Towards Energy Efficient Autonomous Vehicles via Cloud-Aided Learning

Lead Research Organisation: Loughborough University
Department Name: Aeronautical and Automotive Engineering

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.

Planned Impact

This research will address several fundamental problems in developing future vehicle architecture, from both theoretical and application aspects. Finding a way to successfully utilise external information will break down a wall that currently exists between research in autonomous driving and engine control and, therefore, an energy efficient autonomous vehicle can be delivered. This research has considerable potential economic benefit. It embraces the driverless technology market worth up to £50 billion to the UK economy by 2035. Also, it anticipates the government's clean energy policy on low carbon vehicles up to 2040. Future developments in the UK automotive industry, including technical innovations and competitive advantages, will be greatly aided by this research that will provide a bridge between laboratory models and production vehicles. To make this happen, I will focus on the following dissemination and impact activities.

Advisory board meetings composed by both academia and industry will be organised to engage with partners from the start, ensuring a close and fruitful collaboration. The advisory board will lead to two-way conversations between academia and industry, allowing me to will act as an ambassador for research and innovation across interfaces.

A website hosted by LU will be established to increase visibility. Links to the publications, testing videos, tutorial example and a part of algorithm codes would be made available. Similar information would also be published on my homepage. LinkedIn and ResearchGate will also be used to disseminate the research outcomes.

The research outcomes will be presented in industrial workshops and exhibitions including the Future Powertrain Exhibitions and the Consumer Electronics Show. The attendance will attract the interest of many suppliers from decision-making industry and data industry. Also they are ideal opportunities to ensure the technology being generalised.

Working with the host organisation and industrial partners, I will apply for international patents throughout the project.

I will give free webinars through the platform provided by IEEE and Mathworks. The latest research findings would be presented to public through the "IEEE Tech Insider Webinars" and the "Matlab and Simulink Webinars for Academia". Public lectures will be given at Open Days and Student Visits at LU. I will also become a STEM ambassador to disseminate ideas by initiating an activity through the STEM ambassador hubs.

High quality papers will be published in top tier IEEE journals. The research outcomes be also submitted to the most prestigious conferences including IEEE Conference on Decision and Control and International Conference on Robotics and Automation and SAE Congress. The publications will be made open access and shared via the open LU Publications Information Login (LUPIN) system.

Prototype testing will be given in Smart Mobility Living Lab facility, London and the engagement with common end users and media will be emphasised.

I will give consultations to Department for Transportation (DfT) of UK for the policy making of vehicles manufacturing transportation infrastructure in the future. I will work closely with Prof Ricardo Martinez-Botas, a science advisory council member of DfT, to offer the technical support.

AI will organise a workshop to disseminate the results to representatives from industrial partners in LU. I will invite established partners with whom I have connections and potential partners to join the industrial workshop. By successful demos, this pioneering work can build the confidence of automakers and attract more industrial partners.

Publications

10 25 50
 
Description By marrying vehicles with artificial intelligence, vehicles will learn to read their surroundings. It enables the vehicles can make better decisions that are both safe and efficient, whatever the driving conditions and environment.
Exploitation Route The outcomes can be directly used by the manufacturers and developers of vehicles, robotics, drones and other autonomous systems.
Sectors Aerospace

Defence and Marine

Agriculture

Food and Drink

Construction

Digital/Communication/Information Technologies (including Software)

Electronics

Energy

Healthcare

Manufacturing

including Industrial Biotechology

Transport

 
Description The research outcome has attracted interests from industry such as Ricardo and UbiPOS. They are keen to use the developed methodologies to improve the autonomy level of their devices in autonomous driving.
First Year Of Impact 2021
 
Description Capital Equipment Funding of Department of Aeronautical and Automotive Engineering, Loughborough University
Amount £8,200 (GBP)
Organisation Loughborough University 
Sector Academic/University
Country United Kingdom
Start 02/2018 
End 07/2018
 
Description Cooperative Vehicular Communication and Robust Control for Mixed Vehicle Platoons
Amount £74,000 (GBP)
Funding ID NAF\R1\201213 
Organisation The Royal Society 
Sector Charity/Non Profit
Country United Kingdom
Start 03/2020 
End 03/2024
 
Description EPSRC Capital Award
Amount £35,000 (GBP)
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 05/2018 
End 07/2018
 
Description Eco-Safe Driving for Automated Vehicles based on Driving Environment Prediction
Amount ¥100,000 (CNY)
Organisation Tsinghua University China 
Sector Academic/University
Country China
Start 01/2020 
End 12/2021
 
Description Flagship legged robotic platform for the excellence of research and teaching
Amount £100,000 (GBP)
Organisation University of Glasgow 
Sector Academic/University
Country United Kingdom
Start 11/2021 
End 08/2022
 
Description Human-Autonomy-Infrastructure Teaming
Amount £100,000 (GBP)
Organisation University of Glasgow 
Sector Academic/University
Country United Kingdom
Start 01/2022 
End 07/2022
 
Description PhD studentship on low carbon autonomous vehicles
Amount £60,000 (GBP)
Organisation Loughborough University 
Sector Academic/University
Country United Kingdom
Start 01/2019 
End 01/2022
 
Description Travel award for the 2018 Workshop on the Electrification of Road Transport
Amount £2,000 (GBP)
Organisation Newton Fund 
Sector Public
Country United Kingdom
Start 12/2018 
End 12/2018
 
Title Real time optimisation based on model learning 
Description This algorithms enables the real time optimisation of dynamic systems from recursive system model identification. 
Type Of Material Computer model/algorithm 
Year Produced 2019 
Provided To Others? Yes  
Impact This method makes the evolving modelling of dynamic systems possible. It can be generally used in propulsion systems, vehicles, robotics and other machine intelligence systems. 
 
Description Collaboration with Cranfield University 
Organisation Cranfield University
Country United Kingdom 
Sector Academic/University 
PI Contribution Algorithm design.
Collaborator Contribution On- and off- road test track for autonomous vehicles with fully sensored road.
Impact In my proposal submitted to the EPSRC Trustworthy Autonomous Systems Hub, Cranfield University works as an academic partner. This is a multi-disciplinary research across control engineering and communications.
Start Year 2021
 
Description Collaboration with the company Ricardo 
Organisation Ricardo UK Ltd
Country United Kingdom 
Sector Private 
PI Contribution Algorithm and code.
Collaborator Contribution Information and testing data.
Impact A joint proposal was submitted to the EPSRC Trustworthy Autonomous Systems Hub. This is a multi-disciplinary research across control engineering and automotive engineering.
Start Year 2021
 
Description Collaboration with the company UbiPOS 
Organisation UbiPOS UK Ltd
Country United Kingdom 
Sector Private 
PI Contribution Guidance algorithm design.
Collaborator Contribution Testing data.
Impact A joint proposal was submitted to the EPSRC Trustworthy Autonomous Systems Hub. This is a multi-disciplinary research across control engineering and civil engineering.
Start Year 2021
 
Description Collaborative research on communications of connected vehicles with scholars from Beihang University, China 
Organisation Beihang University
Country China 
Sector Academic/University 
PI Contribution Experimental verification
Collaborator Contribution Algorithms design.
Impact Three journal papers and three conference have been finished together. We secured the Royal Society-Newton Advanced Fellowship together from the Royal Society.
Start Year 2019
 
Description Collaborative research on connected vehicles with scholars from Sun Yat-sen University, China 
Organisation Sun Yat-Sen University
Country China 
Sector Academic/University 
PI Contribution Designing algorithms for the collaborative research.
Collaborator Contribution Providing test data.
Impact Four journal papers have been finished together and are being reviewed.
Start Year 2018
 
Description Collaborative research on eco-driving of autonomous vehicles with scholars from Tsinghua University, China 
Organisation Tsinghua University China
Country China 
Sector Academic/University 
PI Contribution The development of eco-driving algorithms.
Collaborator Contribution Experimental facilities.
Impact We have secured a grant together successfully.
Start Year 2019
 
Description Collaborative research on interactive decision making and safe control for autonomous vehicles with Loughborough University 
Organisation Loughborough University
Country United Kingdom 
Sector Academic/University 
PI Contribution Control algorithm design.
Collaborator Contribution Machine learning method design.
Impact We have submitted a joint proposal for EPSRC Trustworthy Autonomous Systems Hub.
Start Year 2021
 
Description Collaborative research on the optimisation of low carbon autonomous vehicles with University of Birmingham 
Organisation University of Birmingham
Country United Kingdom 
Sector Academic/University 
PI Contribution Algorithm design.
Collaborator Contribution Algorithm verification.
Impact We have finished one paper together on a prestigious journal. Another joint journal paper is under review.
Start Year 2019
 
Description Invited talk given at the Research Zoomposium at College of Science and Engineering, University of Glasgow 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Professional Practitioners
Results and Impact I gave a talk "Towards safe and trustworthy autonomous systems" at the Research Zoomposium at College of Science and Engineering, University of Glasgow. Collaborations with fellow academics are secured in applying EPSRC funding jointly.
Year(s) Of Engagement Activity 2021
 
Description Keynote talk given at the Workshop of Electrification and Road Transport 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact I gave keynotes at the workshop on fellowship application and energy efficient autonomous vehicles. Collaborations with researchers from Sun Yat-Sen University are secured for joint publications.
Year(s) Of Engagement Activity 2018
 
Description My current research is reported by the Low Carbon Vehicle magazine 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact My ongoing low carbon autonomous vehicles was highlighted in the Low Carbon Vehicle magazine in September 2018. It shows the strength of the vehicle research in Loughborough University, particularly in my team.
Year(s) Of Engagement Activity 2018
 
Description Participation of the Low Carbon Vehicle Event 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact I presented my poster "Vehicle energy recovery through an intelligent turbocharger assist system" at the Low Carbon Vehicle Event in September 2018. The audiences mainly come from industry. Several institutes showed their interest for further collaborative research.
Year(s) Of Engagement Activity 2018
 
Description Reported as the "Research Story of the Month" at Loughborough University in June 2018 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Undergraduate students
Results and Impact The research being done in my My EPSRC Innovation Fellowship was reported by the University Press. I was interviewed by the Vice Chancellor of Loughborough University. The report motivated many researchers to seek collaborations with me.
Year(s) Of Engagement Activity 2018
 
Description Research story published by the host institution 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Public/other audiences
Results and Impact My research on developing resilient autonomous platoons was reported by the Department of Aeronautical and Automotive Engineering at Loughborough University. It has attracted wide interest from academia, industry, students and general public.
Year(s) Of Engagement Activity 2020
 
Description Won the 1st place of the Best Poster Award at the Future Powertrain Conference 2018 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact My poster won the highest prize in the poster competition. It helps me to secure collaborations with researchers from University of Birmingham. We have worked together on grant proposals and publications.
Year(s) Of Engagement Activity 2018
 
Description Won the 2nd place of the Best Poster Award at the Future Powertrain Conference 2019 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact The Future Powertrain Conference is the flagship conference in automotive propulsion systems in the UK. My poster "Towards energy efficient autonomous vehicles" won the 2nd place in the Best Poster Award competition. My poster attracts wide interests from multiple industry including JLR and NPL.
Year(s) Of Engagement Activity 2019