Telematics-Based ADAS Use Detection for Driver Risk Scoring
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Solution Overview
Problem
It is challenging to determine whether advanced driver-assistance systems (ADAS) features are operational in a vehicle during a trip, as this information is not always exposed to mobile devices or telematics applications.
Innovation Solution
A computer-implemented method and system that receive telematics information from sensors during a trip, process this information to identify vehicle movement data, and determine the probability that an ADAS feature was operational based on this data, ultimately calculating a risk score for the vehicle or driver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If telematics information is collected and processed to determine ADAS operational status, then the ability to assess driving risk is improved, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary processing system that analyzes telematics information to infer ADAS operational status. Instead of directly accessing ADAS status signals, the system uses intermediate indicators such as vehicle speed variations, acceleration patterns, and braking behavior to determine whether ADAS features were active during driving, thereby assessing risk without requiring direct integration with complex ADAS control systems
Solution Approach 2:
The patent replaces direct mechanical/electrical signal access from ADAS systems with data-driven inference methods. By substituting physical signal access with algorithmic analysis of telematics data patterns, the system achieves ADAS status detection without the complexity of direct system integration
2Loss of information
If ADAS operational status is determined through telematics analysis, then information availability is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent inverts the traditional detection approach by not directly measuring ADAS status signals. Instead, it observes the effects of ADAS operation on vehicle dynamics (speed profiles, acceleration patterns, braking events) and infers ADAS status from these indirect measurements, making detection feasible through alternative observable parameters
3Measurement precision
If vehicle movement information is processed to identify ADAS operation, then measurement precision is improved, but the loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary processing of telematics data by continuously monitoring and storing vehicle movement parameters (speed, acceleration, braking events) as they occur during driving. This pre-processing ensures that when ADAS status determination is needed, the relevant data is already organized and ready for analysis, reducing the time required for retrospective assessment
Solution Approach 2:
The patent focuses on analyzing only the critical telematics parameters most indicative of ADAS operation (such as speed variance, acceleration patterns, and braking events) rather than processing all available vehicle data. This selective partial analysis maintains measurement precision while reducing overall processing time by concentrating computational resources on the most informative data points
Data Source
AI summary
A system can include a mobile device that includes one or more sensors for sensing information during a trip in a vehicle. A hardware processor can execute operations including receiving telematics information produced by one or more sensors during a trip in a vehicle; processing, by a hardware processor, the received telematics information to identify vehicle movement information for the vehicle during the trip; determining, by the hardware processor, a probability that an advanced driver assistance system (ADAS) feature of the vehicle was operational during the trip based, at least in part, on the vehicle movement information for the vehicle during the trip; and determining, by the hardware processor, a risk score for the vehicle or a driver of the vehicle based, at least in part, on the probability that the ADAS feature was operational.