Automated Driving: A Literature Review of the Take over Request in Conditional Automation

Morales-Alvarez, Walter; Sipele, Oscar; Léberon, Régis; Tadjine, Hadj Hamma; Olaverri-Monreal, Cristina · 2020 · Crossref

DOI: 10.3390/electronics9122087

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This literature review addresses the critical challenges associated with Take Over Requests (TOR) in conditional automation (SAE Level 3), where automated driving systems (ADS) must safely transfer control back to human drivers when exiting their Operational Design Domain (ODD) or encountering emergencies. The study is motivated by the complexity of this transition, particularly the risk that drivers engaged in non-driving related tasks (NDRT) may exhibit delayed reaction times and reduced situational awareness. The authors aim to synthesize existing research on human-machine interfaces (HMI), driver monitoring, and control transfer mechanisms to provide theoretical foundations and design guidelines for practitioners. The paper categorizes TOR complexity into objective and subjective factors. Objective complexity involves external conditions such as traffic density, road geometry, and environmental constraints. The review finds that high traffic density increases collision risk and extends the time required to regain control, while curved roads negatively impact performance by increasing driver reaction time, lateral deviation, and abrupt deceleration. Subjective complexity pertains to internal driver states, including vigilance, cognitive load, and trust. The authors note that reaction times do not immediately return to baseline after distraction, with residual effects lasting up to 27 seconds for highly distracting tasks. To assess driver readiness, the review examines Driver Monitoring Systems (DMS) that utilize steering wheel torque, eye-tracking, and facial expression analysis, though it highlights privacy concerns and the potential for users to cheat less obtrusive sensors. Regarding HMI design, the paper evaluates visual, auditory, and haptic modalities. Visual displays offer clear information but may be missed by distracted drivers; auditory signals are effective for urgency but lack explicit detail; and haptic feedback reduces cognitive workload but conveys limited information. The review concludes that multimodal combinations—integrating acoustic, visual, and vibrotactile cues—are most effective for improving driver perception of urgency and response quality. Furthermore, shared control systems that provide haptic guidance during the transition phase were found to decrease lateral error and enhance driving comfort compared to abrupt handovers. The significance of this work lies in its comprehensive synthesis of the factors influencing safe TOR execution, highlighting the need for adaptive, multimodal interfaces and robust driver monitoring. The authors identify research gaps, particularly regarding the optimal design of multimodal messages and the legal frameworks surrounding liability in Level 3 automation. They recommend that future systems prioritize intuitive support for disengagement, prevent unintentional system overrides, and ensure lateral control is only released once the driver has firmly regained steering control. This review serves as a foundational resource for designing safer conditional automation systems by addressing the intricate interplay between system capabilities and human cognitive limitations.

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.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• takeover transitions
• automation
• automation surprise
• driverless ads
• hands on hands off engagement
• 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).

• Methodological Resource: measurement protocol, tool software
• Theoretical Contribution: conceptual framework