Abnormal Driving Assessment Using Sensor Deviation
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Solution Overview
Problem
Existing methods for assessing abnormal driving behavior rely on on-board cameras and extensive image processing, which are computationally intensive and do not account for neighboring vehicles, limiting their effectiveness in preventing road accidents.
Innovation Solution
A method and system that utilize position and motion information from sensors to calculate deviation values relative to reference values, such as path estimates or speed limits, without requiring on-board cameras, and consider neighboring vehicles to assess abnormal driving behavior by iteratively monitoring deviations and triggering safety improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If on-board cameras and image processing are used to detect lane markings and calculate vehicle deviations, then measurement precision of driving behavior is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent extracts the essential measurement function from complex image processing systems. Instead of using cameras to capture and process visual information about lane markings and vehicle positions, the invention directly extracts position and motion data from simpler sensors (GPS, accelerometers, gyroscopes) to calculate deviation from reference paths, thereby achieving detection precision with reduced system complexity
Solution Approach 2:
The patent replaces the optical-mechanical image processing system with a computational system based on sensor data fusion. Rather than using cameras to visually detect lane markings and compute deviations through image analysis, the invention substitutes this with electronic sensors that directly provide position and motion parameters for computational deviation assessment
2Reliability
If on-board cameras and real-time image processing are implemented to monitor driving behavior, then detection capability is improved, but use of energy and computational resources increase
Solution Approach 1:
The patent extracts only the necessary position and motion parameters from the environment using low-power sensors, rather than continuously processing visual images. By taking out only the essential data (vehicle position, speed, acceleration, orientation) needed for deviation calculation, the system maintains detection capability while dramatically reducing computational energy consumption
Solution Approach 2:
The patent uses inexpensive, low-power sensors (GPS receivers, accelerometers, gyroscopes) that consume minimal energy compared to camera systems with real-time image processing. These simple sensors provide sufficient data for deviation assessment without the high computational energy requirements of visual processing systems
3Device complexity
If traditional methods only monitor own vehicle parameters without considering neighboring vehicles, then device complexity is reduced, but measurement precision of abnormal driving behavior decreases
Solution Approach 1:
The patent makes the deviation assessment system universal by enabling it to evaluate multiple vehicles simultaneously. The same sensor suite and computational algorithm used for monitoring the host vehicle are also applied to neighboring vehicles detected by the sensor system, allowing multi-vehicle abnormal behavior assessment without proportionally increasing device complexity
Solution Approach 2:
The host vehicle's sensor system serves multiple functions: it monitors the host vehicle's own deviation from its reference path while simultaneously detecting and tracking neighboring vehicles and assessing their deviation from their respective paths. This self-service capability allows the single system to perform multiple assessment functions without requiring separate dedicated systems for each vehicle
Data Source
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AI summary
The invention concerns a method for assessment of abnormal driving behaviour to improve driving safety of a vehicle travelling on a road, using position information and motion information about said vehicle and/or at least one neighbouring vehicle travelling in the proximity of said vehicle on said road. The method comprises a step of obtaining and memorizing (40, 42) a plurality of position information items and motion information items, each information item corresponding to a measuring time instant of an observation time interval and a step of obtaining (44) at least one estimate of the reference value for said vehicle and/or its at least one neighbouring vehicles travelling on said road during the observation time interval. Next, the sets of deviation values are computed (46), each deviation value being computed using a memorized information item corresponding to a measuring time instant and the at least one estimate of the reference value and the quantities of deviation are obtained from the corresponding sets of deviation values. If a quantity of deviation among said quantities of deviation is greater than a predetermined threshold for the corresponding driving behaviour, the method implements (52) a measure for safety improvement.