Driving Monitoring Collision Patterns for Adaptive Danger Assessment
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Solution Overview
Problem
Existing vehicle monitoring systems fail to accurately assess the danger level of collisions with mobile objects, particularly when unanticipated motion changes occur, and do not adequately consider economic or human damage in warning or evasive maneuvers.
Innovation Solution
A driving monitoring device that determines an applicable collision pattern based on velocity vectors, distance, acceleration performance, and time until collision, and calculates a danger level using a weighting factor to account for potential risk and damage considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the driver is alerted frequently or the vehicle is frequently automatically controlled to prevent crashes, then the safety is improved, but the driver's comfort and ease of operation deteriorates
Solution Approach 1:
The system dynamically changes the parameter of danger level thresholds based on traffic conditions. In high-traffic situations, the system adjusts the thresholds to reduce false alerts while maintaining safety, allowing the driver to operate without frequent unnecessary warnings or automatic interventions.
2Measurement precision
If the danger level is calculated based on actual behavior of another vehicle following the behavior of the vehicle, then the measurement precision is improved for predictable scenarios, but the reliability deteriorates when unanticipated motion changes occur
Solution Approach 1:
The system dynamically adapts its calculation model based on the behavior patterns detected. When predictable following behavior is observed, it uses precise trajectory-based calculations. When unanticipated motion changes occur, it switches to a more conservative reliability-based assessment that accounts for unpredictable behaviors, ensuring safety is maintained across varying traffic conditions.
3Productivity
If a minimum necessary velocity change vector is calculated to reduce potential risk level, then the productivity of collision avoidance is improved, but the device complexity increases and economic damage is not considered
Solution Approach 1:
The system segments the collision avoidance problem into distinct risk levels (level 1-4). For each level, different calculation complexities are applied - simpler methods for lower risk levels and more complex velocity change vector calculations only when necessary at higher risk levels. This reduces overall computational complexity while maintaining effective collision avoidance when truly needed.
Solution Approach 2:
The system changes the parameter of calculation intensity based on the assessed risk level. At lower risk levels, basic distance and velocity monitoring suffices. As risk levels increase, the system progressively engages more complex calculations including minimum necessary velocity change vectors, but only when the situation warrants such intensive analysis, balancing computational resources with safety needs.
Data Source
AI summary
A driving monitoring device (100) decides an applicable collision pattern that applies to a collision pattern of a case where a vehicle (200) collides with a mobile object, based on a velocity vector of the vehicle, a velocity vector of the mobile object, and so on. Subsequently, the driving monitoring device calculates a time until collision, which is a time taken until the vehicle collides with the mobile object, in the applicable collision pattern. Then, the driving monitoring device calculates a danger level of an accident in which the vehicle collides with the mobile object, based on the applicable collision pattern and the time until collision.