i4Driving: evaluating road safety in traffic simulation – a controversial topic and a paradox

Published on May 7, 2024

Alkis Papadoulis

Scientific Researcher

Evaluating safety in a traffic simulation environment has always been a controversial research topic because vehicles in a simulation are programmed in such a way that they cannot potentially cause accidents. Alkis Papadoulis, PhD (Scientific Researcher at Aimsun) discusses how the i4Driving research project is aiming to address this issue by incorporating the most important human factors in existing human driver models; the hope is that the models will then serve as a reliable baseline for cooperative, connected and automated mobility.  


According to the World Health Organisation, the number of road traffic deaths is “unacceptably high,” reaching 1.35 million every year worldwide. Road accGeidents are expected to become the fifth leading cause of deaths by 2030 (WHO, 2015). Because of this grim projection, academia, original equipment manufacturers, the automotive industry, and highway infrastructure providers have focused their efforts on developing both infrastructure-based and vehicle-based technologies that aim to improve road safety.

Cooperative, connected and automated mobility (CCAM) is one of the most recent examples of such technology that has advanced significantly over the past few years, and an increasing number of real-world testsare taking place world-wide to test its readiness and effectiveness. These tests have provided useful insights with regards to CCAM performance; however, they can come at a heavy price.  Early-stage, real-world CCAM tests have caused accidents, which in some cases have even cost the lives of drivers or pedestrians.

Traffic simulation could be a good alternative virtual environment to test CCAM technologies. However, safety evaluation in traffic simulation has always been a controversial topic among transport researchers and has received criticism due to the overarching idealized human driver models that are expected to interact with Connected and Automated Vehicles (Tarko, 2005). When looking at existing simulation environments, one faces an important paradox: How can road safety be evaluated when in this virtual environment certain safety critical events that can potentially occur – rarely but they do occur – are not able to be reproduced? To be more specific, vehicles in current traffic simulation environments cannot crash and important human factors related to human reaction times, distraction and perception errors are not captured.

Just to be clear, seeing vehicles crash in every single simulation is not what I am suggesting, they just need to have the potential to do so. In any case, crashes, or accidents as they are more commonly referred to in literature, are the tip of the iceberg, or the tip of the pyramid (see below). More importantly, vehicles need to be able to reproduce realistic numbers of safety critical behaviors which may result in events way more common than crashes, such as near-misses (sometimes also known in road safety terms as ‘conflicts’ in Hyden’s safety pyramid shown below). Only then can simulation environments provide a reliable virtual testbed for CCAM technology.

The above issue is exactly what i4Driving is aiming to tackle; the aim is to develop and deliver a new library of credible models of heterogeneous human driver behaviours to provide a road safety baseline for CCAM virtual assessment. i4Driving started in October 2022 and will last three years, with a consortium consisting of 17 partners including universities, research institutes and companies from all over the world.

The first step is to identify use cases and scenarios that pose challenges to human drivers. This is what the i4Driving consortium is currently working on at the time of writing. Putting a large sample of human drivers in these scenarios will help reveal the most important factors that generate unsafe human driving behaviors. The quantitative parametrization of these behaviors will be undertaken by processing data collected through Naturalistic Driving Studies (NDS) which are data collected during driving in the real world, as well as data from Driving Simulator experiments. Once these factors are identified, they will be coded in probabilistic human behavioral models and incorporated in an integrated simulation framework, where the safety of CCAM can be evaluated effectively. Aimsun will be at the forefront of this effort.

The validation of the newly developed human driver models is essential and is critical to the success of this endeavor.  For example, i4Driving needs to ensure that the new models generate a realistic number of unsafe vehicle acts over a specific period. However, I know from personal experience that driver behavior varies significantly in different countries, particularly driver aggressiveness and, consequently, the number of unsafe acts committed over time.  Another topic that needs to be explored by the consortium is the transferability of the newly developed models – how can we develop models that can be easily calibrated with regards to safety, and can they potentially be used worldwide?   

To ensure that the outcomes of the project are widely accepted, i4Driving aims to be transparent by developing open-source driver models. Additionally, the consortium will set up a “Modeling  the Modeling Process,” which will allow close scrutiny of the traffic modeling assumptions through a sensitivity auditing process.

The project is a challenge, but it is necessary if we are to trust the testing of CCAM in virtual environments. The i4Driving consortium is working hard to make this happen, so watch this space for some important and exciting updates in the next few months.


World Health Organisation (2015) ‘Safer Vehicles and Roads’, Global Status Report on Road Safety, p. 46. doi: 10.1016/B978-0-12-385185-7/00054-8.

Hydén, C. (1987) ‘The Development of a Method for Traffic Safety Evaluation: the Swedish Traffic Conflict Technique’, Bulletin Lund University of Technology, p. 229. doi: 10.1002/2016GC006399

Tarko, A. P. (2005) ‘Estimating the frequency of crashes as extreme traffic events’,

Annual Meeting of the Transportation Research Board, Washington, DC, (765), pp.


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Aimsun Next 24

Aimsun (2024). Aimsun Next 24 User’s Manual, Aimsun Next Version 24.0.0, Barcelona, Spain. Accessed on: April. 16, 2024. [Online].

Available: https://docs.aimsun.com/next/24.0.0/

Aimsun Next 24

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