In the wake of road accidents that cost transport companies billions of dollars every year, there is a necessity for fleet management systems that ensure safety. Like many other systems, fleets need to collect and analyze road data which can influence their safety and performance and prevent road accidents.
This has not always been easy to do because preventive measures are usually based on data from accidents that have occurred. This type of trigger-based approach is quite common yet inefficient.
Legacy systems that use trigger-based crash recorders are gradually being replaced by complex edge computing systems.
Some of these systems such as Driveri use artificial intelligence, HD video, and other predictive technologies to analyze road and driving patterns as well as correct driver performance in real-time to avoid collisions and improve driver safety and retention rates.
The following comparison between edge computing and trigger-based data processing will look at the benefits of each one and how platforms using these technologies compare.
Trigger-Based Data Recorders vs Edge Computing Data Recorders
How do Trigger-Based Data Recorders Work?
Trigger-based data recorders capture data relating to events as they occur. For example, one common trigger you’ll see is a hard braking event that will prompt the camera to start recording. This method can be advantageous, as that data can help prevent similar events from happening in the future. Unfortunately, the data is only captured when the damage has been done — meaning it only catches the aftermath and not the cause.
Fleets need more advanced technologies that process data in real-time without the need for event-based triggers. For a long time, trigger-based data recording was sufficient for fleet safety but new developments in technology have prompted the need for better predictive systems. This has led to the creation and adoption of edge computing technology.
How do Edge Computing Data Recorders Work?
Edge computing is a method of data processing in which data is analyzed close to the point where it was collected. This means the information is analyzed in real-time, without the need for a “trigger” to start the process.
In edge computing, data is typically gathered on the outermost edges and transmitted to servers for processing, often in real-time. In years past, most computers could not process the large amounts of data collected due to their low storage space and computing power. This was true even as computers evolved with Wifi and Bluetooth connectivity due to hardware limitations.
Edge computing became a viable solution to these limitations. Today, many Internet of Things (IoT) devices are capable of performing complex data collection, processing, and storage. Companies have improved their networks which serve as links between edge computers and their servers. As a result, more data can be processed closer to the edge computer.
Significant applications of edge computing can be found in many industries including finance, and gaming. Fast data processing is essential for any fleet management software. In fleet management, edge computing is a great way to collect and process road data because of its low latency. This advantage of edge computing can be seen on platforms like Driveri that use artificial intelligence to make driving predictions.
Benefits of Edge Computing Data Recorders
Systems involved in the collection of high-volume data over a long distance are prone to lag. For example, if your personal computer lacks a powerful processor, it might freeze while you’re uploading or downloading media. Since the time taken for data to reach the processing servers is lower in edge computing, it is great for autonomous vehicles.
There are millions of road miles leading to high volumes of data that require real-time processing. Edge computing makes it easier to collect road data which can be used to train artificially intelligent software. In addition to this, it makes it easier for one autonomous vehicle to communicate with other vehicles.
Traditional cloud computing takes a more centralized approach by connecting computers directly to servers. On the other hand, edge computing connects a single edge computer to multiple processors and devices making it difficult to find a single point of failure. The decentralized nature of edge computing makes it harder for security threats such as Distributed Denial of Service (DDoS) to thrive.
Edge Computing Fleet Safety Systems
The Driveri system combines artificial intelligence with edge computing and smart video technology to automate driver monitoring, safety and analytics. Netradyne applies advanced technologies such as deep learning, edge computing, computer analytics and data science to improve the transportation ecosystem.
With the ever-growing need for driver retention and collision avoidance, Driveri aims to simplify the task of keeping drivers safe. This comes with the bonus of keeping them motivated while they maintain autonomy in their jobs.
How Driveri Works
Driveri tackles both the human and technological aspects for driver safety by targeting analytics, communication, connectivity and value.
It comes in the form of a device that can be attached to vehicles and connected to a remote application. This device keeps track of the driver’s transport cycle while ensuring that drivers stay safe and avoid dangerous situations.
So far, Driveri has captured and analyzed more than 350 million miles of road data owing to its large AI vision-based software and powerful processors. Not only have drivers traveled 1 million unique miles using the Driveri system, but they have also gone over these miles several times to collect more information on the roads and routes.
In every industry, the collection and analysis of data is crucial to how quickly technological advancement occurs. This holds true for the transportation industry as well. The advanced edge computing-based mapping and analysis technology that Driveri uses is a necessity for the industry.
Insights gathered by the system can be used to make informed decisions on driver behavioral patterns as well as draw unbiased conclusions where accidents occur. With close to $60 billion spent by employers annually due to accidents, driver safety systems like Driveri are needed now more than ever.
Features of Driveri
- Forward, side, and interior QUAD HD cameras for high-quality video recording, analysis and playback of real-time events.
- Immediate access to 100+ hours of video playback for records and research or used as evidence in the case of accidents where there are legal consequences.
- A 3-Axis Accelerometer and Gyro Sensor that continuously calculates the speed and orientation of the vehicle while using the data to make real-time decisions.
- Fast 4G LTE / WiFi / BT Connectivity for easy connection between vehicles and their organizations, to send and receive data, view video, and analyze risky behaviors.
- DriverAlert system which acts as a driver’s companion and onboard coach through the average transport cycle. The system detects risky events and advises drivers on the best course of action to take.
- DriverPrivacy for quick communication between drivers and fleet managers.
- Advanced data analysis system with more than 1 million unique miles of US roads analyzed.
- Single module installation system and integrated vehicle mount for quick and easy installation.
- EventAccess which gives authorized personnel access to stored video anytime.
- J1939 / OBD II artificial intelligence vehicle data system for a comprehensive view of the driver’s transport cycle.
As the 2018 winner of the “Best AI-based Solution for Transportation” award from AI Breakthrough, Driveri’s cutting-edge technology goes far beyond the competition.
Driveri has a comprehensive alert, communications, data analysis, and video playback system with excellent connectivity. Coupled with edge computing as a base technology, it offers processing speeds that make it easy to collect and analyze data in real-time. It also houses all of these technologies without the need to integrate any new products. This is perfect for any fleet manager looking to save costs of installation, training drivers to use the system, and remote connections. These savings do not compromise the efficiency of the system or the quality of data collected despite performing better than its competitors.
After analyzing 1 million unique road miles several times, Driveri’s artificial intelligence technology continues to chart road patterns while collecting more data geared towards improving your driver safety, and by extension, your fleet. However, edge computing is what makes this possible since slow data processing would make artificial intelligence feedback tedious.
Companies will continue to optimize their networks to scale the amount of data that can be processed during a single transmission. This means that it will become easier to handle complex tasks such as onboard driver coaching, vehicle self-diagnosis, data mapping and recording, and communication.
In the ever-changing transport industry, the onus falls on fleet managers to diversify their technology and optimize for cost savings, efficiency, and safety. The US Department of Labor, Occupational Safety and Health Administration (OSHA) demands it and your customers require it to maintain trust in your service.