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Details of Grant 

EPSRC Reference: EP/S001956/2
Title: Towards Energy Efficient Autonomous Vehicles via Cloud-Aided Learning
Principal Investigator: Zhao, Dr D
Other Investigators:
Researcher Co-Investigators:
Project Partners:
AVL Hartree Centre
Department: School of Engineering
Organisation: University of Glasgow
Scheme: EPSRC Fellowship - NHFP
Starts: 01 December 2020 Ends: 28 March 2022 Value (£): 158,402
EPSRC Research Topic Classifications:
Control Engineering
EPSRC Industrial Sector Classifications:
Transport Systems and Vehicles
Related Grants:
Panel History:  
Summary on Grant Application Form
There are two ongoing revolutions in modern automotive industry. The first is the development of autonomous transportation systems which is leading to greatly improved safety, traffic economy, environment and passenger comfort. The second on is the development of advanced propulsion systems, envisaging reduced fuel consumption and exhaust emissions. The intersection of the autonomous transportation systems and advanced propulsion systems is the future trend, which has been revealed in the strategic partnerships between automakers and IT companies. However, there is a big challenge that the environment information collected by autonomous vehicles is poorly used in propulsion systems, especially when vehicles run in fast changing conditions. Only few examples of using environment data to optimise energy efficiency can be found, although the potential benefits in fuel reduction and mission flexibility are great.

This project aims to tackle this challenge by developing a cloud-aided learning framework to merge the two themes as integrity. To establish awareness of the environment, the onboard sensors of autonomous vehicles including cameras, light detecting and ranging (LiDAR), ultrasonic, and radar are used to perceive the environment over short distances. The GPS and intelligent transportation systems are used to perceive the environment at a further distance. The combined information enables the autonomous vehicle to establish a comprehensive model of the external environment. Using advanced machine learning algorithms (e.g., dynamic Bayesian network), the environment information can be used to update the propulsion system model in real time. Combining the real-time updated model with dynamic optimisation methods (e.g., adaptive model predictive control), the optimal actions of the propulsion system can be obtained in s systematic way. Employing high performance computing resources on cloud, the computational intensive modelling and optimisation tasks can be cost-effectively addressed.

Aligning with the EPSRC Innovation Fellowship priority area of "Robotics and Artificial Intelligence Systems" through its focus on efficient transport, a cloud-in-the-loop testing platform will be built. This framework focuses on sensing, modelling, control, optimisation and computing of energy efficient autonomous vehicles. In a long term vision, the framework can be generalised from a single vehicle to connected and autonomous for further economic benefits.

Key Findings
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Organisation Website: http://www.gla.ac.uk