Vehicle Response Monitoring Using 360° Sensors and CAN Timing
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Solution Overview
Problem
Existing vehicle monitoring systems rely solely on sensor data from a limited surrounding region, leading to false positives and negatives, and are inefficient in high-speed situations, failing to account for other vehicles' actions and evolving scenarios.
Innovation Solution
An apparatus with a processor and sensors that collect 360-degree sensor data, uses CAN bus data to determine acceptable and actual response times, and initiates remedial actions when actual responses deviate from acceptable ranges, incorporating machine learning models for anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sensor data from a limited surrounding region is used for monitoring, then device complexity is reduced, but measurement precision and reliability deteriorate due to false positives and negatives
Solution Approach 1:
The monitoring system is segmented into multiple independent sensor units distributed around the vehicle, each capturing data from its specific zone. This allows comprehensive 360-degree coverage while keeping each sensor unit relatively simple, resolving the contradiction between system complexity and measurement precision.
Solution Approach 2:
Multiple sensor data streams from different zones are merged and processed together to form a comprehensive view of driver behavior and vehicle surroundings. This combination enables accurate behavior assessment while maintaining manageable individual sensor complexity.
2Device complexity
If solely sensor data is used for monitoring, then device complexity is reduced, but response speed deteriorates in high-speed situations
Solution Approach 1:
The system uses a multi-functional architecture where sensor data serves multiple purposes: real-time behavior monitoring, anomaly detection, and training data generation for machine learning models. This universal data usage improves response speed across different operational contexts without proportionally increasing system complexity.
Solution Approach 2:
Sensor data is continuously collected and pre-processed even before anomalies occur, preparing the system for rapid response. Historical sensor data is used to train machine learning models in advance, enabling faster real-time detection and response during critical high-speed situations.
3Device complexity
If traditional sensor data alone is used, then device complexity is reduced, but reliability deteriorates by failing to account for other vehicles' actions and evolving scenarios
Solution Approach 1:
The system incorporates feedback loops where sensor data about other vehicles' actions and evolving scenarios is continuously fed into the machine learning models. These models update their understanding of normal vs. anomalous behavior based on this feedback, improving reliability of behavior assessment while managing processing complexity through iterative learning.
Solution Approach 2:
The system dynamically adapts its behavior assessment criteria based on changing conditions detected by sensors. As other vehicles' actions and scenarios evolve, the machine learning models adjust their parameters and thresholds in real-time, maintaining high reliability without requiring a statically complex rule-based system.
Data Source
AI summary
Sensor data indicating a substantially 360 degree surrounding of a vehicle is received via a processor included in the vehicle. The sensor data is collected using multiple sensors included in the vehicle. Additionally, an acceptable response time range for a driver of the vehicle to perform an action with the vehicle is obtained, via the processor, based on the sensor data. Additionally, an actual response time for the driver to perform the action is determined, via the processor, based on CAN data collected from a CAN bus included in the vehicle, and not based on the sensor data. Additionally, a determination is made, via the processor, that the actual response time is not within the acceptable response time range. Additionally, a remedial action is caused, via the processor, to be performed in response to the determining that the actual response time is not within the acceptable response time range.