Driving-Behavior Evaluation With Dynamic Training-Target Areas
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Solution Overview
Problem
Conventional safe driving evaluation systems struggle to evaluate driving behavior effectively when the vehicle is outside the predefined evaluation area, making it difficult to perform appropriate assessments.
Innovation Solution
A driving-behavior evaluation system that acquires external environment and traveling information to identify a training-target area suitable for evaluation, using sensors like cameras, radars, and LIDAR, and evaluates driving behavior based on these data, issuing notifications for specific driving issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional safe driving evaluation system uses a predefined evaluation area, then the system structure is simple, but the system cannot evaluate driving behavior when the vehicle is outside the predefined area
Solution Approach 1:
The evaluation area is transformed from a static predefined region to a dynamic training-target area that moves and adjusts based on the driver's movement history. The system continuously updates the training-target area candidate based on where the driver has previously exhibited problematic driving behaviors, allowing the evaluation area to adapt dynamically to the driver's actual driving patterns and locations.
Solution Approach 2:
The system uses the driver's own movement history and driving behavior data to automatically generate and update the training-target area candidate without requiring external input or manual configuration. The driver's past behavior serves as the basis for identifying future evaluation areas, making the system self-adapting and eliminating the need for predefined areas.
2Measurement precision
If the system evaluates all driving items, then comprehensive evaluation is achieved, but notification overload occurs for items not requested by the driver
Solution Approach 1:
The notification system is made selective rather than uniform. Notifications are issued only for driving evaluation items that match both the driver's requested evaluation items and the actual problematic behaviors detected in the training-target area. This localized notification approach prevents overload by filtering out irrelevant notifications while maintaining comprehensive evaluation capabilities.
Solution Approach 2:
The system implements selective feedback by comparing the driver's requested evaluation items with the actual driving problems detected. Notifications are generated only when there is a match between what the driver wants to be evaluated on and what problems are actually observed, creating a targeted feedback loop that avoids information overload.
3Measurement precision
If the system uses multiple sensors (cameras, radars, LIDAR) to acquire external environment information, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Multiple sensor types (cameras, radars, LIDAR) are merged into a unified external environment information acquisition system. The system integrates data from these different sensors to create a comprehensive view of the external environment, allowing the vehicle to be evaluated in various weather and lighting conditions while maintaining a coordinated sensor architecture.
Data Source
AI summary
A driving-behavior evaluation system includes an external-environment-information acquisition unit, a traveling-information acquisition unit, and a processor. The processor is configured to execute a program and thereby evaluate driving behavior of a driver of a vehicle based on information acquired by the external-environment-information acquisition unit and the traveling-information acquisition unit. The processor is configured to receive a request for a driving evaluation item of evaluating the driving behavior; set a training-target-area candidate for a traveling-target area for training based on the driving evaluation item and a movement history of a driver of the vehicle; and identify a training-target area, for which training is requested, from the set training-target-area candidates based on the driving evaluation item received by the processor.