CerebrumX Announces Ford as Partner to Enable Usage-Based Insurance


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


UBI has grown in popularity over the past three years (the recent COVID outbreak has dramatically reduced car use) and allows insurance pricing based on actual miles driven. CerebrumX enhances this further with its SaaS (Software as a Service) product which obtains approximately 250 different data streams from the vehicle’s CAN (Controller Area Network), infotainment and telematics systems. CerebrumX’s Augmented Deep Learning Platform (ADLP) processes and integrates these data streams and complements them with AI-based scores and insights for real-time customer access via 4G/5G connectivity. Besides usage, insurance companies can also use this data to assess driving habits, accident reconstruction, emergency dispatch, and roadside assistance.

Compared to most competing products that use anonymized data, CerebrumX’s approach provides customers with vehicle-specific data and Generates driver and vehicle scores with no additional hardware or app required. This score helps insurers better assess risk and develop more accurate and personalized policies for their customers, such as Pay As You Drive (PAYD) and Pay How You Drive (PHYD), to promote safe driving and optimize claims. Figure 1 shows the types of data inputs and outputs of Cerebrum’s ADLP platform.

The value of ADLP lies in creating insights for different clients dealing with specific applications in the ecosystem. Vehicle condition and driver score are relevant to insurers. Fleet operators appreciate fuel and mileage model information and proactive maintenance information to avoid costly breakdowns. As V2X and advanced ADAS become more common, the types of data available increase and allow for deeper insights and more advanced applications (e.g., assessing driver engagement through haptic sensor data ).

The ADLP platform is a cloud-based “synthetic sensor” that can integrate other data sources (e.g. cab and road-facing camera data). However, continuously capturing and transmitting full camera data is expensive, introduces latency, and risks saturating connectivity bandwidth. Part of CerebrumX’s intellectual property (developed jointly with partners) is to recognize salient events (e.g. driver distraction or an accident) and capture slices of relevant timestamp sensor data. ADLP’s algorithms process this data and provide it to the customer in a time-synchronized format for accident reconstruction and claims management.


As shown in Figure 1, in addition to UBI, which has insurance companies as its end customer, CerebrumX’s SaaS products also have other applications, including:

  1. fleet management: uses connected data across fleet vehicles to monitor location and health, driver safety, collisions and proactive maintenance. Fleet owners use this data to optimize vehicle availability and scheduling.
  2. Warranty and after-sales repair: Service providers can use real-time CAN data and alerts to encourage proactive 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 car users for trip planning.
  4. Road safety : Simplified access to incident alerts and location to enable rapid dispatch of first responders with accurate occupant and vehicle status information
  5. Traffic flow management: the accumulation of telemetry data from groups of vehicles enables traffic flow prediction and signal control to smooth the flow and prevent traffic jams and accidents

CerebrumX was founded in 2018 and its global headquarters was established in Princeton, New Jersey in March 2020. It has a total of 40 employees (30 based in India) and raised a Series A of $5 million in 2020 , with two major investors – LG Technology Ventures and Cerence. Based on agreements in place with OEM partners, more than 15 million vehicles are currently connected to its network. OEMs charge between $2-6/vehicle/month for data access, with the market value of ADLP SaaS products ranging from $6-10/vehicle/month. As volumes increase, OEM data access costs are expected to decrease, increasing the profitability of the SaaS business model. The company is revenue generating and currently has a backlog of over $20 million in customer orders (7 signed customers in the insurance, fleet and aftermarket verticals) to deliver by 2024. It plans to expand into other geographies and verticals, and expects to reach profitability in 2024.

According to Sumit Chauhan, COO of CerebrumX: “Modern cars generate significant amounts of data and incorporate the ability to connect with the outside world. The availability of high-bandwidth connectivity, embedded computing capabilities, cloud computing, and computing smart at the edge and in the cloud is necessary to provide solutions for vertical industries such as insurance, fleet management, after-sales services, smart cities, etc. The overall market for relevant SaaS products and services is expected to reach approximately $100 billion by 2025. the road ahead”.


As L3 autonomy (autonomous driving with a human driver ready to take control within 10 seconds) kicks in and L4 (full autonomy under certain conditions) beckons, SaaS products using vehicle and other data like V2X are becoming more and more impactful for applications that rely on autonomy. For example, the safety of the autonomous driving battery can be qualified, as well as the health and refueling requirements of the vehicle. Passenger data can be analyzed to provide vital information for targeted advertising and passenger and vehicle safety. Communication bandwidth will likely be a challenge, as well as processing actionable and salient data from a much richer sensor stack that supersedes human perception.

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