Automated Driving MRM Trigger Logic for Hazard Response
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Solution Overview
Problem
Current automated driving systems lack specific trigger conditions for initiating a Minimum Risk Maneuver (MRM), which is essential for ensuring safe and efficient dynamic driving operations.
Innovation Solution
The proposed method and device for automated driving systems determine whether a Minimum Risk Maneuver (MRM) is needed based on trigger conditions, including receiving MRM activation requests from infrastructure or neighboring vehicles, and controlling the vehicle to a Minimum Risk Condition (MRC) based on event information related to vehicle accidents or structure collapses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specific trigger conditions for MRM are defined, then the reliability of automated driving system is improved, but the device complexity increases
Solution Approach 1:
The trigger condition determination is segmented into multiple independent evaluation modules: infrastructure event information reception, neighboring vehicle event information reception, autonomous vehicle sensor detection, and threshold comparison. Each module handles a specific aspect of trigger condition evaluation, making the overall system more manageable and reliable despite the increased complexity of having multiple defined triggers.
2Reliability
If multiple trigger conditions are monitored, then the safety of automated driving is improved, but the loss of time for processing increases
Solution Approach 1:
The system performs preliminary actions by pre-defining threshold values for various parameters (obstruction area ratio, obstruction height, sensor detection thresholds) before actual operation. This allows the system to quickly compare real-time sensor data against pre-established criteria without performing complex calculations during critical moments, thus improving safety through comprehensive monitoring while minimizing processing time delays.
3Measurement precision
If comprehensive event information is collected from infrastructure and neighboring vehicles, then the measurement precision of hazardous situations is improved, but the loss of information processing increases
Solution Approach 1:
The system extracts only the essential and relevant features from the comprehensive event information received from infrastructure and neighboring vehicles. Specifically, it extracts key parameters such as obstruction area ratio, obstruction height, and vehicle position data, while filtering out redundant information. This extraction approach maintains high measurement precision for hazard detection while significantly reducing the information processing load on the system.
Data Source
AI summary
A method for automated driving minimum risk maneuver and a device therefor are provided. The method includes performing a dynamic driving task (DDT) by an automated driving system (ADS), determining, by the ADS, whether a minimum risk maneuver (MRM) is needed, based on a trigger condition for the MRM, and controlling a subject vehicle (SV) to a minimum risk condition (MRC) by the ADS.