An enhanced vehicle control model for assessing highly automated driving safety

Monkhouse, Helen E.; Habli, Ibrahim; McDermid, John · 2020 · Crossref

DOI: 10.1016/j.ress.2020.107061

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 limitations of existing automotive safety models, specifically the MISRA Vehicle Control Model (VCM) and ISO 26262 risk assessments, which assume the driver is always situationally aware and integral to vehicle control. The authors argue that these assumptions are invalid for Highly Automated Driving (HAD) systems, where control and situational awareness are shared between human drivers and machine automation. The research aims to develop an Enhanced Vehicle Control Model that accounts for the distributed nature of the driving task, joint cognition, and the influence of external data sources, thereby enabling more effective hazard analysis for automated systems. To achieve this, the authors constructed the Enhanced VCM by integrating Michon’s Hierarchical Control Model (HCM) with concepts from situational awareness theory. The model structures driving tasks into three hierarchical levels: Strategy (long-term planning), Manoeuvring (real-time rule-based tasks), and Control (millisecond-level skill-based actions). It explicitly represents the "joint cognitive system" by modeling how both human and machine agents perceive the environment via sensing and mental models, and how errors in perception or information transfer can lead to hazards. The model also incorporates external inputs from infrastructure and the cloud, moving beyond the single-vehicle perspective of traditional models. The authors evaluated the Enhanced VCM’s utility by applying it to three Advanced Driver Assistance Systems (ADAS) with increasing automation levels: Adaptive Cruise Control (ACC, SAE Level 1), ACC with Lane Centring (SAE Level 2), and Traffic Jam Assist. Using a Hierarchical Task Analysis of Driving (HTAoD) taxonomy, they mapped specific driving tasks to the model’s control levels and assigned responsibility for "doing," "monitoring," and "safety" to either the human or the machine. This scenario-based evaluation demonstrated how the model can identify specific hazard causes arising from shared control, such as discrepancies in situational awareness between the driver and the automation, or failures in handover protocols. The study concludes that the Enhanced VCM provides a conceptual framework capable of proactively identifying hazard causes associated with joint cognitive control in HAD systems. It highlights that traditional controllability metrics are insufficient for automated contexts, as automation alters the driver’s ability to perceive and react to hazards. While the model shows promise for guiding hazard analysis, the authors note that an accompanying methodology is required to make it a practical tool for system analysts. This work contributes a new dimension to automotive safety standards by extending the notion of controllability to include the complexities of human-machine interaction and shared situational awareness.

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.

Topics

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

• situational awareness
• automation
• automation surprise
• driverless ads
• mode awareness
• automation complacency bias

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, computational model, theory or model