Angel Coker Jones
Driver- and road-facing cameras, speed limiters, collision mitigation systems, lane departure warning systems, lane-keeping/lane centering systems, telematics and ELDs. These technologies are all measures many fleets have implemented to increase safety, but a large portion of the truck driver population would disagree that these are beneficial in reducing safety risks.
In fact, in CCJ’s 2024 What Drivers Want survey, more drivers said these systems reduce rather than improve driver safety.
When asked which technologies have the greatest impact on reducing driver safety, 67% said speed limiters, while 54% said driver-facing cameras, and 46% said ELDs. When asked which technologies have the greatest impact on improving driver safety, 61% said forward-facing cameras, while 31% said collision mitigation and lane departure warning systems.
Most drivers have an aversion to any technology in the cab at all with many comments from drivers sharing similar sentiments: “Technology has ruined the industry for drivers;” “Just let me drive the truck, don’t need all of that crap;” “All of this stuff listed is a distraction.”
“We are supposed to be professionals and all the intrusive apparatus that is listed takes the mental aptitude away from the driver,” said Jack Mancini, a driver at Latigo Trucking out of South Carolina.
This attitude can often lead to drivers attempting to disable technology.
“When drivers hate the intervention, they tend to ignore it, or they tend to tamper with the system, both of which are bad,” said Stefan Heck, founder and CEO of AI-powered safety platform Nauto.
It’s all about buy-in, said Dudy Markus, vice president of aftermarket at Cipia, a provider of computer vision AI for driver and cabin sensing, including ADAS and driver fatigue and distraction detection.
He said the key to adoption lies in addressing both practical and psychological barriers, ensuring that safety systems not only perform effectively but also resonate with the priorities of their users.
That was the case with forward-facing cameras.
As previously noted, a large portion of drivers (61%) consider forward-facing cameras to have the greatest impact on improving driver safety, though some noted in comments that it doesn’t actually improve safety so much as it exonerates drivers in the event of a lawsuit. This while 69% of respondents said driver-facing cameras are invasive, and 4% said there should be an option for drivers to disable them in certain situations.
Mark Murrell, president of Carriers Edge, a provider of online driver training for the trucking industry, previously told the CCJ that fleets once experienced the same criticism of road-facing cameras as they are now with inward-facing cameras, but now it has driver buy-in because perception has shifted.
“Drivers didn’t want anybody watching the road or watching what was happening, but then, all of a sudden, we started seeing all of these dashcam videos showing up on YouTube, and it became a way for drivers to share the crazy stuff they were dealing with on a day-to-day basis,” Murrell said. “Then we started seeing more and more stories about how the camera footage had exonerated drivers in crashes, and it was becoming kind of a safeguard and all of a sudden the driver perspective changed completely.”
It’s the same for all technology, Markus said.
He said for safety systems to gain traction, the perceived value must go beyond life-saving benefits. Though, in theory, that should be enough, he said in practice, adoption increases when these systems align with broader incentives.
For fleets, that looks like cost savings, efficiency improvements, regulatory compliance and risk mitigation. For drivers, that looks like bonus checks because of these results.
Markus said once drivers see the benefits and develop confidence in the system, they’re much less likely to attempt to disable it.
Fleets may experience drivers tampering with technology like cameras, from tossing an article of clothing over the lens to “accidentally” breaking the instrument. When it comes to other technology that drivers don’t perceive as an invasion of privacy, they may attempt to deactivate it simply because it can be annoying.
“Poor solutions with high false positives lead to alert fatigue, which is one of the primary reasons drivers attempt to override safety systems,” Markus said. “Systems must demonstrate their value in enhancing driver safety by providing accurate, timely feedback.”
In instances of unreliable technology, Markus said disabling it might benefit safety by reducing driver distraction, but disabling a quality system that prevents unsafe behaviors like unbuckled seatbelts or texting while driving can have life-threatening consequences.
“Loud alerts for objects on the passenger side can scare drivers into jerking the wheel. And following distance buzzers dramatically increase anxiety,” Terrence Hyden, a driver out of Orlando, said in response to the What Drivers Want survey.
Markus said if the initial system configuration properly accounts for varying driving situations, individual driver adjustments are typically minimal, and fleets get more buy-in from drivers.
He said a comprehensive safety system goes well beyond a simple dashboard camera.
“It should be automotive-grade and adaptive per fleet-specific needs, combining ADAS with DMS (driver distraction detection) to provide both real-time alerts and driver analytics that support ongoing skill improvement,” Markus said. “When all these elements are in place, instances of drivers disabling safety technology become negligible.”
Any driver hired could represent a multi-million-dollar negligent hiring lawsuit. You can reduce that risk by using a hiring checklist to avoid pitfalls in five key areas.
Mark Schedler
Any driver hired could represent a multi-million-dollar negligent hiring lawsuit. You can reduce that risk by using a hiring checklist to avoid pitfalls in five key areas.
- Driver application
The regulated driver application is an investigative roadmap for new hires.
Common application errors include:
- The application is missing, partially completed, or not compliant with 391.21. This happens most often with drivers who are:
- Long tenured,
- Part of an acquisition,
- Leased from a temporary agency,
- Office employees who fill in occasionally, or
- Transitioned from a non-regulated role like a warehouse person.
- The driver did not sign the application before the first dispatch.
- Omissions of critical information like regulated employers or prior residences in the prior three years.
- Driver background investigation/Safety performance history
Avoid these prior employer investigation mistakes:
- Failure to inform a driver of their due process rights to review information found in the new-hire screening process. Notification up front is critical if you receive adverse information and choose not to hire the person.
- Failure to question the driver about greater than 30-day employment gaps. These periods could be due to alleged self-employment, a license suspension, or incarceration.
- Not obtaining the safety performance history within 30 days after the hire date.
- Making only one attempt to verify prior employer dates of employment and accidents.
- Disregarding the driver having several employers in 3 years during a driver shortage.
- Motor vehicle records (MVRs)
An expert should review MVRs. The person must know the state codes and the differences between various states’ MVRs. If they are not knowledgeable, they may overlook the following items:
- Improper licensing for the driver’s assigned vehicle or operation, such as:
- Intrastate restriction for an interstate driver,
- Wrong license class,
- Missing endorsement, or
- Restricted/suspended/revoked license.
- Failure to transfer the license to a new state of residency within 30 days.
- Medical certification
Common mistakes when documenting a driver’s medical certification include:
- Not verifying the medical examiner’s listing on the National Registry for new hires when a current medical card is accepted, as well as after each exam (CDL and non-CDL).
- Failing to request a CDL MVR at the time of hire or within 15 days of each DOT exam.
- Incorrect CDL driver self-certification for the type of driving and medical certification. For example, the MVR may show as “Excepted Interstate,” but the driver is not exempt from medical certification requirements.
- Road test
Road tests are a carrier’s seal of approval that a driver can operate their commercial motor vehicle (CMV) safely. Errors or omissions when assessing a potential driver’s skills include:
- No remedial training to correct skill deficiencies noted during the test.
- A missing road test certificate and/or evaluation sheet.
- Tests are part of, not before, the first dispatch, such as while delivering loads.
- The test was not in a vehicle like the one the driver was assigned, such as testing in a straight truck when driving a combination tractor-trailer is part of the driver’s role.
- Inconsistent enforcement of testing standards between applicants.
In closing, reduce the risk of negligent hiring claims by using a hiring checklist for every driver who will operate a CMV for your company.
If you are only using camera footage to exonerate drivers and want to be defendable in the face of costly litigation, use these four steps to build a best-in-class safety program.
Mark Schedler
Excessive verdicts in the United States have spread like wildfire on a hot windy day. This trend has put any fleet that does not use best-in-class safety practices in the path of potential devastation.
According to the American Transportation Research Institute (ATRI), their 2020 study “Understanding the Impact of Nuclear Verdicts on the Trucking Industry” found:
- From 2015-2019, there were nearly 300 cases over $1million; and
- From 2010-2018, the number of verdicts over $10 million nearly doubled and the dollar amount of awards grew 51.7% annually.
If you are only using camera footage to exonerate drivers and want to be defendable in the face of costly litigation, use these four steps to build a best-in-class safety program.
- Obtain leadership support.
Leadership’s attitudes, values, and beliefs drive a company’s culture. Collaboration with the executive team is imperative to create a proactive safety program. Leaders must uphold policies and best practices that keep safe, all employees, including construction equipment transport drivers, and the public.
From the 2024 J. J. Keller & Associates, Inc. Customer and Market Insights Fleet Manager survey, 51 percent of fleet managers indicated that the most significant determinant of running a safe operation was that their leadership consistently shows that safety is important.
- Develop and enforce policies and procedures that exceed regulations.
Another significant finding in the 2020 ATRI study was that pre-crash actions by carriers are critical. Plaintiff and carrier defense attorneys agreed that “crash avoidance is EVERYTHING.” They also agreed that carriers must always follow policies and procedures and should exceed the regulations.
Policies and procedures are the safety management controls that when consistently enforced, guide your team to stay compliant, maintain company safety standards, and drive improvement.
- Use video in a corrective action training (CAT) program.
A plaintiff’s attorney in post-crash litigation can claim negligence if there is any failure to follow policies, procedures, or reasonable practices that find, coach, and remediate high-risk behavior.
Timely detection and correction of unsafe behavior through video-based coaching:
- Avoids crashes and violations,
- Reduces potential liability, and
- Improves retention.
A well-designed CAT program will also align with state labor laws, contracts, and any union agreements.
- Share safety improvements.
To obtain an insurance policy renewal at the lowest possible premium, a carrier must be able to share with an underwriter:
- The level of risk regarding recent crash and loss experience,
- The root cause of severe crashes and high-frequency minor crashes, and
- The actions to address those root causes and to improve the safety program.
Drivers are also stakeholders in your company’s safety efforts. Share safety successes with the entire team to build momentum and transform the culture.
In closing, a best-in-class fleet safety program driven by video-based coaching can protect your business against excessive verdicts and improve hiring and retention.
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.
Mark Murrell
The human toll of a collision should never be underestimated – even if there are no physical injuries, the mental stress can be enormous. However, during that experience, drivers still have responsibilities and need to follow some best practices when it comes to accident scene reporting. One of the ways you can help is by making sure they’re clear on what they need to do after you’ve established that they are safe.
Some of those driver responsibilities include stopping and securing the cargo and reporting the incident to the authorities. However, one of the things that sometimes gets missed is how much a driver should document for the company’s own files (and the insurer’s) so that the event can be understood by everyone who gets involved later. With that in mind, here is a documentation cheat sheet you can use when developing a response plan for your drivers to follow.
Note: This covers a small (but extremely important) requirement when a crash occurs, but it shouldn’t take priority over other obligations the safety manager or crash response team has – including finding out if the driver is okay, notifying loved ones, and so on. Consult with your executive team or legal counsel for a fuller view of these responsibilities.
Reinforce with your drivers that they should record:
Basic information
This may seem too basic to be needed, but remember that the event will be pieced together later on by people who were not there, so having your driver get even the most basic information down can be crucial:
- Driver information should include their name, address, phone number, date of birth and license number, expiration date, and state or province of issue.
- Carrier information should include their DOT or CVOR number, insurance policy details, company name, address, and phone number.
- Vehicle info includes the year, make and model, color, unit numbers for the tractor and the trailer, and plate numbers.
- And don’t forget to include the time and location of the event!
Incident details
A standard accident report will ask for information like the make, model, and color of the other vehicles involved, as well as personal information about the other drivers and any passengers. It will also help if your driver can write down, while the memory is still fresh, an account of vehicle movements during the incident, including direction, points of impact, traffic signals, and vehicle movements such as making a turn, backing up, skidding or weaving, and more. Note: it will also be important to detail your driver’s own status, including on-duty status and driving hours, distance traveled on the current trip, speed at the time of the incident, and any warning signals they gave or witnessed, such as brakes, indicator lights, horn, etc.
External factors
People looking at the incident much later on (especially insurers) will be keen to know about the road conditions at the time of the incident. Be sure your drivers take note of its physical condition, like how wet or slippery it is, the presence of debris or other obstacles, its grade, curve, and whether the road has potholes or cracks. They should also take note of the traffic conditions – were people trying to merge, was there a railway crossing, was traffic heavy or light, and what was the posted speed limit? Make sure they describe the weather conditions, including how sunny or dark it was, whether there was fog, sleet, or other precipitation, and, in case of darkness, what the road lighting was like.
Legal and other players
While the police may be busy collecting information themselves, it’s important for the driver to take down details about the authorities involved—including badge numbers, who contacted them, the agency the police belong to, whether anyone was charged, and what the charge was (as well as arrests, if any). There will also likely be towing and cleanup vehicles present – make sure to keep detailed notes on these! Unscrupulous towing companies will sometimes exaggerate the number of vehicles present or the time spent, so making clear notes about this can help later on if there is a dispute about exaggerated fees.
Photograph…everything?
The point of all of this is that more information is better when it comes to figuring out what happened. But even if everything else is written down in great detail, there may be some critical scene information that’s not on this list. Encourage your drivers to take photos of the scene (respectfully, of course). Even just a panoramic shot of everything can sometimes reveal details that might have otherwise been missed or forgotten.
Again, this is not to suggest that worrying about documenting the scene of a crash should take precedence over looking out for your driver’s well-being. But navigating a crash incident is a bit of a long game—you’ll be dealing with stakeholders sometimes for years after the fact, so if you can help your drivers remember everything they need to do once they are safe, you’ll be able to deploy resources more effectively when you’ve got the information you need.
