Areas of expertise
- Autonomous Systems
- Electric and Hybrid Vehicles
- Mechatronics & Advanced Controls
- On-Road Vehicle Dynamics
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.
Dr Cecotti is involved with several teaching and research activities.
From the teaching point of view, he is supporting the course director Dr Daniel Auger with the preparation of
the new MSc in Connected and Autonomous Vehicle Engineering, starting in
October 2020, and he is supervising the students in Automotive and
Motorsport courses with group projects and individual research projects.
Regarding his research
activities, Dr Cecotti is supporting the delivery of two government-funded projects:
HumanDrive and TASCC. The focus of HumanDrive is to develop an autonomous
vehicle with natural, human-like behaviour. The project is sponsored by
Innovate UK and it is managed by a large consortium led by Nissan. Cranfield's role is
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, in particular the MUEAVI, Cranfield's proving
TASCC is a large project sponsored by EPSRC and Jaguar Land Rover, which is divided into smaller, more focused subprojects. One of them is CogShift, where Cranfield and UCL are working together to improve the transition from autonomous driving to human driving in passenger cars. In particular, Dr Cecotti's role is to support the development of control algorithms to generate haptic cues, taking into account the level of attention of the driver.
Articles In Journals
- Kanchwala H, Viana IB, Cecotti M & Aouf N (2019) Model predictive tracking controller for a high fidelity vehicle dynamics model. In: 2019 IEEE Intelligent Transportation Systems Conference - ITSC 2019, Auckland, 27-30 October 2019.
- Cecotti M, Kanchwala H & Aouf N (2019) Autonomous navigation for mobility scooters: a complete framework based on open-source software. In: 2019 IEEE Intelligent Transportation Systems Conference - ITSC 2019, Auckland, 27-30 October 2019.
- Cecotti M, Larminie J & Azzopardi B (2012) Estimation of slip ratio and road characteristics by adding perturbation to the input torque. In: 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012), Istanbul, 24-27 July 2012.