Driver’s Behavior Analysis
Analyzing the behavior of drivers is a valuable solution for both governments and businesses as it can make tangible indicators out of a mixture of real-time data and enacted regulations. Therefore, the quality of driving which used to be vague and intangible can be assessed the certain and clear criteria. Using this tech-enabled assessment, the fleet managers can monitor the driving style of each vehicle; the officials can observe the roads conditions and potential accident triggers; the insurance companies can offer customized packages based on different driving styles; and the drivers would be assisted to modify their adverse habits. In Arshin, we have developed an Internet of Behavior (IoB) platform, including IoT sensors, fuzzy rule sets, AI processing and user-friendly interface in order to assure our customers of a credible, reliable and precise assessment tool.
How do we analyze the driver’s behavior?
This analysis consists of three categories: input data, processing procedure, and output results. As input data, many variables influencing driving style should be taken into account which comes from various sources such as sensors and accessories, location tracker and map layer, and controlling laws. The IoT sensors data directly and the controlling laws via fuzzy rule sets enter to the Artificial Intelligence (AI) processor to become integrated and analyzed. For example, carelessness does not have a certain definition, but it can be determined by some measurable factors such as sudden acceleration, harsh braking, a sudden twist, spiral movement, lack of concentration on road lines and lanes, smoking, moody distraction or drowsiness, etc. All such parameters besides the entered laws such as speed limitations, cargo specified standards and in-car rules are analyzed by AI to generate insights and notify the users.
How does this analysis helpful?
The insightful output information can be customized based on the need in addition to the practical criteria we offer such as driving risk, driving style and culture, law violation, road conditions, potential accident triggers, etc. These indicators facilitate the assessment and reaction process for the authorities and business managers. For example, officials may design a punishment and reward paradigm to modify driving culture; insurance companies may determine the annual fee based on the driving risk records; and fleet managers can be notified of any law violations by the drivers. Also, our smart processor is equipped with some Machine Learning algorithms by which it improves with more use and can filter the meaningful data from the deviations. For instance, after a while, it can recognize that whether the driver is to blame for the sudden brakes and twists or some environmental factor enforced them to do so. Therefore, the system gets modified to enhance the reliability of output results so that it provides the decision makers with unique and valuable managerial insights.
