Artificial Intelligence (AI) may seem like science fiction, but it has already made its way into the things we use every day without us even realizing it. Voice-controlled personal digital assistants, self-driving cars and vehicles, navigation apps, facial recognition, and other predictive capabilities are some of the forms of AI in our daily lives. Fleet management businesses have begun to use the constantly evolving AI to prioritize resources, collect and analyze data, identify risky driving behaviors, identify areas of cost containment, and enforce compliance. Fleet operations are powered by various practical applications of AI, such as the Internet of Things (IoT), Predictive Analytics, Machine Learning (ML) systems, and Computer Vision-based automated communication and display systems, etc.
Fleet managers are realizing that AI is not meant to replace them, but rather assist them in making their role more productive and streamlined. Fleets can use UI to prioritize driver safety without compromising on cost and efficiency. Keeping track of fleet operations and making timely decisions is now made simple with AI.
Let us now see how AI can help Fleet businesses manage their operations.
Table of Contents
Improved Driver and Vehicle Safety
Using AI, fleets can detect risky driving behavior, such as speeding, sign violations, sleepiness, driver fatigue, inattentiveness, etc. This gives fleet managers an insight into the driver’s performance and intervene if a safety risk is flagged.
Risky driving can be identified by looking for the following signs:
- Constant blinking
- Missing turns or exits
- Drifting out of lane
- Slow reaction times
- Using a cell phone
Without a monitoring system, managers cannot determine if a driver had been texting while driving or nodding off at the wheel. But AI systems can be trained to detect yawning and blinking frequencies, head turns, and signs of risky behavior and broadcast them to managers in real time, thereby allowing them to take corrective measures to coach the driver.
AI can also make smart predictions about the weather and changing road conditions. AI-based predictive technology can also infer risks from data collected from other vehicles. In a world of risk, Driver•i is the only fleet dashcam to proactively analyze 100 percent of drive time. With HD quality video from the highest camera resolution, Driver•i can visually identify signs, signals, pedestrians, and other objects delivering insights in real-time.
Real-time in-cab audio alerts can notify drivers to take corrective action, warn of potential accidents, and immediately make safe decisions. The inward camera of Driver•i processes data in real-time and immediately communicates to the driver through an audio notification. For example, when a driver follows another vehicle too closely, the driver will immediately be warned. There is no need to wait for the fleet manager to analyze the video and summon the driver for coaching.
According to Adam Kahn, President, Commercial Fleet Team with Netradyne, “Netradyne’s AI platform can identify when a driver has picked up a cell phone and remind the driver to put it down within 11 seconds. In 11 seconds, our system has recognized a dangerous maneuver and invoked changes,” he explained.
Reduced Vehicle Downtime
Just like preventive healthcare can save a person from experiencing a more severe health event, using AI to look out for clues in the vehicle data can prevent vehicle breakdown. AI relies on data to gain insights and make predictions. Current trucks have several electronic components and sensors that collect a lot of data, such as engine diagnostics ODB2 and CAN bus data, fuel usage, idle times, location, vehicle utilization, driving hours etc.
AI can be used to predict failure of the vehicle parts or the entire vehicle itself. For example, AI can be used in combination with a high-tech camera system to detect worn tires or missing underbody parts much faster than manual inspection. Internet of Things (IoT), data analytics, and predictive maintenance can forecast potential defects even before they occur. This helps optimize the overall vehicle maintenance costs.
Enhanced Decision Making
Fleets can use AI to determine relationships among different types of data to understand certain outcomes. Studying the performance of a driver over time can be used to arrive at a score for the driver and determine areas where the driver is prone to exhibit risky behavior. For example, the AI model might determine that the driver tends to turn at a high speed while making a left turn. Managers looking at this data can then recommend a training for avoiding Hard Turns.
AI can also be used to study the fuel usage in vehicles by looking at relationships among data collected and determine exactly if the problem lies with the driver, vehicle, or road conditions. Once the cause is known, corrective actions can be taken to improve fuel economy.
Automated Decision Making
AI can be used to perform repetitive tasks and automate decision making thereby saving time.
Virtual coaching is an option of coaching drivers in an automated way without requiring an active review of risky driving events by the fleet manager. By analyzing the driver’s risky events generated during the previous week, the AI algorithm gives drivers a small subset of behaviors to review and focus on. This analysis not only helps in improving safety on the road, but it also decreases the time managers spend reviewing videos by up to 50%.
Investing in the right technologies will equip fleets to have the capabilities to promote safe driving and reduce the risk of accidents. Fleets can use AI and ML to improve their fleet operations, increase vehicle and driver safety, and detect vehicle problems that can be missed by drivers and service personnel. Using AI, managers can make better decisions without putting in a lot of extra effort.
“We found that the artificial intelligence, machine learning, video quality, and instant feedback provided by the Driveri solution was the best dash cam that fit our needs, and the enhanced focus on positive aspects of our driver’s behavior was a true product differentiator.” – Havlor Lines