Methodological challenges of scenario generation validation: A rear-end crash-causation model for virtual safety assessment
Artikel i vetenskaplig tidskrift, 2024

Safety assessment of crash and conflict avoidance systems is important for both the automotive industry and other stakeholders. One type of system that needs such an assessment is a driver monitoring system (DMS) with some intervention (e.g., warning or nudging) when the driver looks off-road for too long. Although using computer simulation to assess safety systems is becoming increasingly common, it is not yet commonly used for systems that affect driver behavior, such as DMSs. Models that generate virtual crashes, taking crash-causation mechanisms into account, are needed to assess these systems. However, few such models exist, and those that do have not been thoroughly validated on real-world data. This study aims to address this research gap by validating a rear-end crash-causation model which is based on four crash-causation mechanisms related to driver behavior: a) off-road glances, b) too-short headway, c) not braking with the maximum deceleration possible, and d) sleepiness (not reacting before the crash). The pre-crash kinematics were obtained from the German GIDAS in-depth crash database. Challenges with the validation process were identified and addressed. Most notably, a process was developed to transform the generated crashes to mimic the crash severity distribution in GIDAS. This step was necessary because GIDAS does not include property-damage-only (PDO) crashes, while the generated crashes cover the full range of severities (including low-severity crashes, of which many are PDOs). Our results indicate that the proposed model is a reasonably good crash generator. We further demonstrated that the model is a valid method for assessing DMSs in virtual simulations; it shows the safety impact of shorter ‘longest’ off-road glances. As expected, ‘cutting away’ long off-road glances substantially reduces the number of crashes that occur and reduces the average delta-v. This work highlights the need to both a) thoroughly understand the process of generating virtual scenarios and b) have the tools to validate them. While more work to develop validation processes for scenario generation is needed across all levels of crash severity, the transform and other validation tools that were developed bring us one step closer to accurate validation methodologies.

Scenario generation


Safety assessment

Counterfactual simulations

Driver model



Jonas Bärgman

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet

Malin Svärd


Simon Lundell


Erik Hartelius


Transportation Research Part F: Traffic Psychology and Behaviour

1369-8478 (ISSN)

Vol. 104 374-410

V4SAFETY Vehicles and VRU Virtual eValuation of Road Safety

Europeiska kommissionen (EU) (EC/HE/101075068), 2022-10-01 -- 2025-09-30.


Teknisk mekanik

Transportteknik och logistik




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