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Projects: Projects for Investigator
Reference Number EP/S001956/1
Title Towards Energy Efficient Autonomous Vehicles via Cloud-Aided Learning
Status Completed
Energy Categories Energy Efficiency(Transport) 15%;
Not Energy Related 70%;
Other Cross-Cutting Technologies or Research(Other Supporting Data) 15%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 75%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 25%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Dr D Zhao
No email address given
Aeronautical and Automotive Engineering
Loughborough University
Award Type Standard
Funding Source EPSRC
Start Date 29 June 2018
End Date 30 November 2020
Duration 29 months
Total Grant Value £526,502
Industrial Sectors Transport Systems and Vehicles
Region East Midlands
Programme ISCF - Skills
Investigators Principal Investigator Dr D Zhao , Aeronautical and Automotive Engineering, Loughborough University (100.000%)
  Industrial Collaborator Project Contact , AVL Powertrain UK Ltd (0.000%)
Project Contact , Hartree Centre (0.000%)
Web Site
Abstract 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.
Publications (none)
Final Report (none)
Added to Database 21/02/19