Digital Alerting Rule Feedback Using Vehicle Telemetry
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Solution Overview
Problem
Cloud-based digital alerting systems struggle to determine if their alerting rules effectively improve roadway safety due to the difficulty in assessing the impact of digital alerts on vehicle behavior in large-scale deployments.
Innovation Solution
A computer-implemented method that determines post-alert behavior of vehicles using telemetry data and modifies digital alerting rules based on this behavior to meet predefined safety principles, allowing for automatic adjustments in alerting zones and alert frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital alerting rules are implemented in large-scale deployments with many types of hazards and alerting rules, then the coverage and versatility of the alerting system is improved, but the difficulty in assessing the impact of digital alerts on vehicle behavior increases
Solution Approach 1:
The system automatically modifies digital alerting rules based on post-alert behavior analysis of vehicles. By continuously monitoring vehicle telemetry data and comparing it against predefined safety principles, the system creates a feedback loop that adapts alerting rules to improve safety effectiveness. This feedback mechanism resolves the measurement difficulty by using actual vehicle behavior data to assess and quantify alert impact.
Solution Approach 2:
The alerting system performs self-optimization by automatically analyzing its own performance through post-alert behavior determination. The system uses its collected telemetry data to modify its alerting rules without external intervention, enabling it to self-assess impact and self-adjust to improve safety outcomes in large-scale deployments.
2Speed
If digital alerts are provided frequently to ensure timely warning, then the safety responsiveness is improved, but the risk of false alerts and driver distraction increases
Solution Approach 1:
The system dynamically adjusts alerting parameters such as alert timing, frequency, and intensity based on analyzed post-alert behavior. By modifying these parameters automatically, the system optimizes the balance between responsiveness and reducing false alerts, ensuring alerts are provided at optimal times to improve safety while minimizing harmful effects.
Solution Approach 2:
The alerting system transitions from static, fixed rules to dynamic, adaptive rules that automatically adjust based on real-time vehicle behavior analysis. This dynamic approach allows the system to respond appropriately to different driving contexts, improving safety responsiveness while reducing unnecessary alerts that could cause driver distraction.
Data Source
AI summary
Systems and methods for use in cloud-based digital vehicle alerting are disclosed. In an example, a computer-implemented method for operating a digital alerting system involves determining a post-alert behavior of a vehicle that was provided a digital alert, the post-alert behavior is determined using 1) a time that the digital alert was provided to the vehicle, and 2) vehicle telemetry data that was received at a digital alerting system from the vehicle, modifying a digital alerting rule based on the post-alert behavior, and updating a rules engine of the digital alerting system with the modified digital alerting rule, wherein the rules engine is configured to implement digital alerting rules.