Modeling Driver Adaptation Patterns During Remote Driving

Xingjian Ma

doi:10.1177/00187208261490983

Modeling Driver Adaptation Patterns During Remote Driving

2026-09-22

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DOI：10.1177/00187208261490983

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Objective To identify driving maneuvers in remote driving under end-to-end latency and examine operator adaptation through within-session maneuver transitions. Background Remote driving systems enable operators to control highly automated vehicles when systems encounter operational limits. End-to-end latency disrupts adaptation and vehicle control, although operators may adjust driving behavior. Research on within-session adaptation clusters and maneuvers emerging under latency remains limited. Method Thirty-six participants completed nine remote driving simulator sessions, navigating construction zones under end-to-end latencies (0–640 ms). Hidden Markov Models identified driving maneuvers from vehicle control data (speed, steering, throttle). Dynamic time warping and K-means clustering classified drives by maneuver-transition sequences. Chi-square tests and ANOVAs examined associations among adaptation clusters, participant-specific differences, experimental conditions, and human-factors measures, while participant-clustered factorial analysis examined maneuver composition. Results Four maneuvers (adapted, transitional, overcompensation, and compensatory) and three adaptation clusters (No adaptation, Rapid adaptation, and Gradual adaptation) emerged. Adaptation clusters were associated with participant-specific differences rather than latency conditions or repeated trials. Rapid adaptation drives were associated with lower workload and higher trust than No adaptation drives and showed a trend toward increasing trust, whereas trust significantly declined in No adaptation cluster. Conclusion Remote driving operators exhibit adaptation clusters characterized by maneuver-transition sequences. Transitional maneuvers reveal intermediate stages. Cluster distributions were more strongly associated with participant-specific differences than tested experimental conditions, with adaptation patterns differing in trust and workload. Application Training programs should assess adaptation capabilities and provide interventions rather than uniform protocols. Early cluster identification enables training on maneuver transitions that facilitate progression toward adaptation.

Modeling Driver Adaptation Patterns During Remote Driving

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