Autonomous Driving and Intelligent Transport

Group Leader(s): Dr Andrew Bradley


About us

The Autonomous Driving and Intelligent Transport group develops state-of-the-art technologies to realise the potential offered by intelligent, connected and highly automated vehicles and transport systems.

Academics and researchers have expertise in many areas including autonomous vehicle perception and control systems, connected vehicle communications, smart city integration, ethical vehicles and transport strategy - enabling us to advise, research and consult on all aspects of the vehicles of the future.

The group is funded by Horizon 2020, and is proud to support OBU’s award-winning autonomous racing team, OBR Autonomous.

About our research areas

Autonomous vehicles see the road ahead using camera snd LiDAR

Related courses

Research impact

Developing roads of the future

The group works closely with local partners and stakeholders, and advises on the Future of Mobility in Oxfordshire with regular interaction with OxLEP. Our research develops technological advances which will significantly impact road safety, and is active in working with SMEs and larger enterprises to implement technology developed at OBU in road-going vehicles. The group has advised on the development of the legal framework for the adoption of Autonomous Vehicles on the UK’s road network.

The OBR Autonomous project seamlessly blends research with teaching, graduating world-class students with the experience required to be the next generation of autonomous vehicle engineers and software developers.


Andrew Bradley

Dr Andrew Bradley

Senior Lecturer

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Name Role Email
Dr Paul Allen Technical Instructor
Dr Aydin Azizi Senior Lecturer
Dr Peter Ball Reader in Knowledge Transfer in Computing and Electronics
Dr George Blumberg Senior Lecturer in Built Environment
Mrs Gordana Collier Head of School
Professor Nigel Crook Associate Dean: Research and Knowledge Exchange (ADRKE)
Professor Fabio Cuzzolin Professor of Artificial Intelligence
Professor John Durodola Professor in Mechanical Engineering and Mathematical Sciences
Dr Muhammad Hilmi Kamarudin Senior Lecturer in Cyber Security
Professor Denise Morrey Professor of Mechanical Engineering and Research Lead
Dr Tjeerd Olde Scheper Senior Lecturer
Mr Ozdemir Ozerem Lecturer in Mechanical Engineering
Dr Alex Rast Lecturer in Computing
Dr Matthias Rolf Reader in Computer Science
Nabil Yassine Senior Lecturer in Electric Vehicles
Professor Hong Zhu Professor of Computer Science


Name Thesis Title Supervisors Completed
Gokhan Budan Connected and Automated Vehicle Enabled Traffic Intersection Control with Reinforcement Learning Professor Denise Morrey, Professor Khaled Hayatleh, Dr Peter Ball 2021
Safras Iqbal Investigations of novel techniques to mitigate against cyber security attacks on autonomous vehicles; Cyber threats to Connected & Autonomous Vehicles Dr Muhammad Hilmi Kamarudin, Dr Peter Ball


Mr Izzedin Teeti Predictive Algorithms for Advanced Autonomous Vehicle Perception Dr Andrew Bradley, Professor Fabio Cuzzolin 2023


Active projects

Project title and description Investigator(s) Funder(s) Dates

Epistemic Artificial Intelligence

Although artificial intelligence (AI) has improved remarkably over the last years, its inability to deal with fundamental uncertainty severely limits its application. This proposal re-imagines AI with a proper treatment of the uncertainty stemming from our forcibly partial knowledge of the world. Epistemic AI’s overall objective is to create a new paradigm for a next-generation artificial intelligence providing worst-case guarantees on its predictions thanks to a proper modelling of real-world uncertainties.
Professor Fabio Cuzzolin Horizon 2020 From: March 2021
Until: February 2025

Advanced Perception

Autonomous vehicles ‘see’ the world around them via the perception system. Working closely with the Visual Artificial Intelligence Lab, we aim to enhance the ability of the autonomous vehicle to interpret the surrounding environment - with a particular focus on dealing with difficult conditions, adverse weather, and predicting the behaviour of other road users.

  • Artificially Intelligent object detection
  • LiDAR, camera and multi-modal perception
  • Detection and prediction of road user behaviour
  • V2X communications to enhance scene understanding
  • Real-time weather augmentation (AI alteration of the weather within images)
  • Robust, weatherproof object detection

Robust object detection in adverse weather
Robust object detection in adverse weather
Autonomous vehicle simulation
Autonomous vehicle simulation

Simulation and Control

Controlling the vehicle is of utmost performance, and testing autonomous vehicles is difficult, expensive, and inherently risky. Fusing OBU’s experience in vehicle dynamics with our computing expertise enables us to work on real-time vehicle control systems and develop simulations to facilitate virtual testing of autonomous driving systems - thus dramatically increasing the pace of development. We are experienced in:

  • Vehicle dynamic modelling and simulation
  • Hardware-in-Loop and Driver-in-Loop testing
  • Real-time vehicle control strategies (including adverse weather)
  • Augmented-Reality simulation
  • Sensor modelling and validation.

Secure Connected Vehicles and Smart Cities

Vehicle-to-Vehicle and Vehicle-to-Infrastructure communications present a huge opportunity for autonomous vehicles to interact with each other and the roadside infrastructure around them in ways human drivers can only dream of, thereby enabling truly smart road networks in the cities of the future. However, these opportunities require extensive research to ensure the communication of timely, useful, authenticated information in a secure manner. Particular areas of interest include:

  • V2X communications
  • Networking and Security
  • Traffic modelling and information management
  • Future road network and city design
  • Smart-city integration
  • Automated transport planning.

Smart city integration
Smart city integration
Neural network subsystem for advanced mapping systems
Neural network sub-system for advanced mapping systems

Localisation and Mapping

Autonomous vehicles need to be able to map out their environment, and / or accurately identify their location within this map. Our research tackles complex problems including:

  • Map-based localisation
  • Sensor fusion
  • Simultaneous Localisation and Mapping (SLAM)
  • Biologically-inspired SLAM
  • Trajectory prediction, planning and optimisation.

Ethical Human-Vehicle Interaction

Eventually, humans and autonomous vehicles will be operating in tandem with one another. Collaborating with the Cognitive Robotics Group and the Institute for Ethical AI, we are addressing:

  • Interaction between vehicle and driver, pedestrians, and other vehicles
  • Human driver awareness monitoring systems
  • Smooth handover of control between human and autonomous driver
  • Managing passenger expectations, and adjusting driving style to suit personal preferences
  • Vehicle behaviour and actions in shared and social spaces
  • How to interpret and recreate human gestures to signal road intentions (e.g. raising a hand to allow pedestrians to cross).
Driver-in-Loop Simulator
Driver-in-Loop Simulator

Autonomous Motorsport

Award-winning autonomous racing team
Award-winning autonomous racing team

The Autonomous Driving and Intelligent Transport group is proud to support OBR Autonomous - Oxford Brookes’ multiple award-winning self-driving student racing team, and winners of the IMechE’s 2020 Formula Student: Artificial Intelligence competition (DDT class). With world-class workshops and access to test facilities, we work closely with the team upon:

  • Real-world experimental testing
  • Augmented-Reality testing
  • Electric vehicle design and build
  • Hardware-in-Loop testing
  • Multi-modal sensor equipment

OBR Autonomous

OBR Autonomous logo