Today, Ron Schindler successfully defended his doctoral thesis "A Holistic Safety Benefit Assessment Framework for Heavy Goods Vehicles". SAFER wishes you all the best and good luck in your future career!
Ron Schindler has been a PhD student in the Crash Analysis and Prevention research unit of the Division of Vehicle Safety, at Chalmers University of Technology. During his PhD, he mainly worked in the EU-funded project AEROFLEX, which aimed to design the next generation of heavy goods vehicles. Within the project, his research focused on the analysis of in-depth road crash databases and modelling of driver behaviour, to conduct safety benefit evaluations of future safety systems. Ron has also contributed to the SAFER associated project PROSPECT and led the data collection and analysis in TRUBADUR, a project within SAFER's Open Research at AstaZero Program. The area of being able to measure the effects of different safety systems is an extremely important area that we will continue to explore within SAFER.
You can find Ron’s thesis here: A Holistic Safety Benefit Assessment Framework for Heavy Goods Vehicles (chalmers.se)
Ron’s main supervisor has been Associate professor Giulio Bianchi Piccinini, Chalmers University.: Dr. Richard Hanowski, Director of Division of Freight, Transit, and Heavy Vehicle Safety, Virginia Tech Transportation Institute was the opponent.
A summary of the thesis
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.