Driving Style Evaluation Algorithm for Aggressive Maneuver Detection
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Solution Overview
Problem
Existing vehicular feedback systems are inefficient in addressing aggressive driving habits, as they often underestimate certain types of aggressive behavior and require significant computing power, which can lead to increased fuel consumption and environmental emissions.
Innovation Solution
A simplified algorithm that calculates an instantaneous BaseScore for each acceleration maneuver, followed by a ManoeuvreScore and a medium-to-long-term score, with the analysis interval limited by specific events, and incorporates penalizations based on the type of interruption event, reducing computing power requirements while providing effective feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If existing feedback systems use long analysis intervals to evaluate driving behavior, then they reduce computing power requirements, but they underestimate aggressive driving behaviors
Solution Approach 1:
The patent divides the evaluation into hierarchical segments: BaseScore (instantaneous acceleration score), ManoeuvreScore (score for specific acceleration-man braking events), and MeanScore (overall driving style score). This segmentation allows precise detection of aggressive maneuvers while managing computational load through structured processing.
Solution Approach 2:
The system uses periodic evaluation at specific event triggers (acceleration-man braking sequences) rather than continuous monitoring. The MeanScore is updated iteratively at each ManoeuvreScore calculation, providing periodic feedback that balances detection accuracy with reduced computational requirements compared to continuous analysis.
2Measurement precision
If feedback systems provide comprehensive evaluation of driving behavior, then they improve detection accuracy, but they increase computing power requirements and fuel consumption
Solution Approach 1:
The patent extracts and focuses evaluation on specific critical events (acceleration-man braking sequences) rather than analyzing all driving parameters continuously. By concentrating computational resources on identifying and evaluating these specific manoeuvres, the system achieves high detection accuracy for aggressive behavior while minimizing overall computing power consumption.
Solution Approach 2:
The system changes evaluation parameters dynamically based on driving context, using different scoring thresholds and criteria for BaseScore and ManoeuvreScore calculations. This allows adaptive evaluation that maintains high detection accuracy for aggressive maneuvers while adjusting computational intensity based on actual driving conditions.
3Reliability
If the system calculates detailed scores for each maneuver, then it improves feedback reliability, but it increases device complexity
Solution Approach 1:
The patent segments the scoring algorithm into distinct hierarchical levels (BaseScore calculation from acceleration data, ManoeuvreScore calculation from acceleration-man braking sequences, and MeanScore as iterative average). This segmentation makes the complex evaluation process more manageable and implementable while maintaining high feedback reliability through structured multi-stage assessment.
Data Source
Figure 1
AI summary
Method for evaluating (feedback) a vehicular driving style comprising the following steps performed in sequence: determination of a BaseScore relative to an acceleration manoeuvre, as a function of a mean position of a vehicle accelerator pedal, interruption of said observation interval on the basis of an interruption event, in which said interruption event can be of two or more types, calculation of a ManoeuvreScore, as a function of a value of said BaseScore and the type of event, out of said two or more types of event, which has caused said interruption of said observation interval.