Connected and Autonomous Vehicles Lab
The Connected and Autonomous Vehicles Lab (CAV Lab) is located in the School of Mechanical Engineering Sciences at the University of Surrey.
The CAV Lab is host to a team of academic scholars, engineers and mechatronic experts whose research has contributed to the state-of-the-art research in the areas of connected autonomous vehicles and advanced driver assistance systems here in the UK but also globally through its collaborative projects delivered in partnership with international companies.
Our Lab’s innovative approach has earnt it a reputation for delivering high impact and award winning research in areas such as:
- Cloud-assisted distributed vehicle control systems
- Intelligent decision-making of autonomous robotic systems
- Artificial intelligence enabled advanced driver assistance systems
- Cooperative situation awareness for connected and autonomous vehicles.
Our Director is Dr Saber Fallah who has been conducting CAV research since 2010. His research has generated four patents, and he is always keen to deliver real world solutions to the companies he works with to support their research and development objectives, but also deliver measurable commercial benefits. Our Lab also offers a range of facilities that can be used by external organisations.
Our capabilities in autonomous vehicles
Meet the team
Innovation and partnerships
Postdoctoral research fellows
Dr Ashith Rajendra Babu
Research fellow in learning-based MPC motion-planning for robotic manipulators
Dr Saeid Safavi
Research fellow in adaptive predictive fault detection for connected autonomous systems
Postgraduate research students
Postgraduate research student in perception of morphology and kinematic properties of space debris for grasp planning using sensor fusion
Postgraduate research student in functional safety analysis of cooperative adaptive cruise control systems
Research assistant in energy management of a fleet of connected electric vehicles
Research assistant in augmented reality for situational awareness of autonomous systems
Research assistant in predictive fault detection for autonomous systems
Postgraduate research student in cooperative decision making for automated driving in urban environment
Postgraduate research student in fault-tolerant cooperative vehicle state estimation (cloud-assisted)
Modelling predictive controls of semi-active suspension systems
Dr Zhengyuan Wang
Optimal Torque-Vectoring Control for stabilisation of Autonomous Vehicles
Our Lab group publishes in high impact journals such as IEEE Transactions.
- Kuutti, S. Fallah, R. Bowden, P. Barber, Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects, Morgan and Claypool Publishers, 80 pages, ISBN-10: 1681736071, Aug. 2019
- Y. Gao, S. Fallah, Y. Jin, C. Lekakou, Towards Autonomous Robotic Systems, Springer, 720 pages,ISBN-10: 3319641069, July 2017.
- A. Khajepour, S. Fallah, and A. Goodarzi, Electric and Hybrid Vehicles: Technologies, modeling and control, A mechatronic approach, Wiley-Blackwell, 432 pages, ISBN-10: 1118341511, April 2014.
- S Taherian, S Dixit, U Montanaro, S Fallah (2020), Autonomous Emergency Collision Avoidance and Stabilisation in Structured Environments - arXiv preprint arXiv:2004.07987.
- S Kuutti, S Fallah, R Bowden (2020) Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies, arXiv preprint arXiv:2002.12078.
- S Kuutti, R Bowden, Y Jin, P Barber, S Fallah (2020) A Survey of Deep Learning Applications to Autonomous Vehicle Control IEEE Transactions on Intelligent Transportation …, 2020.
- S Kuutti, R Bowden, H Joshi, R de Temple, S Fallah (2019) Safe Deep Neural Network-Driven Autonomous Vehicles Using Software Safety Cages Conference on Intelligent Data Engineering.
- S Taherian, U Montanaro, S Dixit, S Fallah (2019) Integrated Trajectory Planning and Torque Vectoring for Autonomous Emergency Collision Avoidance IEEE Intelligent Transportation Systems.
- S Kuutti, R Bowden, H Joshi, R de Temple, S Fallah (2019) End-to-end Reinforcement Learning for Autonomous Longitudinal Control Using Advantage Actor Critic with Temporal Context IEEE Intelligent Transportation Systems.
- S Kuutti, S Fallah, R Bowden, P Barber (2019) Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects Synthesis Lectures on Advances in Automotive Innovation, Springer.
- S Dixit, U Montanaro, M Dianati, D Oxtoby, T Mizutani, S Fallah (2019) Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments Using Robust MPC, IEEE Transactions on Intelligent Transportation Systems.
- E Arnold, OY Al-Jarrah, M Dianati, S Fallah, D Oxtoby (2019) Cooperative object classification for driving applications, IEEE Intelligent Vehicles Symposium (IV).
- E Arnold, OY Al-Jarrah, M Dianati, S Fallah, D Oxtoby (2019) A survey on 3d object detection methods for autonomous driving applications IEEE Transactions on Intelligent Transportation Systems.
- U Montanaro, S Fallah, M Dianati, D Oxtoby, T Mizutani, (2018) Cloud-assisted distributed control system architecture for platooning, 21st International Conference on Intelligent Transportation Systems.
- S Dixit, U Montanaro, S Fallah, M Dianati, D Oxtoby (2018), Trajectory planning for autonomous high-speed overtaking using MPC with terminal set constraints, 21st International Conference on Intelligent Transportation Systems.
- U Montanaro, S Fallah, M Dianati, D Oxtoby, T Mizutan (2018), On a fully self-organizing vehicle platooning supported by cloud computing, Fifth International Conference on Internet of Things.
- Written an article in The Conversation magazine regarding the impact of autonomous cars and 5G technologies on future cities
- Interviewed by BBC Surrey Radio, Sputnik News and The Guardian, discussing the challenges and potentials of autonomous vehicles to the society and industry,
- Contributed in a white paper written by Publitek Company on the technological evolution of autonomous driving
- Invited by Huawei Technologies in Germany (Nov. 2018 and July 2019) to attend an expert panel to discuss the opportunities for Huawei to be involved in this field of research and business and investigated potential collaboration with CAV-Lab
- Chaired an industrial European forum discussing the IoT-Connected Smart Cars and Vehicles,
- Presented the CAV-Lab research to British, Chinese and USA delegates in 13th UK-China Workshop on Space Science and Technology and in Automation, AI and Robotics Workshop at the NASA Johnson Space Centre.