The fleet industry has faced challenges related to operational inefficacies, theft, fleet maintenance since time immemorial. Today AI is helping to solve these and other persistent problems of the industry. Is it possible to eliminate these challenges completely? Perhaps not, but with AI-powered solutions, it is possible to face these with greater efficiency.
Artificial intelligence (AI) has woven its way into many aspects of our lives. Just a few years ago, most of us depended on paper maps for direction. We struggled while searching the internet to find a store or restaurants that we were looking for. Now we simply whip out our phones or speak into our smart speakers to instantly get personalized recommendations and insightful advice.
Today, AI-based recommendation engines are delivering the same level of accessibility, accuracy, and speed to the transportation, logistics, and mobility industry, making the transport of goods and people safer, faster, and more economical. Given the rapid adoption of smart fleet management solutions powered by AI, Grand view research expected the size of the market to reach USD 565.1 billion by 2025. The report further states:
“The market is anticipated to register a CAGR of 7.6% from 2017 to 2025. Recently vehicular communication technology has increased the acceptance of smart fleet management solutions, which enable vehicle tracking and monitoring, fleet analytics, fuel management, predictive maintenance, remote diagnostics, and driver performance tracking and monitoring, etc.”
The current crop of fleet management solutions empowers operators to merge connected smart technologies to increase operational efficiency, enhance productivity, enable cost saving, build up driver and road safety, reduce carbon emission, and efficiently manage traffic bottlenecks.
Currently, trucks have a range of electronic parts and sensors, and they now operate a lot more like computers. Today, all such parts, right from the Diesel Particulate Filter and Selective Catalyst Reduction to the Alternator, have sensors that can collect a lot of data. These can play a chief role and relay information as well as insights to the operator – but only if the operator captures and use the data.
For example, by using AI technology, we can actually predict failure of the parts and the entire vehicle, rather than assuming that the mechanics and maintenance routine will keep the fleet on the roads for the optimum amount of time. AI impact security also: systems powered by AI and machine learning can identify threats at speeds that humans can’t. With AI, fleet management companies have access to insights and visibility into problems that help to:
Let’s take a closer look at how AI can help to address the two most common problem areas in fleet management.
Some years ago, video-based safety systems that provided vehicles in the fleet the context for hard braking and other safety-critical events were in vogue. However, supported by machine learning, these systems today have advanced to bring automation to review a range of video and data, identifying complex patterns of risk.
For example, it is now possible to detect risky driver behaviors like driver fatigue, inattention, and yawning. The driver’s face can be monitored with analytics determining the driver’s state of the mind. This gives fleet managers the opportunity to take the driver’s physical condition into account while planning, or to intervene if a safety risk is flagged, or to assess the performance of a team of drivers.
It is also possible to identify drivers covering distances at speeds that are unsafe when the road and traffic conditions are taken into account. A ‘lane detection’ system can detect and send notifications or reports when the driver leaves his lane.
Such patterns of behavior are detected via cameras or sensors and the data is processed in a cloud server, or in some cases, it is processed in edge computing devices.
We helped one of our clients in the industry develop a dash camera for fleet monitoring using the deep learning library, TensorFlow. The solution helps to track the driver behavior from the video data. We trained it for detecting 11 different anomalous behaviors. The aim is to predict what a driver is doing in the car; for example, texting, eating, displaying symptoms of drowsiness, talking on the phone, etc.
Mainly, the deep learning framework is used to generate driver scoreboard, perform sentiment analytics, optimize routes, increase driver efficiency, reduce insurance premiums, reduce accidents and lawsuits, track driver behavior, and generate actionable reports.
For solving the problems related to safety and driver behavior, IBM Watson™ Fleet Management and Driver Safety solution is widely adopted. Companies can leverage the power of Watson analytics via the cloud, and a set of APIs (application programming interfaces) allow them to analyze a host of rich new data sources.
IBM Watson™ Visual Recognition services use deep learning algorithms to identify scenes, objects, and faces from within the images. It helps systems to gather and analyze data from sensors and cameras, helping to develop and deploy fleet management systems that make the driver’s life on the road safer and more productive.
Thousands of cargo ships travel oceans every day. They face poor weather, congested ports, equipment breakdowns and several other issues. Any unpredicted delay, even by just a few vessels, can create major logistical issues that impact the entire fleet. And that’s not all – risky or illegal behavior of the crew can also contribute to the chaos.
So what are the some of the key reasons behind inefficient management of maritime fleets? Let’s look at the four major factors:
No track of en-route operational activities: Lack of visibility on where the vessel is halting, what speed it is sailing at, what is the average transit time, average loading time, average unloading time from the sea to the warehouse can make it difficult to optimize and improve the processes.
We worked on a project where the client was facing all the above problems and looking for a solution. So how can these issues be solved using technology? We created a solution that enables vessels in the commercial fleets to maintain vital data communication links with home ports no matter where they are on the earth. The home port will know the exact routes taken by the vessels throughout the journey. It further helps to provide real-time remote vessel tracking, monitoring and control for the entire fleet.
Continuous monitoring of vessel operational data ensures that all the vessels are working at peak efficiency at all time through continuous monitoring of vessel operational data. AIS (Automatic Identification System) and GPS tracking data is used to provide the port information about the exact location and condition of high and low tides. Live tracking of weather & tidal status via public APIs and auto clearance for traveling from the sea to the river and vice versa helps to reduce operational inefficiencies. At the same time, Live tracking of fuel through IoT sensors helps put a stop to fuel theft.
Many of the major challenges that have affected the fleet industry can be resolved by using AI-driven solutions – be it a fleet of trucks or ships. eInfochips (An Arrow Company) can provide connected transport and logistics solutions that include live tracking and geo-fencing, route management, predictive maintenance, fuel consumption management, vessel management, trip management, along with custom notification and alert mechanisms. To know more about how we can help you, get in touch with us.
