Contact Dr Marco Cecotti
- Tel: +44 (0) 1234 758512
- Email: M.Cecotti@cranfield.ac.uk
- ORCID
Areas of expertise
- Autonomous Systems
- Electric and Hybrid Vehicles
- Mechatronics & Advanced Controls
- On-Road Vehicle Dynamics
Background
Dr Cecotti received the PhD degree in electrical engineering from Oxford Brookes University, UK, in 2013. He worked for Tata Motors and Dyson on different automotive projects, focused on the control of electric and hybrid electric powertrains, and the development of autonomous vehicles. He then moved to Cranfield University to work on several research projects focused on vehicle automation.
Dr Cecotti is now a Lecturer at the Advanced Vehicle Engineering Centre at Cranfield University, UK. His research interests include vehicle trajectory control, path planning, localisation and sensor fusion, applied to autonomous ground vehicles.
Research opportunities
Dr Cecotti's research interests span across a wide range of topics, including vehicle trajectory control, path planning, localisation and sensor fusion, generally applied to autonomous ground vehicles.
Current activities
Recently, Dr Cecotti has supported the delivery of two large government-funded projects: HumanDrive and CogShift.
The HumanDrive project, sponsored by Innovate UK and managed by a large consortium led by Nissan, was focused on the development of an autonomous vehicle with natural, human-like behaviour. Cranfield's role was to provide a framework to evaluate the human-like behaviour of autonomous vehicles, and to test the autonomous vehicle developed for the project using the facilities of the university, including the MUEAVI, our own proving ground.
CogShift was sponsored by EPSRC and Jaguar Land Rover and, in partnership with UCL, we focused on improving the transition from autonomous driving mode to human driving mode in passenger cars. Dr Cecotti's role, in particular, was to support the development of control algorithms to generate haptic cues, taking into account the level of attention of the driver
Publications
Articles In Journals
- Yin C, Cecotti M, Auger DJ, Fotouhi A & Jiang H. (2025). Deep‐learning‐based vehicle trajectory prediction: a review. IET Intelligent Transport Systems, 19(1)
- Yin C, Cecotti M, Auger DJ, Fotouhi A & Jiang H. (2025). Lane centerline extraction based on surveyed boundaries: an efficient approach using maximal disks. Sensors, 25(8)
- Liu X, Fotouhi A, Cecotti M & Auger D. (2024). Optimal control of race car with aerodynamic slipstreaming effect. IEEE Transactions on Control Systems Technology, 32(6)
- Courtois H, Aouf N, Ahiska K & Cecotti M. (2023). NDT RC: Normal Distribution Transform Occupancy 3D Mapping with recentering. IEEE Transactions on Intelligent Vehicles, 9(1)
- Courtois H, Aouf N, Ahiska K & Cecotti M. (2022). OAST: Obstacle Avoidance System for Teleoperation of UAVs. IEEE Transactions on Human-Machine Systems, 52(2)
Conference Papers
- Kanchwala H, Viana IB, Ceccoti M & Aouf N. (2019). Model predictive tracking controller for a high fidelity vehicle dynamics model
- Cecotti M, Kanchwala H & Aouf N. (2019). Autonomous navigation for mobility scooters: a complete framework based on open-source software
- Cecotti M, Larmine J, Fellows N & Hayatleh K. (2019). Development of an Autonomous Battery Electric Vehicle
- Cecotti M, Larminie J & Azzopardi B. (2012). Estimation of slip ratio and road characteristics by adding perturbation to the input torque
