Vehicle Collision Characterization Using Multi-Sensor Context
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Solution Overview
Problem
Conventional collision detection systems using accelerometer data are unreliable due to high rates of false positives and false negatives, as they cannot distinguish between actual collisions and non-collision events such as kicks, bumps, or road surface features, and lack sufficient contextual information for accurate decision-making.
Innovation Solution
A system that analyzes a combination of sensor data from accelerometers, GPS, engine diagnostics, and image/video data to determine the likelihood of a collision by evaluating data before, during, and after a potential collision event, using machine learning techniques and rule-based criteria to classify events as collisions or non-collisions, and adjusts for factors like road surface features and driver behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional accelerometer-based collision detection is used, then the system is simple to implement, but the reliability of collision detection is poor due to high false positive and false negative rates
Solution Approach 1:
The patent combines multiple sensing modalities (accelerometers, GPS, engine diagnostics, image/video cameras, microphones) into an integrated collision detection system. By merging these different data sources, the system achieves more reliable collision detection than any single sensor could provide alone, resolving the contradiction between reliability and complexity through synergistic integration.
Solution Approach 2:
The system uses a multi-functional approach where a single detection platform performs multiple functions: accelerometers detect impact forces, GPS tracks location and speed changes, cameras capture visual evidence, and microphones record audio signals. This multi-functionality allows the system to improve reliability across various collision scenarios without requiring separate specialized systems for each detection method.
2Measurement precision
If multiple sensor data sources are combined for collision analysis, then the measurement precision of collision characterization is improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex data processing task into distinct analytical components: acceleration pattern analysis, GPS trajectory evaluation, image recognition for collision evidence, and audio signal processing. Each segment handles a specific aspect of collision characterization, improving measurement precision while making the overall complex system more manageable through modular processing.
Solution Approach 2:
The system introduces intermediary processing layers that integrate and reconcile data from multiple sensors. These intermediaries correlate timing information across sensors, match spatial coordinates from GPS with visual data from cameras, and synchronize audio-visual streams, thereby improving measurement precision while managing data processing complexity through structured integration.
3Reliability
If contextual information from before, during, and after the event is analyzed, then the reliability of collision determination is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing sensor data during normal vehicle operation before a collision occurs. Baseline acceleration patterns, GPS trajectories, and system states are established in advance, allowing the system to quickly compare post-collision data against pre-established norms, thereby improving reliability while minimizing processing time delays.
Solution Approach 2:
The system implements feedback mechanisms where data from the immediate post-collision period is rapidly analyzed and fed back to confirm or refute collision hypotheses. This iterative feedback process allows the system to quickly converge on a reliable determination by focusing computational resources on the most critical time windows, balancing reliability improvement with acceptable processing time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces false detection rates by considering a broader range of data points and contextual information, improving the accuracy and reliability of collision detection and characterization.
Implementation Method 1
obtain acceleration data including acceleration values for the vehicle at one or more times
Data Source
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AI summary
Described herein are various techniques, including systems and non-transitory instructions, that, in response to obtaining information regarding a potential collision between a vehicle and an object, obtain data describing the vehicle for a time period extending before and after a time of the potential collision. The system may determine a likelihood that the potential collision is a non-collision event based on the data describing the vehicle by performing one or more assessments. The assessments may include telematics monitor assessment, driver behavior assessment, road surface feature assessment, trip correlation assessment, and/or context assessment. In response to determining that the likelihood indicates that the potential collision is not a non-collision event, the system may trigger one or more actions responding to the potential collision.