AEB Driver Behavior Scoring for Adaptive Vehicle Countermeasures
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Solution Overview
Problem
Existing AEB systems fail to consider individual driving behaviors and behaviors of other vehicles, leading to inadequate activation of vehicle countermeasures.
Innovation Solution
A system that measures and scores driving behavior through sensors and an electronic processor, adjusting vehicle countermeasures based on weighted attributes and thresholds to enhance safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AEB systems activate braking based on standard detection algorithms, then collision avoidance is achieved, but the system cannot adapt to individual driver behaviors and behaviors of other vehicles
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and storing driving behavior data before actual AEB activation is needed. Sensors collect information about driver responses to potential hazards, and this data is stored in memory for later analysis. This preliminary data collection enables the system to adapt to individual driver behaviors when activation conditions are met, without adding complexity to the real-time decision-making process.
Solution Approach 2:
The system implements feedback mechanisms by analyzing sensor data about driver behavior during potential collision scenarios and using this information to adjust future AEB activation decisions. The electronic processor evaluates whether the driver responded appropriately to previous hazard detections, and this feedback loop allows the system to learn and adapt to individual driving patterns over time, making the AEB system more versatile without significantly increasing complexity.
2Reliability
If AEB systems use basic activation thresholds, then system operation is simple, but vehicle countermeasures are not adequately adjusted to driver behavior patterns
Solution Approach 1:
The system segments the evaluation of driver behavior into multiple independent attributes, each scored separately. Instead of using a single complex threshold, the electronic processor divides driver responses into distinct categories (e.g., reaction time, braking smoothness, steering control) and assigns scores to each attribute independently. This segmentation approach improves reliability by comprehensively evaluating driver behavior while keeping the complexity manageable through modular scoring of individual attributes.
Solution Approach 2:
The system changes parameters by dynamically adjusting AEB activation thresholds based on scored driver behavior attributes. Rather than using fixed thresholds, the electronic processor modifies activation criteria according to the accumulated scores of driver performance across multiple attributes. This parameter change approach enhances the reliability and effectiveness of collision avoidance by adapting to actual driver behavior patterns while maintaining a structured scoring framework.
Data Source
AI summary
A system for controlling a vehicle using driving behavior involving automatic emergency braking (AEB) events. The system includes a plurality of sensors and an electronic processor. The system receives event data of an attribute of a driving behavior of a user related to handling an AEB event of a vehicle from the plurality of sensors. The system assigns a weighted value to the attribute based on a set of conditions for the attribute and the event data. The system determines an event score of the driving behavior related to the AEB event based on the weighted value of the attribute. The system determines a user driving behavior score based on the event score associated with one or more AEB events handled by the user. The system activates a vehicle countermeasure related to attributes responsive to determining the driving behavior score of the user is greater than a driving behavior threshold.