Roger Woodman

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Assistant Professor

University of Warwick

United Kingdom

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Roger Woodman is an Assistant Professor at the University of Warwick, UK. His research focuses on the adoption of next-generation transportation technologies, particularly regarding users with disabilities and the development of automated driving systems. Recent publications include studies on hazard detection, virtualisation technologies, and user intentions towards autonomous vehicles.

Recent Grants

Grant: Close

Micromobility Transport Modelling

Open Date: 2021-10-03

Close Date: 2025-03-30

Grant: Close

Evaluation of E-scooter Sounds to Support Blind and Partially Sighted People

Open Date: 2021-01-01

Close Date: 2023-01-01

Grant: Close

Innovation to Commercialisation of University Research (ICURe)

Open Date: 2014-10-01

Close Date: 2015-03-01

Articles (22)

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Collaborators (6)

Mark T. Elliott

University of Warwick

UNITED KINGDOM

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Neel Ocean

Assistant Professor

University of Warwick

UNITED KINGDOM

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Christopher Burns

Cranfield University

UNITED KINGDOM

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Stewart Birrell

Professor of Human Factors for Future Transport at Coventry University

Coventry University

UNITED KINGDOM

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Mehrdad Dianati

Professor

Queen's University Belfast

UNITED KINGDOM

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Hakan Yekta Yatbaz

Queen's University Belfast

UNITED KINGDOM

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Roger Woodman

Research Interests

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About

Recent Grants

Articles (22)

Key factors influencing adoption of next-generation transportation technologies among people with disabilities

Understanding future adoption of autonomous vehicle services among disabled and non-disabled users

HazardVLM: A Video Language Model for Real-Time Hazard Description in Automated Driving Systems

State-of-the-art virtualisation technologies for the centralised automotive E/E architecture

Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations

Driving forward together: The common intention of Indonesians in different residential areas to use autonomous vehicles

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

HazardVLM: A Video Language Model for Real-Time Hazard Description in Automated Driving Systems

Optical Flow Based Detection and Tracking of Moving Objects for Autonomous Vehicles

VRSTNN: Visual-Relational Spatio-Temporal Neural Network for Early Hazardous Event Detection in Automated Driving Systems

VRSTNN: Visual-Relational Spatio-Temporal Neural Network for Early Hazardous Event Detection in Automated Driving Systems

Run-Time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations

How Do Drivers Perceive Risks During Automated Driving Scenarios? An fNIRS Neuroimaging Study

The relationship between aggressive driving and driver performance: A systematic review with meta-analysis

The relationship between aggressive driving and driver performance: A systematic review with meta-analysis

Review of Graph-Based Hazardous Event Detection Methods for Autonomous Driving Systems

Review of Graph-Based Hazardous Event Detection Methods for Autonomous Driving Systems

Using fNIRS to Verify Trust in Highly Automated Driving

Ethical considerations and moral implications of autonomous vehicles and unavoidable collisions

Ethical considerations and moral implications of autonomous vehicles and unavoidable collisions

Driver State Monitoring: Manipulating Reliability Expectations in Simulated Automated Driving Scenarios

Physiological Measures of Risk Perception in Highly Automated Driving

Collaborators (6)

Mark T. Elliott

Neel Ocean

Christopher Burns

Stewart Birrell

Mehrdad Dianati

Hakan Yekta Yatbaz

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