Driving Through the Network: Performance and Workload Under Latency and Video Impairment

2026-05-15

AI Summary

The impact of network latency and video quality on operator workload and driving performance in remote driving remains poorly understood, limiting system reliability. This study investigates the effects of systematic variations in control latency (100/300 ms) and video bitrate (500/2000 kbit/s) through a driving simulation experiment, integrating multimodal physiological measures—including eye-tracking, electrocardiography, and electrodermal activity—to assess cognitive load and driving performance. Results indicate that a 300 ms latency combined with a 2000 kbit/s bitrate achieves speed performance equivalent to ideal conditions. Furthermore, physiological metrics reveal sub-additive interaction effects, supporting the development of a physiology-based, proactive overload warning mechanism. These findings provide both theoretical grounding and a technical pathway for optimizing remote driving system design.

Abstract

Teleoperation promises to extend the operational envelope of automated vehicles, yet it critically depends on network latency and video quality. We report a fixed-base driving-simulator study (N=25) with a 2x2 manipulation of added latency (100/300 ms) and bitrate (500/2000 kbit/s), plus a best-case baseline (0 ms added, 9000 kbit/s). We measured effective glass-to-glass (G2G) latency per condition (baseline approx. 413 ms; effective totals approx. 500-700 ms) and verified stable framerate and encoder settings. Multimodal measures covered performance (speed, steering reversals, crashes), oculomotor behavior (blink rate, fixation duration), physiology (RR interval, heart rate, skin conductance), and subjective workload. Latency and bitrate each increased operator load and modestly affected performance. Physiological measures (heart rate, RR interval) exhibited sub-additive interactions, whereas performance and oculomotor interactions were small or non-significant. Equivalence tests showed that 300 ms with 2000 kbit/s was velocity-equivalent to best-case (SESOI +/- 2 km/h), while 300 ms with 500 kbit/s was not. We argue that latency and video quality should be treated as largely independent design levers, and that physiology-aware adaptation can anticipate overload before safety is compromised.

Problem

• teleoperation
• network latency
• video quality
• workload
• driving performance

Innovation

• teleoperation
• network latency
• video bitrate
• physiological adaptation
• multimodal assessment