoring for not addressing how each state differs in its enforcement priorities.
“The biggest issue with CSA is almost always going to be its correctness to eliminate geographical biases,” Heller said. “As an industry, we shouldn’t be afraid to have our safety systems measured at the carrier level; we should insist it be done correctly.”
While FMCSA has regularly tweaked CSA scoring since 2010, the latest update still does not normalize for geography.
It is still unclear when FMCSA will publish the CSA overhaul final rule, but Scopelitis sees a good chance it will arrive this year.
“We anticipate the FMCSA will move forward with this proposal in 2025, even under a new administration,” Chris Eckhart, attorney with Scopelitis, told FleetOwner.
While the industry waits for a final rule, the agency allows carriers to preview what their new scoring might look like through the CSA Prioritization Preview website.
Crash Preventability Determination Program
FMCSA is also developing a final rule to update its Crash Preventability Determination Program. If a carrier suffers a crash and then shows FMCSA that it was non-preventable, that incident won’t affect the carrier’s CSA score.
The program began in 2020 and received regular adjustments since. FMCSA issued a notice for its latest update in December. The update adds four new crash types to the program’s existing 16 crash types, expanding which types of incidents are eligible under the program. The new crash types include accidents where another motorist lost control and where a video demonstrates the sequence of events.
“With motor carriers’ increasing use of onboard cameras, this additional crash type is a significant improvement to the CPDP,” Eckhart said.
Speed limiters
FMCSA and NHTSA are still working on a joint rulemaking to mandate speed limiters in heavy commercial vehicles. The agencies first issued a notice of proposed rulemaking in 2016.
“Speed limiters have been kicked down the road several times,” Heller said. “There were several due dates in which the agency was going to come out with a supplementary notice of proposed rulemaking.”
In the fall 2024 regulatory agenda, FMCSA and NHTSA suggested they would issue a supplementary notice of proposed rulemaking in mid-2025. If successful in publishing an NPRM, it would still take several months or years before the agencies issue a final rule. The final rule would then allow several months or years before the limiter requirements for OEMs take effect.
In addition to rulemaking delays, the agencies have also not yet shared the exact speed limit that their mandate would set. FMCSA initially proposed 68 mph in 2023 but quickly revoked the number.
“Because of the constant delay in issuing the SNPRM, we’re not wholly convinced that this rulemaking is going to be moving forward,” Heller said.
AEB mandate
FMCSA and NHTSA are planning a final rule to mandate automatic emergency braking systems on new heavy trucks.
The agencies issued a joint NPRM for the AEB rule in 2023. If the agencies do release the AEB final rule in 2025, it would still take multiple years to affect manufacturers. When NHTSA issued an AEB final rule for passenger vehicles in 2024, it set the effective date as September 2029.
AEB systems are also already popular among large carriers.
“I think the tea leaves almost always read that innovators are going to beat regulators,” Heller said. “And AEB is an innovation that carriers are already using.”
Carrier registration system
FMCSA is still working on its next version of an online carrier registration system, which could transform registration processes this year.
Carriers have to use several separate paper forms to manage and update their information. The agency hopes a new registration system can simplify carrier registration processes, forms, and verification through a single online platform.
The new FMCSA Registration System, or FRS, would replace the Unified Registration System (URS), which suffered from poor implementation. FMCSA first planned to develop URS in 1996, created the platform with only partial functionality in 2015, and then never finished it. URS today still only serves first-time applicants with their initial registrations.
FRS, if it lives up to the hype, would integrate several forms into a simplified series of questions and add more robust verification to combat fraud. FMCSA suggested it would launch FRS sometime in 2025.
Mark Schremmer
Addressing Highway Trust Fund shortfalls and a lack of truck parking were among the topics discussed at the House Highways and Transit subcommittee’s first hearing of the 119th Congress.
The subcommittee held the hearing “America Builds: Highways to Move People and Freight” on Wednesday, Jan. 22.
Rep. David Rouzer, R-N.C., chairman of the subcommittee, used his opening statement to discuss inequities with the current Highway Trust Fund, which uses fuel taxes to pay for federal road and bridge projects. According to Rouzer, the fund hasn’t been fully solvent since 2008.
“We must also have a frank conversation about the solvency of the Highway Trust Fund – the main funding source for highway projects,” Rouzer said. “Since 2008, Congress has transferred approximately $275 billion to cover the shortfall of revenues as expenditures have grown.”
Although the problem is not a new one, the congressman said it is time for lawmakers to figure out a new funding mechanism, as electric vehicles are not contributing to the current system.
“Highway funding relies on a user-pay principle,” Rouzer said. “It’s pretty simple: You purchase fuel to fill up your vehicle to use the roads, and the fuel tax collected from that purchase is put into the Highway Trust Fund. However, electric vehicles, which are often heavier than their conventional counterparts because of the weight of their batteries, do not pay in the Highway Trust Fund.”
In previous sessions, a vehicle-miles-traveled tax and tolls have been presented as potential ways to correct the issue. However, a VMT tax has raised concerns over privacy, and the trucking industry has argued against efforts to create truck-only tolls.
Rouzer suggested that getting all vehicles to pay their fair share should be a priority.
“It is wholly unfair that an entire segment of users doesn’t contribute to the roads and bridges they use,” he said. “This won’t address the greater solvency issue, obviously, but we must rectify this so that all users are treated fairly and contribute to the systems on which they rely.”
Dennis Dellinger, president of Cargo Transporters, testified that funding should be generated in an equitable manner.
“The trucking industry is the leading payer into the Highway Trust Fund, contributing almost half of all revenues while representing less than 5% of road users,” Dellinger wrote in his submitted testimony. “While the trucking industry is proud to pay our fair share, Congressional attention and action is necessary to ensure a lasting, viable and equitable revenue source for continued infrastructure investments.”
Truck parking
The truck parking crisis across the nation has been well-documented. The 2019 Jason’s Law Report found that 98% of drivers regularly experience problems finding safe parking. According to the Owner-Operator Independent Drivers Association and the American Trucking Associations, there is only one truck parking space for every 11 truckers nationwide.
Rep. Mike Bost, R-Ill., introduced the Truck Parking Safety Improvement Act in the previous two sessions. The bill would allocate $755 million over three years to the construction of parking spots. According to the bill text, any project funded by the bill cannot include paid parking. All parking under the bill must be publicly accessible and free of charge.
Bost, who is expected to reintroduce the bill, asked Jim Tymon of the American Association of State Highway and Transportation Officials if states would pursue funding for truck parking if Congress created a grant program.
“If there was a grant program for truck parking, states would be interested in that,” Tymon said. “I would say that it’s not just availability of funding on the state DOT side. A lot of the right-of-way that the state DOTs have control of, there is a restriction as to what they can do within that right-of-way, including establishing new rest areas and commercializing them to be able to support truck parking.”
Bost then asked Tymon for additional conversations with his staff to determine what would need to be done to make sure truck parking expansion was possible.
