Cooperative Overtaking: Overcoming Automated Vehicles' Obstructed Sensor Range via Driver Help
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This paper addresses the challenge of automated vehicles (AVs) encountering scenarios where sensor ranges are obstructed, such as when a slower vehicle blocks the view of oncoming traffic during an overtaking maneuver. While shifting control back to the human driver is a common solution, it introduces significant human-factor risks, including reduced situation awareness and post-automation behavioral issues. The authors propose "cooperative overtaking," a hybrid approach where the driver assists the vehicle by approving maneuvers based on their superior visual perception, while the system retains control over steering and execution. The study aims to evaluate the feasibility of this cooperation and compare two touchscreen interaction techniques for maneuver approval and cancellation: a "CLICK" method (click to approve, click again to cancel) and a "HOLD" method (hold to approve, lift finger to cancel). The researchers conducted a driving simulator study with 32 participants using a fixed-base simulator equipped with eye-tracking technology. The experimental design was a within-subjects study in Part I, where participants experienced both interaction techniques under low and high cognitive load conditions induced by a non-driving-related task (NDRT), specifically an n-back task. In Part II, participants were unexpectedly challenged with oncoming traffic (contraflow) shortly after approving a maneuver to test safety behaviors and cancellation responsiveness. The system was designed to allow both the driver and the vehicle to cancel the maneuver if oncoming traffic was detected during the lane change. Data collected included System Usability Scale (SUS) scores, Driving Activity Load Index (DALI) for workload, maneuver approval times, and eye-glance patterns to assess monitoring behavior. The results indicated that cooperative overtaking is a feasible concept, with both interaction techniques demonstrating good usability. However, the CLICK system received significantly higher usability scores than the HOLD system in scenarios without oncoming traffic. Crucially, the HOLD technique facilitated faster cancellation maneuvers, leading to safer outcomes when errors occurred. Glance analysis revealed that while most participants responsibly monitored the traffic scene before approving maneuvers, complex situations and high cognitive loads reduced safety precautions, such as checking rear mirrors. Furthermore, participants approved maneuvers significantly later when oncoming traffic was present, indicating some level of situational awareness, though erroneous approvals still occurred. The significance of this work lies in demonstrating that driver-vehicle cooperation can effectively overcome specific AV sensor limitations without requiring full manual control handovers. The findings suggest that while cooperation is viable, it requires adaptive systems to monitor driver state and ensure comprehensive scene checking, particularly under cognitive load. The study highlights that interaction design impacts safety, with the HOLD method offering advantages in cancellation speed despite slightly lower overall usability ratings. This approach offers a practical middle ground between fully automated and manual driving, enhancing efficiency and safety in edge cases where automation alone is insufficient.
Key finding
Cooperative overtaking is a feasible strategy for overcoming automated vehicle sensor limitations, with the hold-to-approve interaction technique offering safer maneuver cancellation despite the click technique receiving higher usability ratings.
Methodology
simulator
Sample size: 32
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 unpaywall_oa_fetch on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | canonical_url | — | — | 8 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-07 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- automation
- automation surprise
- ehmi external hmi
- automation complacency bias
- trust calibration
- situational awareness
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Theoretical Contribution: conceptual framework