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With 41 European partners, Hi-Drive (2021-2025) is the European Commission’s flagship project on automated driving. The aim is to build an extended and continuous operational design domain (ODD) making it possible to operate vehicles for longer periods compared to the current state-of-the-art.

As part of that effort, the Human Factors and Safety group is involved in a number of aspects of the project, including leading the “Users” sub-project (SP6).

Professor Natasha Merat is the Users sub-project lead and Leeds Principal Investigator. Leeds' main focus in Hi-Drive will be to investigate and evaluate user behaviour and interactions with CAVs; assessing user perception, and enabling the development of acceptable, safe and efficient communication and interactions between CAVs, on-board users and other road users.

The User sub-project will include work packages on Acceptance and awareness (led by Dr Esko Lehtonen, VTT), Human-like driving and user comfort (led by Stefan Wolter, Ford), User Monitoring and related HMI (led by Dr Tyron Louw, University of Leeds) and Interactions with other road users (led by Michael Oehl, DLR).

We conduct sempirical, user-based studies, incorporating a range of tools, such as human-in-the-loop simulatorsWizard-of-Oz vehicles, test track studies, surveys and questionnaires, to develop a firm understanding of user behaviour, expectations and limitations when interacting with CAVs, both as drivers and as VRUs/other road users.

Dr Yee Mun Lee is deputy for the Users sub-project, Dr Ruth Madigan leads the Methods for user evaluation (SP4), while Dr Mahdi Rezaei is involved in applying machine learning techniques in the development of automated driving (SP2).

Multi-Disciplinary Pedestrian-in-the-Loop Simulator

Pedestrians represented roughly 24% of road fatalities and 22% of the seriously injured in the UK in 2015 (Department for Transport, Reported Road Casualties Great Britain: 2015, Annual Report). The most commonly recorded factors were: "in accidents where a pedestrian was killed or injured; pedestrian failed to look properly was reported in 59 per cent of accidents. Failed to judge other person's path or speed was the most typical secondary cause." (DfT, 2015)

In this context, the increased use of Autonomous Vehicles (AVs) and new urban warning systems that can help monitor and assist pedestrians and their interactions with vehicles has the potential to dramatically reduce road deaths. A major concern, however, is that the AVs and warning systems must be designed to take into account the capabilities and limitations of pedestrians.

This project will develop a new pedestrian laboratory to support safe experimental research in a repeatable fashion in which a variety of variables with respect to AV design, warning system design, and intersection configuration can be studied. The experiments can also look at the impacts of a wide range of human factors including age, vision and mobility.

The pedestrian laboratory (PEDSIM) will consist of a Virtual Reality (VR) simulator that will allow a participant to experience a variety of urban configurations and interact with new vehicles and urban robots. The pedestrian laboratory will track the participant's performance in a variety of tasks to compare the effectiveness of various designs.

What makes the PEDSIM unique in the world is its very high resolution displays combined with its large walkable environment (9 metres by 4 metres) and its integration with driving simulators to test interactions between pedestrians and drivers.

As automated and autonomous vehicles get closer to deployment, research into their design and impact has rapidly increased. There are several studies currently funded by the EPSRC that can take immediate advantage of the new research capabilities of the PEDSIM. These include research to evaluate solutions for cooperative interaction of automated vehicles and urban robots with pedestrians and research that will test various lighting conditions and its impact on visibility, trip hazards, and understanding intentions of other pedestrians and vehicles.

HAROLD: HAzards, ROad Lighting and Driving

The frequency and severity of road collisions are reduced if drivers are able to detect and recognize potential hazards in sufficient time to take evasive action such as braking and steering. An improvement in detection time measurable in hundreds of milliseconds could substantially increase the probability that a crash can be avoided.

After dark, visual functions such as reaction time are significantly reduced, and road lighting is installed as a countermeasure to this visual impairment. Road lighting is of particular importance for revealing hazards beyond the reach of vehicle headlights such as pedestrians emerging from the side. The British Government recognise the need for significant reductions in road traffic collisions, particularly those involving vulnerable road users such as pedestrians, and committed to enhancing protection of these people in the 2015 British Road Safety Statement.

This project will investigate the way in which lighting can be used to enhance safety on main roads. Specifically, we seek lighting that increases the chance of drivers seeing a hazard and reduces the time taken to see the hazard. These hazards include other vehicles, stationary objects, and pedestrians.

There are two problems with the current situation. First, while British and European standards provide guidance on road lighting, the empirical basis of the recommended lighting is not clear. Therefore, we do not know whether they recommend optimal conditions. Second, we suspect there is a better way for enhancing the detection of pedestrians when they are otherwise unexpected, which is frequently the case for pedestrians on main roads. This is that pedestrians should use a pulsing or flashing LED band, worn on the wrist or ankle to take advantage of bio-motion. An LED band could provide a low-cost counter-measure to reduce the risk of an accident.

To investigate these proposals we will first carry out experiments to find out how the detection of hazards including pedestrians is affected by changes in lighting, using variations in the intensity and spectrum (colour) of lighting. Whilst drivers should be continuously scanning for potential hazards, there are many distractions - listening to music, speaking to passengers and looking at maps or digital navigation devices. These distractions reduce our ability to detect hazards.

We will therefore also investigate how hazard detection is affected by distraction and whether optimal lighting can mitigate the distraction decrement. This research is of particular benefit to elderly drivers; the elderly tend to have poorer vision and, overall, they perform worse than younger people when driving with distractions From these data we will identify the changes in lighting conditions likely to improve safety. These recommendations will be validated by manipulating lighting conditions within a high fidelity driving simulator. The simulator places the test participant in a more realistic setting while still maintaining control on road situation and ensuring participant safety. To facilitate the implementation of results we will work to ensure the guidance and standards documents used by lighting designers are revised to include the proposed criteria.

COMMOTIONS: Computational Models of Traffic Interactions for Testing of Automated Vehicles

As automated vehicles (AVs) are being developed for driving in increasingly complex and diverse traffic environments, it becomes increasingly difficult to comprehensively test that the AVs always behave in ways that are safe and acceptable to human road users. There is wide consensus that a key part of the solution to this problem will be the use of virtual traffic simulations, where simulated versions of an AV under development can meet simulated surrounding traffic. Such simulations could in theory cover vast ranges of possible scenarios, including both routine and more safety-critical interactions. However, the current understanding and models of human road user behaviour are not good enough to permit realistic simulations of traffic interactions at the level of detail needed for such testing to be meaningful. This fellowship aims to develop the missing simulation models of human behaviour, to ensure that development of the future automated transport system can be carried out in a responsible, human-centric way.

The behaviour of car drivers and pedestrians will be observed both in real traffic as well as in controlled studies in driving and pedestrian simulators, in some cases complementing behavioural data with neurophysiological (EEG) data, since several candidate component models make specific predictions about brain activity. The fellowship will then build on existing models of driver and pedestrian behaviour in routine and safety-critical situations, and extend these with state of the art neuroscientific models of specific phenomena like perceptual judgments, beliefs about others' intentions, and communication, to create an integrated cognitive modelling framework allowing simulations of traffic interactions across a variety of targeted scenarios.

Such cognitive interaction models, based on well-understood underlying mechanisms, will be one main contribution from the fellowship. Some researchers have suggested the use of another type of model altogether, instead obtained directly by applying machine learning (ML) methods to large datasets of human road user behaviour, i.e., without an ambition to correctly model underlying mechanisms. This fellowship hypothesises that to achieve reliable virtual testing of AVs, both types of modelling approaches will be needed, and methods for combining them will be researched. Not least, due to their "black box" nature, ML models need to be investigated and benchmarked, to for example determine their ability to generalise to rare, safety-critical events.


Project TRANSITION, led by Professor Richard Wilkie in the School of Psychology, will use sophisticated laboratory-based measures (including the UoLDS) to examine drivers re-engaging with the vehicle after a period of AV control. We will determine the capability of drivers regaining steering control under conditions that simulate various types of visual and cognitive load (e.g. driving at night, and/or when looking away at a satellite navigation system). These findings will be used to identify situations where drivers are particularly vulnerable to making steering errors and develop the TRANSITION model of AV-Human transitions that will inform improvements to the design and implementation of AV systems.

VeriCAV: Using simulation to put Connected and Autonomous Vehicles to the test

The VeriCAV project will develop an integrated platform to allow Automated Driving Systems (ADS) to be tested in simulation.

As Connected and Autonomous Vehicles technology becomes increasingly sophisticated (and the UK more prepared for driverless cars on public roads), the industry needs to explore ways to ensure autonomous vehicles operate safely. However, safety evaluation is laborious and complex, and real-world testing can be impractical and incomplete – simulation gets around these problems and opens the doors to evaluating multi-layered and uncommon situations.

Delivered by a world-class consortium of industry and academic experts, and starting in November 2018, VeriCAV is a 2-year project that will open the door to evaluating complex and uncommon CAV situations in a simulated environment.


  • Start date: 1 May 2019
  • End date: 30 April 2022
  • Funder: EU Horizon 2020
  • Partners and collaborators: TNO; Aristotelio Panepistimio; Thessalonikis; Ivl Svenska Miljoeinstitutet Ab; Statens Vag - Och Transportforskningsinstitut; Hs Data Analysis And Consultancy; Icct - International Council On Clean Transportation Europe Ggmbh; Technische Universitaet Graz; Crossyn Automotive B.V.; Eidgenossische Materialprufungs- Und Forschungsanstalt; Vrije Universiteit Brussel; Infras Ag; Ceske Vysoke Uceni Technicke V Praze; Tsinghua University; University Of Leeds.
  • Primary investigatorProfessor Samantha Jamson

uCARe is a project funded by the European Union (H2020 LC-MG-1-1-2018) with the aim to reduce the impact of transport on air quality. The project has a 36 months duration and started on the 1st of May 2019. The budget is 3 million Euro, spent on 288 person months by 14 partners. The ambition of uCARe is to reduce the overall pollutant emissions of the existing vehicle fleet to improve air quality with impact on, among others, the environment and people’s health. To achieve this, uCARe will: provide vehicle users with simple, insightful and effective tools to decrease their individual emissions; support stakeholders with an interest in local air quality in selecting feasible intervention strategies that lead to the desired user behaviour.

Project website


SHAPE-IT, short for Supporting the interaction of Humans and Automated vehicles: Preparing for the Environment of Tomorrow, is a Innovative Training Network (ITN) project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement 860410.

The project has a duration of four years starting October 1st 2019. Fifteen PhD-students will perform research in the project, together with their academic and industrial supervisors.

The overall goal of SHAPE-IT is to enable rapid and reliable development of safe and user-centred automated vehicles (AVs) for urban environments. Vehicle automation has been identified as a game-changer in transport, promising substantial reductions in road-traffic fatalities while improving mobility. However, the processes to integrate automation in transport have been primarily technology-focussed, with insufficient consideration given to how users both inside and outside of the AVs will interact with AVs.

The three SHAPE-IT PhD researchers based at the University of Leeds are:

  • Chen Peng. Topic: Developing more acceptable, pleasant and transparent AV-kinematic cues for drivers. Project description
  • Yue Yang. Topic: Long Term Effects of AV Exposure on AV/VRU Interactions. Project description
  • Amir Hossein Kalantari. Topic: Computational AV/Pedestrian Interaction Models. Project description


Autonomous vehicles are becoming a reality and most of the major automakers have plans to commercially release an autonomous vehicle (nearly or fully self-driving, i.e. SAE levels L4 or L5 vehicles, respectively) by 2020-2024. However, the human factor will remain essential for the safety and performance of road transport in the forthcoming decades. Central to the human role in the Connected Automated Driving (CAD) is the transition from automated to manual driving mode. This might be system-initiated or user-initiated.

Trustonomy will investigate, setup, test and comparatively assess, in terms of performance, ethics and acceptability, different relevant technologies and approaches in a variety of autonomous driving and RtI scenarios, covering different types of users (in terms of age, gender, driving experience, etc.), road transport modes (private cars, trucks, buses), levels of automation (L3 – L5) and driving conditions.

Project website