Real-Time Rash Driving Detection via Sensor Augmentation
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Solution Overview
Problem
Current methods for monitoring and analyzing driving styles in on-demand transport services are inadequate as they fail to provide real-time or near real-time feedback, leading to potential accidents and increased vehicle damage due to substandard driving practices.
Innovation Solution
A system and method that utilizes sensor data from vehicles to detect rash driving events in real-time or near real-time through a prediction model trained on augmented sensor outputs, allowing for immediate alerts and driver scoring, without requiring additional hardware on the vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If periodic inspection and feedback collection are performed after ride termination, then implementation complexity is reduced, but real-time detection capability deteriorates
Solution Approach 1:
The system performs preliminary action by collecting and processing sensor data during the ride in real-time, rather than waiting for post-ride inspection. The server receives sensor data from the vehicle and communication device continuously, enabling real-time rash driving event detection and immediate alert notification to the driver, thus preventing catastrophic incidents before they occur
Solution Approach 2:
The patent replaces the mechanical/manual system of periodic inspection with an automated electronic monitoring system. Sensors continuously collect data on vehicle parameters (acceleration, braking, steering), and a server with trained prediction models automatically analyzes this data in real-time to detect rash driving events, eliminating the need for manual post-ride inspections
2Measurement precision
If real-time sensor data collection and analysis are implemented, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary server that acts as a mediator between the vehicle's sensors and the analysis system. The server receives sensor data from the vehicle, processes it through trained prediction models, and generates detection results. This intermediary architecture allows real-time analysis without requiring complex processing hardware in the vehicle itself, thus improving detection precision while managing system complexity
Solution Approach 2:
The system uses copies of sensor data rather than requiring direct complex processing of raw sensor signals. The server creates processed representations of the sensor data through prediction models, analyzing patterns and anomalies in these processed copies rather than dealing with the full complexity of raw sensor inputs in real-time
Data Source
AI summary
A method for augmenting transport services using event detection is provided. The method includes collection of first sensor data generated by various sensors associated with a plurality of vehicles. The first sensor data includes sensor outputs that indicate a plurality of rash driving events. The sensor outputs are augmented based on angular rotation to obtain augmented sensor outputs. A prediction model is trained based on the augmented sensor outputs. Target sensor data associated with a target vehicle is provided as input to the trained prediction model, and based on an output of the trained prediction model an occurrence of a rash driving event is detected in real-time or near real-time. Based on a count of rash driving events associated with the target driver within a cumulative driving distance, a driver score of the target driver is determined.