How AI Fleet Management Will Shape the Future of Transportation
There are many opinions about how Artificial Intelligence (AI) is going to change the world with expectations about its capabilities for now and in the future. AI simply refers to intelligence displayed by machines in contrast to that displayed by humans. Although humans are intelligent, they cannot be programmed to exceed their current capabilities in the same way a machine can. This has led to the creation of smart machines that handle tasks otherwise difficult for humans to handle efficiently.
Artificial intelligence is gradually becoming a constant presence in many technological applications. From apps and websites that show accurate user recommendations to gaming predictions, it is changing user experience in many fields.
AI and transportation is one of the areas that is growing. The growing need to put driver safety first without compromising cost or efficiency has led to the adoption of smart fleet management systems, optimizing fleet performance.
For the average driver, the presence of AI can be felt heavily in the use of smartphones and telematics devices that recommend the best routes to take in traffic. This used to be a herculean task marked by paper maps and listening to radio broadcasts of traffic routes; today, we have complex traffic apps that combine GPS and artificial intelligence to make drivers’ lives easier.
Fleets benefit from powerful AI-based applications that handle anything from route recommendation to road risk data analysis and even driver coaching. It provides the accuracy, efficiency, convenience, and ease that earlier technology failed to provide. As a result, it is becoming safer to transport goods and services.
What is AI Fleet Management?
AI fleet management is the use of artificial intelligence-based technology to manage fleet operations. In a constantly changing world, it streamlines the work of any fleet manager by gradually eliminating human error from the transport process.
AI-based recommendations ensure that fleet drivers, managers, and mechanics can make better decisions that improve the long-term performance of the fleet. It also serves as assistive technology, ensuring that drivers retain autonomy during each transport cycle. Here are some key aspects of fleet management that AI can optimize:
Real-time Fleet Safety Analytics
Collecting data is a key element of any operational process because without analyzing past data, you cannot make informed decisions. With historical insights to inform millions of data points analyzed in real-time, the result is the prioritization opportunities and risks so that fleet managers and drivers can determine the best course of action to take in potentially problematic situations.
AI systems can be used to collect data for fleet management predictive analytics; data such as traffic and road conditions, environmental hazards, real-time weather, and mechanical faults can be used to predict incoming risk. This allows fleet managers to make better routes, schedules, maintenance delivery, and dispatch arrangements that improve fleet outcomes and activities.
Finally, with AI-based analytics, drivers no longer need to go in blind and can stay prepared for any unexpected events.
Better Repair and Maintenance Decisions
In May 2019, autonomous driving car brand Tesla made headlines after debuting AI-based technology that allows Tesla vehicles to diagnose their faults accurately. Although this technology has existed for some time and has been seen in several modern cars, artificial intelligence is providing a more accurate self-diagnostics as well as solutions to faults. Recently, Tesla announced the use of in-car camera when in autopilot for driver monitoring; continuing the evolution and power of AI in transportation.
AI ensures that potential faults can be predicted before they even happen. For example, a normal vehicle with a diagnostics system would most likely signal an engine problem when it has already occurred. On the other hand, AI-based Internet of Things (IoT), data analytics and predictive maintenance, can lead to fault detection long before it eventually happens. According to a study by McKinsey, predictive maintenance will reduce costs by 10-40%, downtime by 50% and capital investment by 3-5%.
Predictive maintenance gives managers and their mechanics more than enough time for repairs which could potentially prevent accidents. More importantly, AI can recommend the most efficient and cost-effective solutions to mechanical faults. This has two major benefits:
- It saves mechanics’ time usually spent on diagnostics.
- It gives managers a clearer picture of the state of their fleets at all times. This could mean that service managers could save a lot of routine maintenance costs by carrying out repairs only when the AI systems show potential faults.
One major problem with fleet operations, especially in large fleets, is the number of moving parts within the system that need to be accessed. Several departments need a continuous inflow of information that needs to be in sync with all other departmental operations. Although a skilled workforce can make this happen, it is time and labor-intensive.
An AI system could simplify the process by seamlessly integrating every department on a single platform and feeding them information simultaneously. Service managers can save time and costs on planning, maintenance and monitoring operations since all data on those operations are fully accessible. This ensures that all personnel across the different departments have access to the data that helps them make informed decisions. It also leads to a more cohesive fleet, since every department automatically works in sync with the others.
Simpler Recruitment Process
According to a report by the U.S. Bureau of Labor Statistics. The need for automotive and diesel technicians is expected to grow by up to 5% by 2028. The American Trucking Association estimates that there will be a shortfall of up to 175,000 truck drivers by 2026.
As the older generation drivers and technicians retire, there is a need for younger tech-savvy replacements; however, this presents a problem with onboarding and training. AI can simplify the onboarding process by capturing the specialized skills of these workers before they retire.
This is especially great for technicians with unique ways of carrying out their tasks. AI can also recommend the most qualified drivers that suit the needs of the company from a pool of thousands of applicants, reducing the strain on recruiters.
How is AI Integrated with Fleet Management?
AI-integrated software is usually a sophisticated system made up of several devices and applications such as Internet of Things, predictive data analysis and machine learning systems, HD cameras and sensors, communication and display systems, and WiFi.
For example, AI-based fleet management platform Driveri, currently deployed in fleets across the country is a combination of all of these components. There are also many other AI-integrated fleet management systems with one or more of these components.
Before understanding how each of these parts combines to create a fleet management powerhouse, it is important to know what each one does.
Internet of Things (IoT)
The Internet of Things refers to a network of actuators and sensors continuously collecting data from their environment. In fleet management, IoT ensures that enough data is captured for analysis while promoting the seamless sharing of information between all stakeholders on the supply chain such as retailers and manufacturers.
IoT for fleet management works through the use of 3 main technologies:
- Wireless Communication (4G, Bluetooth< WiFi ) convey relevant information
- Global Positioning System (GPS) for accurate real-time location tracking
- Onboard Diagnostics (such as OBDII and J1939) scanners for self-diagnostics and reporting
Machine learning technology allows fleets to learn from data collected over time and make managed adjustments based on that data. The result is the creation of smart systems in which AI can learn decision making capabilities that enable more effective handling of practical situations.
Cameras ensure that video data can be captured, analyzed and accessed at any time leading to a better study of driver behavior, road conditions or hazards.
An AI system with all of the above components will be capable of performing the following tasks:
- Collecting accurate road data and transmitting it to other devices
- Passing information across every arm of the supply chain
- Analyzing data in real-time and advising the driver on the best course of action
- Detecting distracted or drowsy driving behaviors in drivers before they lead to accidents
- Capturing full video footage of accidents from different external vehicle angles
- Running Self-diagnostics and recommending solutions through predictive maintenance
This is significant because it creates a future of fleet management in which human error is reduced in different aspects of the transport cycle. This, in turn, could lead to better outcomes and cost savings.
How AI Fleet Management Will Shape the Future of Transportation
Today, the automotive vehicle industry is faced with several problems that affect fleet activities and profitability. If properly applied, AI can potentially solve these problems and create a better future for transportation.
These problems include:
- Resource prioritization and efficacy
- Risky driving behaviors that lead to accidents
- Road risks
- Data collection and analysis
- Cost containment
Risky road behaviors such as distracted and drowsy driving are often accompanied by signs that drivers are told to look out for. These signs include:
- Constant blinking
- Missing turns or exits
- Drifting out of their lane
- Slower reaction times
- Picking up a cellphone
Ordinarily, managers rely on their drivers to avoid these signs and have no way of knowing if a driver had been texting while driving or nodding off at the wheel. Artificial intelligence systems could be trained to detect head turns, missed exits, yawning and blinking frequencies and other signs of risky behavior. These signals can be broadcast to fleet managers in real-time, allowing them to take corrective measures.
Changing road conditions present another challenge for managers because they are difficult to detect without proper technological tools. These conditions present a huge risk evident in the 42,000 deaths they cause annually. AI-based predictive technology can map reduce the risk associated with this problem by studying and mapping out routes while also drawing from data gathered by other vehicles. It can also be trained to make smart predictions about the weather and detect environmental changes such as fog before a driver reaches that point.
A good example of this type of risk assessment through data collection is Netradyne, whose product has already mapped out over 1 million unique miles of US roads. In the future, an extensive database of road conditions will be essential for promoting safety.
As discussed above, AI-based systems can help managers save costs through fuel economy and predictive maintenance. No matter what type of fleet you operate, from trucks to trains, city buses, or taxis, fuel and maintenance are major contributors to operational costs. Vehicles break down and fuel prices increase without warning, leading to more expenditure. The elimination of routine maintenance schedules using IoT self-diagnostics and fuel control could be the key to better cost containment in the future of fleet management.
Which is the Best Fleet Management Software?
Fortunately, AI fleet management software has gone from being dreamy concepts to reality. Several technology companies have created software that improves driver safety and fleet performance without compromising cost or efficiency.
In our research, we looked at the key components that made each one stand out.
After analyzing their mapping capabilities, technological range, as well as sensor technologies, Driveri emerged as the best fleet management software due to the following features:
- An artificial intelligence DriverAlert system that captures and analyze every minute of driving time.
- Real-time analysis and feedback enabled by powerful Edge Computing and Machine Learning capabilities.
- Internal lens that detects drowsy or distracted driving behaviors such as yawning that alerts managers in real-time enabling quick action to mitigate risk.
- Advanced data analysis system with more than 1 million unique miles of US roads analyzed and stored in an accessible database
- Forward, side, and interior HD cameras that capture high-quality videos in real-time
- Access to up to 100 hours of video playback for records and as evidence in the case of accidents in which there are legal consequences
- 4G LTE / WiFi / BT connection within fleets, to send and receive data, view video and analyze risky behaviors
- A mobile application for real-time feedback
- Single module installation system for quick and easy installation
The future of fleet management looks more promising than ever due to the exciting applications of AI in transportation. Unpredictable road conditions, operational costs, and driver retention problems could easily become obsolete as fleets move to AI-based systems. Every stakeholder stands to benefit a lot from the efficiency and reliability of this technology because of a reduction in costs, accidents, driver turnover, and other problems which could reflect on the pricing of fleet services. It could also ensure that other road users remain safe.
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