Safety space index (SSI): A two-dimensional metric for quantifying drivers' perceived risk

Area of Science:

• Autonomous Driving Systems
• Human-Computer Interaction
• Cognitive Psychology

Background:

• Advancements in autonomous driving necessitate systems that replicate human decision-making in complex traffic.
• Understanding drivers' cognitive mechanisms is crucial for developing human-aligned autonomous responses.
• Current subjective risk quantification methods are limited, often focusing on objective metrics or one-dimensional analysis.

Purpose of the Study:

• To introduce a novel two-dimensional risk perception metric, the Safety Space Index (SSI).
• To address the limitations of existing methods in comprehensively characterizing subjective risk in dynamic, two-dimensional scenarios.
• To provide a tool for modeling human-like decision-making in intelligent driving systems.

Main Methods:

• Integration of psychological safe space theory and risk field modeling.
• Development of a two-dimensional risk perception metric (SSI).
• Experimental validation through car-following behavior analysis and reaction time calibration.

Main Results:

• SSI improved correlation with car-following behavior by 32.2% and achieved a reaction time calibration of 0.92 s.
• SSI effectively differentiated perceived risk among drivers in identical conflict scenarios, aligning with human cognition.
• SSI demonstrated risk homeostasis, normal distribution of target levels, and robust cross-scenario generalization with an average target level of 0.50.

Conclusions:

• The Safety Space Index (SSI) offers a comprehensive measure of subjective risk perception in two-dimensional driving environments.
• SSI provides critical theoretical support for human-like behavior modeling and autonomous decision-making strategies.
• SSI's adaptability and scalability make it a valuable tool for validating intelligent driving systems.