CerebrumX announces Ford as a partner that enables usage-based insurance | Rare Techy


CerebrumX announced a data-sharing partnership with Ford, enabling it to provide insurance companies with real-time data for UBI (Usage-Based Insurance). This partnership applies to model year 2020 and later editions of Ford and Lincoln vehicles. The announcement adds to an impressive list of automotive OEM partners with whom CerebrumX has signed agreements, including Toyota, Nissan and Stellantis.

UBI has gained considerable popularity over the past three years (the recent COVID epidemic sharply reduced car use) and allows insurance pricing based on actual miles driven. CerebrumX further enhances this through its SaaS (Software as a Service) product, which pulls ~250 different data streams from the vehicle’s CAN (Controller Area Network), infotainment and telematics systems. CerebrumX’s ADLP (Augmented Deep Learning Platform) processes and integrates these data streams and augments them with AI-based scores and insights for real-time access to customers over 4G/5G connectivity. In addition to being used, insurance companies can also use this data to assess driving patterns, accident reconstruction, emergency dispatch and roadside assistance.

Compared to most competing products that use anonymous data, CerebrumX’s approach provides its customers with vehicle-specific data and generates driver and vehicle scores without additional hardware or applications. This score helps insurers better assess risk and create more specific, personalized policies for their customers, such as Pay As You Drive (PAYD) and Pay How You Drive (PHYD), to support safe driving and optimize claims. Figure 1 shows the types of data inputs and outputs of the Cerebrum ADLP platform.

The value of ADLP lies in the creation of knowledge for different customers dealing with specific applications in the ecosystem. Vehicle health and driver score are important to insurance providers. Fleet operators value knowledge of fuel and mileage patterns and predictive maintenance information to avoid costly breakdowns. As V2X and advanced ADAS become more common, the type of data available will increase and enable deeper insights and more advanced applications (such as assessing driver engagement through haptic sensor data).

The ADLP platform is a cloud-based synthetic sensor capable of integrating other data sources (for example, in-cabin and road-facing camera data). However, capturing and transmitting continuous full camera data is expensive, introduces latency and is likely to saturate the bandwidth of the connection. Part of CerebrumX’s intellectual property (developed with partners) is detecting salient events such as driver distraction or an accident and capturing relevant time-stamped sensor data segments. ADLP algorithms process this data and deliver it to the client in a time-synchronized format for accident reconstruction and loss management.

As shown in Figure 1, CerebrumXi’s SaaS products have other applications besides UBI, whose end customers are insurers, including:

  1. Fleet management: uses connected fleet data to monitor location and health, driver safety, collisions and preventive maintenance. Fleet owners use this data to maximize vehicle uptime and scheduling.
  2. Aftermarket warranty and repair: Service providers can use real-time CAN data and alerts to encourage preventive maintenance and reduce repair costs
  3. Electric vehicle (EV) battery monitoring and charging: ADLP data products can be used by charging service providers to optimize site selection and power/battery needs, and by car users to plan trips.
  4. Road safety: Simplified access to alerts and location in the event of an incident, allowing first responders to be quickly dispatched with accurate information on vehicle occupants and status
  5. Traffic flow control: telemetry data collected from groups of vehicles makes it possible to predict the flow of traffic and control the signal to ensure smooth movement and avoid traffic jams and accidents

CerebrumX was founded in 2018 and its global headquarters was established in March 2020 in Princeton, New Jersey. It has a total of 40 employees (30 in India) and raised $5 million in Series A in 2020, with two notable rounds. investors – LG Technology Ventures and Cerence. More than 15 million vehicles are currently connected to its network under contracts with OEM partners. OEMs charge $2-$6 per vehicle per month for data access, ADLP’s SaaS products have a market value of $6-$10 per vehicle per month. Due to the increased volumes, OEM data access costs should decrease, increasing the profitability of the SaaS business model. The company is generating revenue and currently has a customer order backlog of >$20M (7 signed customers in the insurance, fleet and aftermarket services verticals) due for delivery by 2024. The company plans to expand into other geographies and verticals and expects to reach profitability in 2024.

According to Sumit Chauhan, CEO of CerebrumX: “Modern cars generate significant amounts of data and include the ability to connect to the outside world. The availability of high-bandwidth connectivity, embedded computing capabilities, cloud computing, and intelligent edge and cloud computing is required to provide solutions for verticals such as insurance, fleet management, aftermarket services, smart cities, etc. The total market for relevant SaaS products and services will grow to ~$100 billion by 2025. CerebrumX is excited to be a part of this journey and pioneer. ahead of the road”.

As L3 (autonomous driving with a human driver ready to take over within 10 seconds) autonomy takes hold and L4 (full autonomy under certain conditions) beckons, SaaS products using vehicle and other data (e.g. V2X) will become increasingly influential for autonomy-based applications. For example, the safety of a chimney with an autonomous drive can be qualified, as well as the health and refueling requirements of the vehicle. Passenger data can be analyzed to provide important information for targeted advertising and passenger and vehicle safety. The challenge is likely to be communications bandwidth, as well as efficient and salient data processing from the vastly richer array of sensors that replace human perception.


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