A Holistic Safety Benefit Assessment Framework for Heavy Goods Vehicles
Doktorsavhandling, 2022

In 2019, more than one million crashes occurred on European roads, resulting in almost 23,000 traffic fatalities. Although heavy goods vehicles (HGVs) were only involved in 4.4% of these crashes, their proportion in crashes with fatal outcomes was almost three times larger. This over-representation of HGVs in fatal crashes calls for actions that can support the efforts to realize the vision of zero traffic fatalities in the European Union. To achieve this vision, the development and implementation of passive as well as active safety systems are necessary. To prioritise the most effective systems, safety benefit estimations need to be performed throughout the development process. The overall aim of this thesis is to provide a safety benefit assessment framework, beyond the current state of the art, which supports a timely and detailed assessment of safety systems (i.e. estimation of the change in crash and/or injury outcomes in a geographical region), in particular active safety systems for HGVs. The proposed framework is based on the systematic integration of different data sources (e.g. virtual simulations and physical tests), using Bayesian statistical methods to assess the system performance in terms of the number of lives saved and injuries avoided. The first step towards the implementation of the framework for HGVs was an analysis of three levels of crash data that identified the most common crash scenarios involving HGVs. Three scenarios were recognized: HGV striking the rear-end of another vehicle, HGV turning right in conflict with a cyclist, and HGV in conflict with a pedestrian crossing the road. Understanding road user behaviour in these critical scenarios was identified as an essential element of an accurate safety benefit assessment, but sufficiently detailed descriptions of HGV driver behaviour are currently not available. To address this research gap, a test-track experiment was conducted to collect information on HGV driver behaviour in the identified cyclist and pedestrian target scenarios. From this information, HGV driver behaviour models were created. The results show that the presence of a cyclist or pedestrian creates different speed profiles (harder braking further away from the intersection) and changes in the gaze behaviours of the HGV drivers, compared to the same situation where the vulnerable road users are not present. However, the size of the collected sample was small, which posed an obstacle to the development of meaningful driver models. To overcome this obstacle, a framework to create synthetic populations through Bayesian functional data analysis was developed and implemented. The resulting holistic safety benefit assessment framework presented in this thesis can be used not only in future studies that assess the effectiveness of safety systems for HGVs, but also during the actual development process of advanced driver assistance systems. The research results have potential implications for policies and regulations (such as new UN regulations for mandatory equipment or Euro NCAP ratings) which are based on the assessment of the real-world benefit of new safety systems and can profit from the holistic safety benefit assessment framework.

heavy goods vehicle

Bayesian methods

Safety benefit assessment

crash data analysis

driver behaviour modelling

Room Alfa, Building Saga, Campus Lindholmen
Opponent: Dr. Richard Hanowski, Director of Division of Freight, Transit, and Heavy Vehicle Safety, Virginia Tech Transportation Institute


Ron Schindler

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet

Truck drivers’ behavior in encounters with vulnerable road users at intersections: Results from a test-track experiment

Accident Analysis and Prevention,; Vol. 159(2021)

Artikel i vetenskaplig tidskrift

Making a few talk for the many – Modeling driver behavior using synthetic populations generated from experimental data

Accident Analysis and Prevention,; Vol. 162(2021)

Artikel i vetenskaplig tidskrift

Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data

International Journal of Environmental Research and Public Health,; Vol. 19(2022)

Artikel i vetenskaplig tidskrift

Crashes in traffic are still one of the leading causes of death worldwide, particularly when heavy trucks are involved. A large part of these crashes can be mitigated or avoided altogether by active safety systems. While modern passenger cars are already equipped with these systems, they are less common in the heavy truck fleet. The physical features (heavier and longer) as well as the driver behaviour (professional drivers vs. casual drivers) of heavy goods vehicles are very different than those of passenger cars; thus active safety systems cannot simply be transferred from one to the other. The systems for cars need to be re-developed and adapted to trucks. This thesis provides a framework for analysing these new systems, so that developers get an understanding of how well their systems perform while still under development, before they are implemented in heavy trucks. To facilitate this analysis, typical crash patterns involving heavy goods vehicles from different European crash databases are studied. In addition, detailed driver behaviour information is collected and analysed, to facilitate the safety benefit assessment of specific active safety systems in the most relevant crash scenarios. To further support the assessment, a new methodology was developed that creates an artificial population of drivers, thereby increasing the sample size available for the analysis and allowing a more detailed and reliable analysis of small data samples.

Proactive Safety for Pedestrians and Cyclists (PROSPECT)

Europeiska kommissionen (EU) (EC/H2020/634149), 2015-05-01 -- 2018-10-31.

Proactive SAFEty systems and tools for a constantly UPgrading road environment (SAFE-UP)

Europeiska kommissionen (EU) (EC/H2020/861570), 2020-06-01 -- 2023-05-31.

Aerodynamic and Flexible Trucks for Next Generation of Long Distance Road Transport (AEROFLEX)

Europeiska kommissionen (EU) (EC/H2020/769658), 2017-10-01 -- 2021-03-01.




ReVeRe (Research Vehicle Resource)





Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5083



Room Alfa, Building Saga, Campus Lindholmen


Opponent: Dr. Richard Hanowski, Director of Division of Freight, Transit, and Heavy Vehicle Safety, Virginia Tech Transportation Institute

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