Contact Dr Marco Cecotti
- Tel: +44 (0) 1234 758512
- Email: M.Cecotti@cranfield.ac.uk
- ORCID
Areas of expertise
- Mechatronics & Advanced Controls
- On-Road Vehicle Dynamics
Background
Dr Marco Cecotti is a Lecturer in Driving Automation and Course Director of the MSc in Connected and Autonomous Vehicle Engineering (CAVE). He holds a PhD in Electrical Engineering from Oxford Brookes University and an MSc in Electronics Engineering from the University of Udine.
Before joining academia, Dr Cecotti spent eight years in automotive R&D: first as Senior and then Principal Control Systems Engineer at Tata Motors European Technical Centre (Coventry), working on hybrid-electric vehicles and the UK Autodrive autonomous demonstrator; then as Advanced Control Systems Engineer in Dyson's Electric Car Team, defining the Vehicle Supervisory Controller architecture and ISO 26262-aligned development processes.
He joined Cranfield as a Research Fellow, was appointed Lecturer in 2019, and has served as Course Director of the MSc CAVE since 2023. He is a Fellow of the Higher Education Academy (FHEA).
His research focuses primarily on two complementary themes: capturing the driving environment through multi-modal sensing (ultrasonic, RADAR, LiDAR, V2X) and modelling human driving behaviour for trajectory prediction and naturalistic automated navigation.
Research opportunities
Dr Cecotti's research spans two interconnected themes:
* Capturing the driving environment - developing multi-modal perception systems for automated vehicles using sensors beyond the conventional camera–LiDAR combination: novel ultrasonic arrays, automotive RADAR, infrastructure-mounted LiDAR, and V2X communication. Active industrial partnerships include Calyo, Oxford RF, Ohmio, and Benedex.
* Modelling human driving behaviour - building data-driven models of how drivers interact with the road, covering trajectory prediction, drowsiness and distraction detection, and the design of automated manoeuvres that replicate naturalistic human driving.
PhD opportunities are available in both areas, with particular interest in industry-sponsored projects. Prospective students are welcome to contact Dr Cecotti to discuss potential topics.
Current activities
Dr Cecotti recently completed Driven by Sound (Innovate UK ref. 10059986, 2024–2025), a £912k collaborative project in which he served as Principal Investigator for Cranfield's £267k share. The project integrated Calyo's novel ultrasonic sensor technology into a passenger car to deliver Cranfield's first autonomous vehicle demonstrator. He is currently pursuing further funding across several Innovate UK and Horizon Europe competitions.
As Course Director, Dr Cecotti oversees the full student journey on the CAVE programme: from applicant selection and induction through to industrial project scoping, group presentations, and graduation. He recently led the MSc CAVE through successful IET and IMechE accreditation (backdated to 2020) and has guided student teams through the UTAC Challenge at the Autodrome de Linas-Montlhéry.
In addition to his Course Director role, Dr Cecotti leads the Vehicle Control Applications module for the MSc in Automotive Mechatronics and Advanced Motorsport Mechatronics, and the Group Design Project for the MSc in Connected and Autonomous Vehicle Engineering. He also supervises PhD and MSc students on research projects in Intelligent Mobility.
Publications
Articles In Journals
- Yin C, Cecotti M & Jiang H. (2026). Goal oriented trajectory prediction conditioned on reachable road context. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
- 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)
- Shen Q, Jiang H, Li A, Cecotti M, Yin C, .... (2025). Generating G2 continuity reference paths for autonomous vehicles at roundabouts. IEEE Transactions on Intelligent Transportation Systems, 26(9)
- 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
