Allusion 2: External Communication for SAE L4 Vehicles

Rossi-Alvarez, Alexandria I; Klauer, Charlie; Schaudt, Andy; Doerzaph, Zac · 2023 · ROSA P / Safety through Disruption (Safe-D) University Transportation Center (UTC)

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Summary

This study addresses the critical safety challenge of external communication between SAE Level 4+ Automated Driving Systems (ADS) and vulnerable road users, particularly pedestrians. As highly automated vehicles lack human drivers, they lose the nonverbal cues (e.g., eye contact, gestures) that pedestrians currently rely on to interpret vehicle intent. While prior research has utilized simulators to evaluate single-vehicle external human-machine interfaces (eHMIs), this research investigates pedestrian behavior in real-world conditions involving multiple ADSs simultaneously. The primary objective was to determine how the presence, color, and complexity of eHMIs influence pedestrian crossing decisions when multiple automated vehicles compete for attention. The researchers conducted a within-subject experiment at the Virginia Tech Transportation Institute’s Smart Roads facility. Forty participants (aged 18–65) acted as pedestrians, using a "decision-making box" to indicate their willingness to cross without physically entering the roadway. Two vehicles emulated SAE L4+ ADSs using seat-suits to disguise human operators. The vehicles displayed LED light bars communicating two states: "drive" (uniformly lit) and "yield" (flashing inward). The study manipulated light bar colors (white vs. amber) and tested four complex traffic scenarios, including right turns and mid-block crossings, with speeds up to 35 mph. Participants experienced 12 trials per scenario, including conditions where eHMIs were active, inactive, or mixed. A subset of participants were active law enforcement officers to assess professional perspectives on light bar colors. The results indicated that the presence and specific condition of eHMIs did not significantly influence participants' willingness to cross. Pedestrians struggled to focus on eHMIs when multiple vehicles were present, typically attending only to the vehicle nearest to them or most detrimental to their path. While scenario complexity caused participants to make more cautious decisions, it did not alter their fundamental willingness to cross. Furthermore, there were no statistically significant differences in crossing decisions based on participant age or gender. Qualitative data and analysis of pattern recognition revealed that participants found it difficult to notice and correctly interpret the eHMI patterns amidst competing visual stimuli. Law enforcement officers provided specific feedback on color preferences, noting potential conflicts with existing traffic signal conventions. The study concludes that current eHMI designs, even those with distinct patterns, may be insufficient for complex traffic environments involving multiple automated vehicles. The findings suggest that eHMIs require further simplification to be effectively interpreted by pedestrians in real-world conditions. The research highlights a gap between the technical implementation of vehicle intent communication and the cognitive limitations of road users in dynamic settings. These implications underscore the need for refined design principles that prioritize clarity and reduce cognitive load, ensuring that external communication systems can reliably support safe interactions between automated vehicles and vulnerable road users.

Key finding

The presence and condition of external human-machine interfaces on multiple automated vehicles did not influence participants' willingness to cross the street.

Methodology

on_road

Sample size: 40

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.

Topics

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• ehmi external hmi
• vru facing ehmi
• signaling behavior
• driver vru interaction
• pedestrian behavior perception
• situational awareness

Information type

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• Applied Guidance: design guidelines
• Methodological Resource: tool software