Periodic Reporting for period 2 - Road Lifeguard (Scalable Deep learning AI driver influence solutions - Road Lifeguard for global prevention of car accidents)
Período documentado: 2021-10-01 hasta 2022-09-30
More than 25 000 people lose their lives on EU roads every year, and many more are seriously injured. Although this is a decrease of 21% compared to 2010, numbers have not declined at the desired rate. After a sharp decrease, fatality rates have plateaued from 2013 onwards. In 2018, the average fatality rate decreased only by 1% compared to the previous year – standing at 25 100 road accident fatalities. Poor driving behaviour is an exceedingly expensive human conduct: It costs hundreds of thousands of lives every year; it burdens social welfares with the care of disabled survivors or bereaved relatives; it is a major contributor to pollution across the globe.
The challenge underpinning all of the above is how to encourage drivers to adhere to a safer and more environmentally conscious driving style. Our innovation improves the behaviour of drivers. Most drivers today are unaware of how risky their driving behaviour is; they are often not aware of how they expose themselves and others to unnecessary increased risk by ordinary factors such as stress and inattentiveness. Our experience in the field of AI-based digital risk analysis shows that bringing real-time assessment software to the roads can decrease the risk of collisions by 40% and reduce CO2 emissions by 26%.
The Enerfy solution is addressing services and manufacturers within the automotive industry, such as insurance carriers, with their end-users in sight. The infrastructure within this market is rather developed thus contributing to the emergence of new business models. At the same time, organisations are highly motivated to contribute to decreasing road accidents and find ways to ensure and to influence a higher consciousness and security on the roads. End-consumers on the other hand, are becoming increasingly aware of the value of the individual data that they leave behind and want something in exchange for it such as, more insights and knowledge, lower pricing, personalized offerings and/or solutions, or the ability to negotiate new insurances.
Greater Than provides a whole new insight, which is crucial for profitability improvement and a necessity for anyone who wants to develop new mobility services and subscription-based models, such as car sharing, carpools, and digital car insurances. We provide insurance carrier solutions powered entirely by mobile solutions and our company is founded on the principle that car insurance rates should be based on how you drive and not by who you are, through AI predictions in real time. The result is a usage-based insurance platform that rewards responsible, smart and planet-friendly drivers lower insurance costs.
The challenge underpinning all of the above is how to encourage drivers to adhere to a safer and more environmentally conscious driving style. Our innovation improves the behaviour of drivers. Most drivers today are unaware of how risky their driving behaviour is; they are often not aware of how they expose themselves and others to unnecessary increased risk by ordinary factors such as stress and inattentiveness. Our experience in the field of AI-based digital risk analysis shows that bringing real-time assessment software to the roads can decrease the risk of collisions by 40% and reduce CO2 emissions by 26%.
The Enerfy solution is addressing services and manufacturers within the automotive industry, such as insurance carriers, with their end-users in sight. The infrastructure within this market is rather developed thus contributing to the emergence of new business models. At the same time, organisations are highly motivated to contribute to decreasing road accidents and find ways to ensure and to influence a higher consciousness and security on the roads. End-consumers on the other hand, are becoming increasingly aware of the value of the individual data that they leave behind and want something in exchange for it such as, more insights and knowledge, lower pricing, personalized offerings and/or solutions, or the ability to negotiate new insurances.
Greater Than provides a whole new insight, which is crucial for profitability improvement and a necessity for anyone who wants to develop new mobility services and subscription-based models, such as car sharing, carpools, and digital car insurances. We provide insurance carrier solutions powered entirely by mobile solutions and our company is founded on the principle that car insurance rates should be based on how you drive and not by who you are, through AI predictions in real time. The result is a usage-based insurance platform that rewards responsible, smart and planet-friendly drivers lower insurance costs.
By the start of the project, Greater Than developed a pilot plan with four different partners in order to achieve two main objectives: training the AI to predict risk and optimizing communication tools to influence driving behavior. Greater Than's choice of partners with varied geographical reach, business models and demographics aimed to maximize the insights to be drawn from the pilot across different factors. The pilot plan was divided into four stages: the first and second stage included embedding the pilot into the business strategy and gauging market uptake, and the third and fourth stages included testing the pilot with partners and validating the technology - of which the last stage will be a continuous process.
To meet the pilot’s two objectives – optimizing the AI and end-user communication tools – the former has been analysed by collecting claims data and running analyses on the statistical significance of the correlation between risk level and claims cost, as well as risk level and injury, and the latter has been analysed by launching new features with a reduced customer group and comparing behavioural changes with the control group. At the end of this first period, Greater Than's AI and data analysis module were successfully completed. The data analysis findings show that Greater Than’s AI technology is making accurate measurements, which predict accident likelihood and injury occurrence. The tools that have been introduced to incentivize accident prevention, such as gamification and app use, have been found to significantly reduce accident risk. Equally, user and customer experience of these apps has been central to improving the efficacy of these tools.
A significant part of preventing accidents before they happen will rely on communication with high-risk drivers in real-time. We have conducted an experiment to evaluate the efficiency of different means of communication with drivers in high-risk situations. The experiment relied on two types of warnings: alarms and voices. After each communication, respondents were invited to grade the alarm/voice on two scales, the participant’s perception – i.e. whether it was irritating/stressful vs. enjoyable/pleasant – and the alarm’s effectiveness – i.e. how effectively said warning was in conveying the fact that one is driving in high risk. One overarching conclusion is that voice communication is both more effective and pleasant for users. Equally, personalization is key for user satisfaction. As such, any future implementations ought to include multiple choices, including gender as well as the option to record a personalized voice. This personalized voice could be used for an emotionally closer relationship, such as a child, husband, daughter, friend or other.
With regards to legal preparations and agreements we have identified and established the documents needed in legal matters to fulfil the partner legal requirements and to make the sign on process with new partners as smooth as possible. The review of what are requested in legal terms have been validated with several customers in several region globally.
Focusing on business model development, we have analysed our offerings to adjust the business and pricing model to the market willingness to pay for the service. We have within this development identified and established a structure to make it easy to prepare an offering to the partner, which we know is competitive both in pricing and with added value of the services and features.
To meet the pilot’s two objectives – optimizing the AI and end-user communication tools – the former has been analysed by collecting claims data and running analyses on the statistical significance of the correlation between risk level and claims cost, as well as risk level and injury, and the latter has been analysed by launching new features with a reduced customer group and comparing behavioural changes with the control group. At the end of this first period, Greater Than's AI and data analysis module were successfully completed. The data analysis findings show that Greater Than’s AI technology is making accurate measurements, which predict accident likelihood and injury occurrence. The tools that have been introduced to incentivize accident prevention, such as gamification and app use, have been found to significantly reduce accident risk. Equally, user and customer experience of these apps has been central to improving the efficacy of these tools.
A significant part of preventing accidents before they happen will rely on communication with high-risk drivers in real-time. We have conducted an experiment to evaluate the efficiency of different means of communication with drivers in high-risk situations. The experiment relied on two types of warnings: alarms and voices. After each communication, respondents were invited to grade the alarm/voice on two scales, the participant’s perception – i.e. whether it was irritating/stressful vs. enjoyable/pleasant – and the alarm’s effectiveness – i.e. how effectively said warning was in conveying the fact that one is driving in high risk. One overarching conclusion is that voice communication is both more effective and pleasant for users. Equally, personalization is key for user satisfaction. As such, any future implementations ought to include multiple choices, including gender as well as the option to record a personalized voice. This personalized voice could be used for an emotionally closer relationship, such as a child, husband, daughter, friend or other.
With regards to legal preparations and agreements we have identified and established the documents needed in legal matters to fulfil the partner legal requirements and to make the sign on process with new partners as smooth as possible. The review of what are requested in legal terms have been validated with several customers in several region globally.
Focusing on business model development, we have analysed our offerings to adjust the business and pricing model to the market willingness to pay for the service. We have within this development identified and established a structure to make it easy to prepare an offering to the partner, which we know is competitive both in pricing and with added value of the services and features.
During this project and Greater Than's extensive research and testing, three methods have been found to be effective in preventing accidents - the three proactively reduce driving risk so as to prevent accidents before they happen: the use of an end-user app was found to reduce risk by 33% in comparison with non-app users; the introduction of gamification features in said apps reduced risk further, with a reduction up to 45%; and proactive fleet management, whereby fleet managers singled out high risk individuals and conducted risk reviews, reduced risk by 7%.
Beyond this, the project has expanded relationships with world leading global companies that all have strong distribution networks and great credibility and influence on relevant markets. These companies are typically very selective with choosing companies to partner with, but have shown great interest in the value we can provide to their business.
The project timeline and results are aligned with our expectations, and we strongly believe that by the end of this project we will be able to advance our current solution into a scalable standard application that can detect and thus reduce the number of car accidents, while also providing the automotive and insurance industries with new digitalized business models.
Beyond this, the project has expanded relationships with world leading global companies that all have strong distribution networks and great credibility and influence on relevant markets. These companies are typically very selective with choosing companies to partner with, but have shown great interest in the value we can provide to their business.
The project timeline and results are aligned with our expectations, and we strongly believe that by the end of this project we will be able to advance our current solution into a scalable standard application that can detect and thus reduce the number of car accidents, while also providing the automotive and insurance industries with new digitalized business models.