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.
Following are two articles regarding the $100M verdict against Werner Enterprises. If you or one of your commercial trucks is involved in an accident, please engage help. Get someone you trust, and of course I am glad to help – JoelBeal@JBATelematics.com. No mater what you think about the facts of the case, preparedness is key.
Texas Supreme Court hears Werner’s controversial nuclear verdict case
Tyson Fisher
The Texas Supreme Court heard oral arguments in a controversial nuclear verdict case involving Werner Enterprises that has a wide range of stakeholders calling for tort reform.
On Tuesday, Dec. 3, attorneys for Werner, a truck driver and victims of a fatal crash stated their case in front of the Texas Supreme Court. Last year, the 14th Court of Appeals in Texas affirmed a lower court’s nine-figure verdict finding the trucking company and its driver liable for a 2014 crash.
Werner’s case has left trucking and legal stakeholders scratching their heads. Despite all the facts suggesting the truck driver did nothing wrong, plaintiffs’ attorneys convinced a jury otherwise. At the center of oral arguments on Tuesday was the level of duty a truck driver owes to motorists traveling on the other direction of a divided highway.
Attorneys also argued whether a legal doctrine known as the Admission Rule should be formally adopted in Texas. That rule could prevent a trucking company’s practices, policies and overarching operations and culture from being used in certain crash lawsuits.
2014 crash
The Texas Supreme Court case stems from a lawsuit filed by the family of Zachary Blake and Brianna Blake. Zachary, who was 7 years old, was killed in the crash with a Werner truck. Brianna, who was 12, was rendered a quadriplegic.
On Dec. 30, 2014, the Blakes were traveling east on Interstate 20 in Texas in a pickup truck driven by Zaragoza “Trey” Salinas. Weather conditions were icy at the time. Salinas lost control of the vehicle going 50-60 mph and careened across a grass median, entering the westbound lanes.
At this time, Shiraz Ali was driving a truck for Werner going west on I-20. He was driving below the speed limit when the pickup truck began to spin. Also present in the Werner truck was Jeff Ackerman, a Werner driver-trainer. According to the appellate brief, Ali reacted within half a second, hitting the brakes.
Even the Blakes’ expert witness conceded that Ali’s reaction was “very quick” and “appropriate to the conditions.”
In addition to Salinas making statements suggesting guilt and responsibility, a Texas Department of Public Safety trooper defended Ali’s actions. Trooper Villareal, a 17-year veteran who investigated the accident, concluded “this is truly an accident,” Ali “didn’t do anything wrong” and there was nothing Ali “could have done to avoid the collision.” A higher-ranking trooper who approved the report concurred.
More details of the crash can be found here.
Werner on the hook for more than $100M
During the trial, plaintiffs were allowed to present evidence that had no direct proximate effect on the crash in order to persuade the jury that Werner’s culture and policy had caused it.
This evidence included:
- Details about Werner’s training and supervision of Ali
- Werner’s lack of a “command center” for weather monitoring
- Werner’s handling of crash investigations
- Claims of Werner’s driving school director being unqualified
- Werner’s failure to require a CB radio
- Werner’s failure to require an outside temperature gauge
Werner argued, however, that this evidence had nothing to do with the crash in question and that relevant evidence insufficiently supported a finding of negligence.
In 2018, a jury found Werner 70% liable, Ali 14% liable and Salinas 16% liable. After calculating all damages, the jury award for the plaintiffs was in excess of $100 million. In May 2023, a state appellate court affirmed the ruling.
Duty of care
A question critical to the case in front of the Texas Supreme Court is where to draw the line when determining a truck driver’s duty of care to other road users.
Representing Werner and Ali, attorney Thomas Wright argued that drivers should not have to anticipate someone coming into their lane from the opposite side of an interstate with a 30-plus-foot median between them. Although motorists can foresee another vehicle encroaching into their lane in icy conditions when going the same direction, it is unreasonable to believe a car coming from the other direction is also foreseeable.
Wright conceded that a trucker owes a duty of care to react appropriately once a wayward vehicle enters his or her lane. In this case, both sides acknowledged that Ali reacted quickly and safely.
Representing the Blakes, attorney Darrin Walker argued that a cross-median crash was foreseeable considering the weather conditions. Supporting that argument, Walker pointed to the CDL manual, which states that drivers should slow down to about 15 mph during inclement weather. Ali was traveling at about 43 mph.
“The purpose of this rule is mainly to protect other motorists, because passenger vehicles are even more likely than an 18-wheeler to lose control on icy road conditions, and we don’t want an 18-wheeler going highway speeds on icy roads when another motorist loses control in front of it,” Walker said.
Admission rule
Werner is asking the Texas Supreme Court to adopt what is called the Admission Rule, which could determine the fate of nuclear verdicts like Werner’s.
The company has argued that since it accepted responsibility by admitting Ali was in the course and scope of employment, plaintiffs cannot pursue “derivative theories of negligence.” Under the Admission Rule, once an employer establishes liability, “evidence of the employer’s hiring, training or supervision practices becomes inadmissible as irrelevant and likely to prejudice the jury,” according to the law firm Lewis Brisbois.
In this case, the appellate and district courts rejected the Admission Rule by allowing a variety of evidence dealing with Werner’s companywide policies and training. This in turn allowed plaintiff attorneys to use a tactic known as reptile theory, which evokes emotions of fear and anger in jurors to encourage nuclear verdicts. As Lewis Brisbois put it, “This company was so terrible you should punish them, regardless of whether any of our evidence showed the company or its driver’s actions actually caused the subject crash.”
The Admission Rule has been adopted in several states. In Texas, courts are split on the rule, with some adopting it and others – like the 14th Court of Appeals – rejecting it.
Werner is asking the Texas Supreme Court to cement the Admission Rule into state legal precedent.
During oral arguments, Werner said that a trucker’s entire day is on trial. Rather than focusing on what a driver did immediately before and during a crash, juries are considering irrelevant information about the company.
(H3) What should have been a quick trial turned into a six-week circus dissecting every decision made by Werner.
“We wanted to make this case about everything except for the three-second accident sequence,” Wright said. “It was all about trying the company.”
Walker argued that the Admission Rule disincentivizes employers from training and supervising their employees. He said that the negligence of the employer and the negligence of the employee both contribute to an injury.
“I think it’s unfair to hide that from the jury and then foist liability that would ordinarily fall on the employer onto the untrained employee,” Walker said.
The following groups have filed amicus briefs in the case, all of them supporting Werner’s bid for the Texas Supreme Court to adopt the Admission Rule:
- Acuity Insurance
- American Trucking Associations
- Schneider National Carriers
- Texas Association of Defense Counsel
- Texas Civil Justice League
- Texans for Lawsuit Reform
- Texas Trucking Association
- Trucking Industry Defense Association
- U.S. Chamber of Commerce
Texas Supreme Court Hears Werner’s $100M Verdict Appeal
Attorneys Spar Over Jury Award in 2014 Fatal Accident
Eric Miller
Attorneys for Werner Enterprises have asked the Texas Supreme Court to reverse a $100 million verdict against the company in a case involving a fatal accident. The case centers on a 2014 crash in which a pickup truck crossed a median and struck a Werner truck traveling in the opposite direction on a snowy highway.
Werner has during trials in lower courts steadfastly maintained that its driver could not have avoided the crash and did not share any of the fault. However, a Texas jury in a 2018 trial was instructed that it could apply a “proximate cause” legal standard in the case. A proximate cause is defined as a partial cause that was a substantial factor in bringing about an injury, and without which such injury would not have occurred. The court ruled that there was sufficient evidence to support a finding that insufficient training and supervision for the Werner driver behind the wheel at the time “proximately caused the collision.” An appeals court upheld that ruling, prompting Werner to take its case to the state’s highest court.
In oral arguments on Dec. 3, Werner attorney Thomas Wright told the court that Werner driver Shiraz Ali could not have foreseen that a pickup truck would suddenly cross an icy divided highway and strike the carrier’s truck. He further argued that the appeals court deviated from legal precedent in upholding the lower court ruling, and “disregarded all the cases from the Texas state courts and around the country’s lower courts.” Wright added that the appeals court, “for the first time has held that a driver in his own lane under control of his vehicle is liable when somebody on the opposite side of an interstate highway loses control, spins out, crosses over a 30-foot median plus the shoulders, runs into the driver in his own lane with no time to react.”
Countering Wright’s stance, Supreme Court Justice Jane Bland asked, “Is it reasonably foreseeable that on an icy day that there may be lane intrusion by someone if not this exact scenario traveling across a highway median, but somewhat skidding and entering your lane?
Wright replied, “It’s certainly foreseeable somebody in the next lane could come into your lane, but you cannot realistically travel down the highway in any kind of weather if you have to anticipate that somebody without warning leave the interstate lane 30 feet across. That’s why they built the interstate highway system, to keep these cars separated. What’s the point of having an interstate?
Wright continued, “We believe that the plaintiff’s way to decide the case is to say he had no duty to people across the road. …If I thought that driving down the [divided] highway afraid that somebody would lose control, come across and hit me and it would be my fault, I might just stay home.”
Arguing on behalf of the plaintiffs in the case, attorney Darrin Walker suggested that the Werner driver should either have been “driving very slow or pulling over during icy conditions, and that at the time of the collision was driving too fast.”
“The road was like a skating rink,” Walker said. “It was covered in ice, cars were going off the road left and right, and there were two cross-median collisions in this case.” He acknowledged, however, that the Werner driver did not encounter those crashes.
“In some cases there may be some circumstances whereas a matter of law a cross-median collision is not foreseeable and the driver does not have to take any precautions,” Walker said, “but in this case the evidence was clear that a cross-median collision was foreseeable.”
The crash resulted in the death of 7-year-old Zachery Blake, catastrophic permanent injuries to 12-year-old Brianna Blake, significant injuries to 14-year-old Nathan Blake and the Blake children’s mother, Jennifer Blake.
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Werner objected to the 2018 jury’s finding that the driver and company were negligent, and also to the court judge’s decision to allow certain evidence in the case. Additionally, Werner objected to the jury’s award of future medical care expenses for the plaintiffs.
Werner maintained that at the time of the accident, Ali was “proceeding in his lane, in control of a Werner tractor-trailer and well below the speed limit, when the [plaintiff’s] vehicle suddenly careened into his path, leaving him no time to avoid a collision.”
Despite this, the lower court jury found both Werner and Ali liable and assessed the award, according to court documents.
Trucking industry groups have come forward to support Werner in legal briefs filed with the Texas Supreme Court. They include American Trucking Associations, the Texas Trucking Association, the U.S. Chamber of Commerce and the Texas Civil Justice League.
“In recent years, trucking companies have faced a growing trend of so-called ‘nuclear verdicts’ in highway accident litigation — verdicts that are not only shockingly large, but which, like the verdict here, are fundamentally unfair in that they are untethered to the realities of the case,” ATA wrote in a brief filed with the high court last month.
Werner ranks No. 16 on the Transport Topics Top 100 list of the largest for-hire carriers in North America and No. 4 on the truckload/dedicated sector list. It also ranks No. 30 on the TT100 of the largest logistics companies in North America.
Bahar Gholipour
CHICAGO — The familiar tune of the Bee Gees song “Stayin’ Alive” has been used for medical training for quite a few years now: It has the right beat — not to mention, the perfect title — for providing CPR’s chest compressions at the right pace to revive a patient.
The 1977 hit song has a rhythm of 103 beats per minute (bpm), which is close to the recommended rate of at least 100 chest compressions per 60 seconds that should be delivered during CPR. Plus, the song is well known enough to be useful in teaching the general public to effectively perform the lifesaving maneuver.
In fact, the American Heart Association (AHA) officially recommends that if you see someone collapse, you should “call 9-1-1 and push hard and fast in the center of the chest to the beat of the classic disco song “Stayin’ Alive.” The AHA has gone as far as depicting the act in an educational music video featuring comedian and physician Ken Jeong.
But although the song seems to be the perfect soundtrack for CPR, it does have some drawbacks. Namely, it is an American song, so not everyone around the world is familiar with it. However, there are other songs with the right beat that might do just as well, according to researchers in Japan.
In a new study, Dr. Yoshihiro Yamahata, of Kyoto Prefectural University of Medicine, and his colleagues tried using new songs to instruct a group of newly hired nurses to perform CPR. The researchers presented their findings this week at the AHA meeting in Chicago.
“The quality of CPR is the key to [helping] the victim recover,” Yamahata said. “Our solution to master adequate CPR skills is to put the educational words on several famous songs with 112 bpm and 8 beats” per measure, he said.
Receiving high-quality CPR can double, or even triple, a person’s chance of surviving cardiac arrest outside the hospital, according to the AHA. For effective CPR, the AHA recommends delivering at least 100 chest compressions per minute, making each compression at least 2 inches (5 centimeters) deep and ensuring full “recoil,” meaning the chest wall returns to its original position between each compression.
