In this interview, Hari Balakrishnan, Founder & Chief Technology Officer of Cambridge Mobile Telematics, shares his insights on how the smartphone is one of the key components in distracted driving and how the same tool can actually be used to improve driving quality. Cambridge Mobile Telematics is a global company working with 35 organizations from over 20 different countries to accurately measure driving quality, deploy behavioral incentives to improve driving, and use AI to automate the claims process.
Keep reading to learn how Hari and CMT are investing in the future of better driving by expanding their global platform, developing new video analytics software, and improving driving safety in a world where self-driving cars are a soon-to-be reality.
In 2010, the concept of using a smartphone to collect fine-grained data to draw accurate inferences about vehicle dynamics or give direct feedback to drivers was unknown. Any attempts at that time to use smartphones to better understand user behavior were seen as inaccurate and generally unreliable, especially in the field of telematics.
When we started Cambridge Mobile Telematics (CMT), however, we were betting that the smartphone would be a critical, central component to the field in the future; more so, that although the smartphone would be the very device responsible for distracting drivers on the road, it could also help make them less distracted and become better drivers.
We also realized that many people do not recognize the dangers of driving while distracted. We were seeking approaches for what we viewed as preventable incidents. Our goal was to accurately measure driving quality and to create better drivers, making the roads safer for all.
Over the last nine years, CMT’s DriveWell platform has helped make roads safer by making drivers better in a world where crashes are rising because of factors like distracted driving. CMT’s rapid growth is fueled by a company culture that is deeply customer-committed, values collaboration, and values creativity via investment in research to improve current solutions and develop new ones.
Many of the largest insurers in the world have adopted CMT’s DriveWell platform – a complete telematics and behavioral analytics solution for the connected car world that (1) accurately measures driving quality using mobile sensor data, (2) deploys a range of behavioral incentives to improve driving by reducing risk factors such as phone distraction and risky speeding, and (3) uses artificial intelligence on telematics data to automatically automate several aspects of claims management.
Using machine learning and signal processing, DriveWell accurately infers key metrics about mileage, speed, acceleration, driving style, distraction, and collisions. DriveWell understands a driver’s behavior over time, providing positive reinforcement for safe driving behavior: incentives such as gift cards and reduced insurance rates based on good driving influence behaviors. Meanwhile, our AI methods operating on sensor data can step in to save lives in the event of a crash.
Following a crash, it takes a lot of time and money for insurers and drivers to process a claim. CMT’s crash reconstruction technology applies AI techniques to telematics and contextual data, providing insights and decision-support capabilities to reduce the effort and cost. As a first step, crash reconstruction provides a comprehensive picture of the event, using processed telematics data, calculated crash indicators, and contextual information. Insurers can receive this information visually in the DriveWell portal or via an API. With this service, insurers can see machine-generated crash descriptions and details like severity rating, number of impacts, duration of impact, probability of vehicle hit location, weather and more.
As a result, insurers and agents are able to begin the claims process earlier and reduce manual efforts to document and analyze crash details.
We have pioneered many innovations since our 2010 inception and spinoff from MIT’sComputer Science and Artificial Intelligence Lab. We deployed the first service to efficiently gather and process sensory data from phones for auto insurance in 2012; used phone sensors to measure phone distraction in 2013; and induced better driving with gamification in 2014. Together, these innovations created the category of “behavior-based insurance,” also known as “mobile usage-based insurance,” that enables the insurance industry to better price policies and save their customers money.
Using machine learning algorithms to assimilate information such as excessive speeding, harsh acceleration or distraction, DriveWell understands the drivers’ behaviors over time. It can then provide feedback, gamified ranking and positive reinforcement to encourage long-term driving style change. DriveWell’s impact on driver safety is undeniable and well documented:
The relative risk of crashing increases by a factor of 23 if texting while driving. Throughout the U.S., distracted driving occurs on over one-third of trips. After seven days of using the DriveWell app, we observe a 15% decrease in distraction events, followed by a 35% reduction in distraction after 30 days. These improvements can be sustained.
We recently introduced new services including real-time impact alerts for roadside assistance and first notice of loss (FNOL) as well as crash reconstruction and claims assessment using mobile telematics data. These services benefit insurers, agents and drivers by starting the claims process earlier and reducing manual efforts to submit, document and analyze crashes.
CMT’s crash reconstruction technology allows insurers to save time and expenses by automating many of the key steps in the claims lifecycle. By using the technology provided through CMT, they can receive accurate, comprehensive impact and contextual data, improving and building driver relationships with additional safety services.
We also launched extensive vehicle fleet features for commercial insurers and ride-hailing companies, and best-in-class risk scoring models, which enable insurers to accurately price risk using telematics data.
Our actuarial risk scoring services emphasize crash risk factors like phone distraction and at-risk speeding to augment traditional telematics factors such as hard braking. The model provides a decile lift of 22x and is now approved by regulators in 28 states.
In 2010, the concept of using a smartphone to collect fine-grained data to draw accurate inferences about vehicle dynamics or give direct feedback to drivers was unknown. Any attempts at that time to use smartphones to better understand user behavior were seen as inaccurate and generally unreliable, especially in the field of telematics.
As of 2018, CMT has more than 35 customers in 23 countries, serving millions of users every month with its DriveWell platform. A few months ago, SoftBank’s Vision Fund, the world’s largest technology investment firm, invested $500 million in CMT to accelerate the adoption of its DriveWell platform and fuel product and market expansion.
Since CMT’s inception in 2010, we have been certain that phones can effectively make people safer drivers, reduce the number of accidents, and improve the productivity and profitability of insurers, thanks to the promise of telematics. Today, our DriveWell platform is used by leading insurers but also mobility fleet managers, cellular carriers, and other organizations in more than 20 countries around the world.
We hope to continue on this path, using our company’s profitability and the recent infusion of capital from the Softbank Vision Fund to invest in three areas:
Today, we see this change in ride-hailing and delivery, where amateurs are driving in a professional context. We are developing solutions to improve insurance and safety for drivers, passengers, and goods in this world. We are also seeing an increase in assistive driving capabilities on the road to autonomy.
As assisted – and later autonomous – vehicles become more commonplace, humans and machines will have to learn to share the road together. Telematics will play a fundamental role in insurance for autonomous vehicles, where understanding and measuring a vehicle’s behavior and performance will become the de facto approach toward insuring it.
It will require data-driven methods to evaluate the quality of sensory data, measure the quality of the automotive machine learning systems, and evaluate the software itself. At CMT, we plan to use our position as a leader in advanced telematics and analytics to ensure the ride is as safe as possible as this future unfolds.
Founder & CTO of Cambridge Mobile Telematics
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