Vehicle Collision Severity Reduction via Criticality Zone Segmentation
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Solution Overview
Problem
The increasing presence of machines in industrial settings leads to heightened safety risks due to collisions between humans, machines, and objects, which existing technologies fail to adequately address in terms of predicting and mitigating the severity of impending collisions.
Innovation Solution
A system and method utilizing sensor data to determine impending collisions between vehicles and objects, adjusting operational controls to change the vehicle's travel path and reduce the severity of collisions by determining expected locations, velocities, masses, and criticality maps, and autonomously controlling the vehicle to avoid high-risk impact zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing sensor technologies are used for collision detection, then collision detection capability is provided, but the severity of impending collisions cannot be adequately mitigated
Solution Approach 1:
The system segments the collision risk assessment by dividing the environment into multiple criticality zones (high, medium, low) based on potential damage severity. This segmentation allows the vehicle control system to prioritize avoidance actions toward high-criticality zones while accepting impacts in low-criticality zones, thereby improving collision mitigation effectiveness without requiring complete avoidance of all obstacles.
Solution Approach 2:
The system performs preliminary assessment of criticality zones before the collision occurs by analyzing environmental data, object properties, and vehicle trajectory in advance. This preliminary action enables the control system to pre-determine the optimal collision avoidance strategy, adjusting operational controls proactively rather than reactively, which improves mitigation effectiveness while maintaining reasonable system complexity.
2Reliability
If the vehicle autonomously controls to avoid all objects, then collision avoidance is maximized, but the vehicle's productivity and operational efficiency deteriorate
Solution Approach 1:
The system applies different avoidance strategies to different spatial zones based on their criticality. High-criticality zones trigger active avoidance maneuvers, while low-criticality zones allow the vehicle to maintain its original trajectory. This localized quality approach ensures collision avoidance capability is maximized where necessary while preserving operational efficiency in non-critical areas.
Solution Approach 2:
The system performs partial avoidance action by selectively avoiding only the high-criticality zones rather than all objects. This partial action is sufficient to achieve acceptable safety levels while minimizing disruptions to the vehicle's operational efficiency and productivity.
3Measurement precision
If criticality maps with multiple zones are implemented, then collision severity prediction is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The environment is segmented into discrete criticality zones (high, medium, low) based on simplified criteria such as object type, mass, and location relative to the vehicle. This segmentation provides sufficiently accurate collision severity assessment while avoiding the need for complex continuous field calculations, thereby balancing measurement precision with data processing complexity.
Data Source
AI summary
Systems for collision avoidance for a vehicle. One or more inputs are used to determine an impending collision. Once determined, corrective actions are taken to reduce the severity of the collision. The corrective actions can avoid the collision and/or reduce the damage caused by the collision. The systems and methods can be performed at the vehicle based on data available to a control unit in the vehicle. The systems and methods can also be performed at a system level that controls one or more vehicles and/or objects.