How AI Is Transforming Fleet Safety
AI-integrated software is a sophisticated system made up of several devices and applications such as predictive data analysis and machine learning systems, HD cameras and sensors, communication and display systems. AI-based fleet management platform Driveri, currently deployed in fleets across the country, is a combination of all 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.
HD Cameras
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
- 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
This is significant because it creates a future of fleet management where human error is reduced across the transport cycle. This, in turn, leads to better outcomes and cost savings.
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
- Compliance
Risky road behaviors like distracted and drowsy driving are often accompanied by signs that drivers are told to look out for. These signs include:
- Yawning
- Constant blinking
- Missing turns or exits
- Drifting out of their lane
- Slow reaction times
- Using a cellphone
Ordinarily, managers have no way of knowing if a driver had been texting while driving or nodding off at the wheel. AI systems can 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 are another challenge because they are difficult to detect without proper technological tools. AI-based predictive technology can 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 1.5 million unique miles of US roads. In the future, an extensive database of road conditions will be essential for promoting safety.
Final Thoughts
The future of transportation has exciting applications of AI in fleet management. 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.
The first vehicle technology company to provide Artificial Intelligence with video and advanced onboard sensors to detect, reason, and determine causality of events, Netradyne is reducing accidents by creating a new safe driving standard for commercial vehicles. We empower drivers by providing them with more awareness of risky driving behavior and reward safe driver decision-making.
