The Ultimate Guide to AI in Trucking: Empowering Your Fleet for Success

We’ve all heard the buzz about AI in trucking. But let’s be honest: Some promises can feel a little far-fetched. The term AI has been floating around since Dartmouth coined it in 1956, but so far only the big players have been able to afford implementation. Where’s the payoff for a small-to-medium-sized trucking company?

To stay competitive, most smaller trucking companies rely primarily on physical assets including best-performing trucks, well-located warehouses, and experienced people. However, as the workforce ages and infrastructure changes, truckers must find ways to eternalize that expertise and act agilely in increasingly more complex logistics networks.

The good news is that we’re finally at a tipping point. Thanks to more efficient chip manufacturing and cloud data centers, computing power and data storage are affordable, making AI tools accessible. Route planning tools can operate from real-time traffic data, and vehicle telematics can help truckers optimize fuel consumption without buying brand-new fleets.

Experts predict a surge of AI in trucking over the coming decade. The key is understanding trucking AI applications beyond the hype and the tangible benefits they bring.

Let’s examine the biggest challenges trucking companies face today, AI’s benefits and use cases in trucking, and how to get started.

The Biggest Challenges and How AI in Trucking Can Really Help 🤝

Navigating Economic Fluctuations

Logistics has always been subject to economic cycles; when GDP is low, so is demand, and hikes in living costs, inflation, and interest rates impact business flow. More recently, the pandemic and supply chain disruptions, such as droughts in Panama or limited passage via main ocean routes, have made it difficult for trucking companies to predict demand proactively, but they don’t make it impossible.

With advanced demand forecasting, trucking companies can factor in historical, recent, and real-time disruptions to help build realistic projections. AI algorithms, such as machine learning and deep learning, are stepping in to help analyze historical data, telematics, and external trends, and today, tools are available that make this process 95+% accurate.

In times of low demand, truckers can use these quieter spells to optimize their operations. They can pay closer attention to fuel consumption, drive times, and route planning to identify areas for improvement and enhance efficiency. Advanced logistics planning tools can help here, too.

Optimizing Fuel Efficiency

Did you know that the Environmental Protection Agency (EPA) has found that frequent, aggressive driving can significantly reduce fuel economy? Data on speed, acceleration, braking, and idling provide valuable insights into fuel usage. For this reason, tracking these behaviors and introducing personalized driver coaching is crucial to the bottom line.

Trucking companies can develop practical training and recognition programs to encourage fuel-efficient practices by monitoring driver behavior and identifying optimal driving conditions. Sharing this data directly with drivers helps them understand the impact of their actions on fuel consumption and track their progress over time.

Additionally, avoiding areas with regular traffic congestion can save time and fuel. By analyzing GPS and real-time vehicle tracking data, trucking companies can use route optimization software to immediately re-route trucks and select more efficient ways for future trips.

Attracting Top Drivers

A career in truck driving has a poor reputation — it’s physically demanding, keeps you away from your family, and the size and weight of the vehicles can instill fear in new drivers. Human error significantly contributes to road accidents, accounting for nearly 95% of incidents. However, regulations like the mandatory implementation of advanced driver assistance systems (ADAS) and automatic emergency braking (AEB) systems are increasingly making the market safer and more attractive.

Computer vision, combined with sensors like radar and lidar found in the latest ADAS and AEB systems, can help attract top drivers as it opens doors to higher safety, driving efficiency, and work quality. It can accurately identify and track vehicles, pedestrians, and other road users in its radius. It can also communicate with the truck to ensure the driver is alert and even make the executive decision to break or slow down in unsafe situations.

Trucking companies can integrate AI analytics with visual and vehicle data to build driver coaching tools that offer new drivers a sense of personal development. This data also enables the creation of high-definition maps and precise location tracking, which logistics planners can use to optimize routes and improve journey planning to ensure everyone has enough time with their families.

Truck drivers don’t get enough recognition. By rewarding them for their driving excellence, and using the latest tools to make their experience on the roads safe and enjoyable, the industry will improve its reputation as the much-needed and respected role it has in our society.

Embracing Energy Innovation

HDVs are responsible for more than a quarter of the EU’s road transport greenhouse gas (GHG) emissions, and manufacturers must comply with targets for fleet-wide average CO2 emissions starting from 2025.

As of Q1 2024, 14% of fleets operated electric vehicles (EVs) across various regions and fleet sizes, with a substantial increase expected over the next five years. Beyond its positive environmental impact, these trucks bring smart features that benefit logistics companies.

EVs often have set routes of known distances, vehicles that routinely overnight in the same location, and built-in telematics — data sources that fuel many of today’s advanced logistics software. The tools that measure battery performance can also enable remote truck monitoring, providing alerts for maintenance needs or potential issues and promoting proactive vehicle care. In addition, it allows for analyzing driving habits, which can be instrumental in improving energy efficiency and reducing truck wear and tear.

Moreover, by 2030, EVs will likely be cheaper than their diesel counterparts across four out of five listed truck categories.

Prioritizing Parking Solutions

Logistics planners have a lot on their plates. Increased regulatory compliance, navigating local, national, and international laws for customs and trade, and more complex intermodal supply chains require them to file a lot of paperwork. The industry has grown so quickly that even city planners are catching up in building infrastructure like parking spots to accommodate more trucks, and planners need help managing routes to ensure drivers always have a service station with plenty of available space.

Several forms of AI in Trucking can help with this scenario:

Better internal planning tools can reduce logistics planners’ administrative load. Generative AI, for example, is increasingly stepping in to help automate quote, supplier contract, and invoice generation, allowing logistics planners to prioritize strategic decision-making.

Advanced route planning software can help logistics planners meet driver priorities. For instance, if multiple stops are a priority for a particular driver, logistics planners can add this filter to their planning tool to find the best route.

Digital twins-powered scenario planning enables logistics planners to create digital simulations of real-world situations. They can create virtual replicas of roads, parking lots, and expected traffic at set times of day, helping to review the optimal routes, including service stations with plenty of amenities — like truck parking — to meet their drivers’ needs.

As logistics technology advances and data capture on truck status, location, global traffic, regulations, and disruptions becomes increasingly automated in real time, trucking companies can more confidently rely on these tools to maximize their operations. They can accurately predict demand and plan accordingly to ensure all deliveries are met, and drivers are offered healthy schedules. They can implement driver coaching and reward programs to give their team the recognition they deserve, and they can save on fuel consumption with drivers’ expert fuel-saving techniques and advanced route planning.

AI in Trucking: Benefits and Use Cases 🤖

Now that we have reviewed the biggest challenges impacting the trucking industry, let’s move to understand AI in trucking applications beyond the hype and the actual impact that they bring.

Seeing Around the Corner: Computer Vision for Enhanced Driver Awareness (and more)

No one wants Big Brother breathing down their neck and watching every move, but what if you could put eyes on the back of your own head instead?

Imagine having a virtual co-pilot constantly watching out for you, helping you improve your ability to navigate turns, manage blind spots, and operate in different weather conditions.

Cameras mounted throughout the vehicle, paired with AI algorithms, offer truckers a new power: computer vision. It can analyze driver behavior in real time and provide immediate feedback through audio alerts or on-screen notifications.

As well as supporting drivers on the go, evaluating historical driver behavior data can help truckers identify patterns and areas where specific additional training is needed. This allows for targeted coaching, preventing accidents, and keeping drivers (and everyone on the road) safe.

Plus, as autonomous driving matures, this same trucking AI will be crucial for its safe implementation — so getting familiar with it now only sets you ahead.

Predicting the Future, One Mile at a Time: ML & Predictive Analytics

We can’t control the market, but we can prepare for it. Machine Learning (ML) and predictive analytics are a crystal ball for truckers. These tools help to predict maintenance needs before breakdowns occur, forecast operational costs, and even estimate spot rates.

With access to vehicle data — such as engine speed, RPM, oil pressure, coolant temperature, and fuel consumption; brake pad wear, pressure, and temperature; tire inflation — and GPS data, logistics planning teams can reveal abnormal fuel consumption patterns, indicating potential route inefficiencies or mechanical problems.

Load weight and type, past repair records, part replacements, and driver logs of unusual noises, handling, or vibrations are crucial parameters for algorithms to pinpoint the cause of unusual vehicle performance. But don’t forget the impact weather and traffic conditions have on trucks’ handling and fuel.

While it might sound like a lot, you don’t need a team of data scientists or a mountain of cash to get started; the right data and algorithms are readily available for truckers to leverage. All you need is the blueprint, and you can make informed decisions, optimize routes, and maximize profits.