Driver Scoring Using Optimum Path Deviation and Landmarks
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Solution Overview
Problem
Conventional driver scoring techniques are event/alert-based and do not consider data reflecting vehicle movements between events/alerts, lacking detailed characterization of driving behavior.
Innovation Solution
Implement a Safety Driving Model (SDM) that utilizes landmark detection and positional mapping to identify deviations from an optimum driving path, incorporating data from sensors to enhance scoring techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If event/alert-based scoring is used, then the system is simple to implement, but the measurement precision of driving behavior is insufficient
Solution Approach 1:
The patent segments driving behavior into multiple dimensions: event/alert data, sensor data, map data, and computed deviation data. Each dimension is processed separately through specific modules (event data module, sensor data module, map data module, computation module) and then integrated to form a comprehensive driver score, enabling precise characterization without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary computation module that processes sensor data and map data to generate deviation metrics. This intermediary layer transforms raw data from multiple sources into meaningful driving behavior indicators, bridging the gap between simple event-based scoring and complex multi-source data integration
2Quantity of substance
If only event/alert data is used, then the data processing is simple, but the quantity of driving behavior information is insufficient
Solution Approach 1:
The patent merges four distinct data sources: event/alert data, sensor data (accelerometers, gyroscopes, cameras), map data (optimum paths, landmarks), and computed deviation data. Each data source is processed by dedicated modules and then integrated through a scoring module, combining diverse information to comprehensively characterize driving behavior
Solution Approach 2:
The scoring system is designed to universally process multiple data types through a unified architecture. The same computational framework handles event data, sensor data, and map data, with each data type contributing to the overall driver score through standardized processing pipelines, enabling the system to handle diverse data sources efficiently
Data Source
AI summary
Techniques are disclosed to determine driver scoring that consider deviations in map and sensor data in driver score computations. The deviations can be based on deviations from an optimum driving path, and include the determination of vectors between a reference point and one or more landmarks, and one or more reference vectors between the reference point the landmark(s) when traveling on an optimum driving path. A difference between the vectors may then be used determine the deviations from the optimum driving path. In contrast to the conventional approaches, the use of positional deviations (e.g. determined from road markings) in the computation of driver scores allows for improved driver scoring techniques and driver characterizations.