Event

Welcome to Ron Schindler's Doctoral Thesis defence "A Holistic Safety Benefit Assessment Framework for Heavy Goods Vehicles"! 

Ron Schindler is 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.

ABSTRACT

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.
 
Supervisor: Associate professor Giulio Bianchi Piccinini, Chalmers University, has been the main supervisor.

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

Grading committee: David LeBlanc (University of Michigan Transportation Research Institute), George Yannis (National Technical University of Athens), Sogol Kharrazi (Swedish National Road and Transport Research Institute)

Research page: https://research.chalmers.se/en/publication/528899  (The thesis will be available in the beginning of April)

Welcome!