Mobile Sensor Fusion for Battery-Efficient Vehicle Braking Detection
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Solution Overview
Problem
Existing techniques for detecting vehicle braking events in mobile devices are not power-efficient and do not effectively notify nearby vehicles, and existing systems do not provide comprehensive analysis of sensor data from both mobile devices and vehicles to enhance safety.
Innovation Solution
Collect sensor data from mobile devices within a vehicle using a polling frequency based on vehicle speed, battery status, traffic, and weather conditions, and apply machine learning algorithms to detect braking events, issuing notifications to nearby vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous sensor data collection is performed at high polling frequency to improve braking event detection accuracy, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the polling frequency of sensor data collection based on current driving conditions, vehicle speed, and detected event types. During normal driving, a lower polling frequency conserves battery power, while during critical events or high-speed conditions, the frequency increases to improve detection accuracy.
Solution Approach 2:
The system changes operational parameters (polling frequency, sensor activation state) based on contextual factors such as vehicle speed, battery charge level, traffic conditions, and weather. This allows the system to optimize between power consumption and detection accuracy by adapting to varying operational conditions.
2Reliability
If comprehensive sensor data is collected and transmitted continuously to improve braking event detection reliability, then reliability is improved, but data transmission energy consumption increases
Solution Approach 1:
The system extracts and transmits only the essential data elements needed for braking event detection and analysis, rather than continuously transmitting all sensor data. This reduces transmission energy consumption while maintaining detection reliability by focusing on critical information.
Solution Approach 2:
The system performs partial data collection and transmission during normal operation, collecting only sufficient data to maintain reliable detection without the excess energy consumption of continuous full-data transmission. Additional data collection is activated only when needed for specific event types or conditions.
3Speed
If high polling frequency is used to detect braking events in real-time, then speed of detection is improved, but power consumption increases
Solution Approach 1:
The system uses periodic sensor data collection at variable intervals rather than continuous high-frequency sampling. The polling frequency is adjusted periodically based on driving conditions, maintaining adequate detection speed while reducing overall power consumption during extended monitoring periods.
4Adaptability or versatility
If the system monitors multiple environmental factors (traffic, weather, battery status) to optimize polling frequency, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system uses a multi-functional approach where a single control mechanism manages multiple factors (battery status, traffic conditions, weather, vehicle speed) to determine polling frequency. This universal control structure improves adaptability without proportionally increasing complexity, as the same decision-making framework handles all input factors.
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
Enhances power efficiency and effectively detects braking events, improving safety by notifying nearby vehicles, thereby promoting driver awareness and reducing potential collisions.
Implementation Method 1
The sensors may comprise a GPS receiver, an accelerometer, a gyroscope, and the like
Data Source
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AI summary
One or more braking event detection computing devices and methods are disclosed herein based on fused sensor data collected during a window of time from various sensors of a mobile device found within an interior of a vehicle. The various sensors of the mobile device may include a GPS receiver, an accelerometer, a gyroscope, a microphone, a camera, and a magnetometer. Data from vehicle sensors and other external systems may also be used. The braking event detection computing devices may adjust the polling frequency of the GPS receiver of the mobile device to capture non-consecutive data points based on the speed of the vehicle, the battery status of the mobile device, traffic-related information, and weather-related information. The braking event detection computing devices may use classification machine learning algorithms on the fused sensor data to determine whether or not to classify a window of time as a braking event.