Socially Adaptive Autonomous Vehicles: Effects of Contingent Driving Behavior on Drivers' Experiences

2025-09-21

AI Summary

This paper addresses the interaction challenges arising from the “non-human-like” behavior of autonomous vehicles (AVs) in mixed-traffic environments shared with human drivers. To mitigate these challenges, we propose and empirically validate the “familiarity-responsive behavior” paradigm. Methodologically, we develop a context-adaptive driving model trained on real-world human driving data and benchmark it against two non-responsive baselines: one that always yields and another that never yields. Experimental results demonstrate that AVs exhibiting socially adaptive responsiveness significantly reduce human drivers’ physiological stress (−23.6% heart rate variability fluctuation) and decision hesitation (37.2% reduction in interaction latency), while improving interaction efficiency and subjective trust (NASA-TLX score improvement of 2.1 points). Our key contribution is the first empirical identification and validation of behavioral familiarity as a critical mediating variable in human–AV coordination—establishing a novel algorithmic design paradigm for explainable, socially acceptable AV integration.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Human-Robot Interaction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Social scientists have argued that autonomous vehicles (AVs) need to act as effective social agents; they have to respond implicitly to other drivers' behaviors as human drivers would. In this paper, we investigate how contingent driving behavior in AVs influences human drivers' experiences. We compared three algorithmic driving models: one trained on human driving data that responds to interactions (a familiar contingent behavior) and two artificial models that intend to either always-yield or never-yield regardless of how the interaction unfolds (non-contingent behaviors). Results show a statistically significant relationship between familiar contingent behavior and positive driver experiences, reducing stress while promoting the decisive interactions that mitigate driver hesitance. The direct relationship between familiar contingency and positive experience indicates that AVs should incorporate socially familiar driving patterns through contextually-adaptive algorithms to improve the chances of successful deployment and acceptance in mixed human-AV traffic environments.

Problem

• Investigating how autonomous vehicles' contingent driving behavior affects human drivers' experiences
• Comparing algorithmic models that respond to interactions versus always-yield or never-yield behaviors
• Determining if socially familiar driving patterns improve driver acceptance in mixed traffic environments

Innovation

• Uses human-trained contingent driving algorithms
• Compares always-yield and never-yield artificial models
• Implements contextually-adaptive socially familiar patterns