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.
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.
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.
Organisations
- Loughborough University (Lead Research Organisation)
- Beihang University (Collaboration)
- LOUGHBOROUGH UNIVERSITY (Collaboration)
- Sun Yat-sen University (Collaboration)
- CRANFIELD UNIVERSITY (Collaboration)
- UNIVERSITY OF BIRMINGHAM (Collaboration)
- UbiPOS UK LTD (Collaboration)
- Tsinghua University China (Collaboration)
- Ricardo UK Ltd (Collaboration)
- University of Glasgow (Fellow)
Publications
Zhou Q
(2021)
Modified Particle Swarm Optimization With Chaotic Attraction Strategy for Modular Design of Hybrid Powertrains
in IEEE Transactions on Transportation Electrification
Zhou Q
(2021)
Knowledge Implementation and Transfer With an Adaptive Learning Network for Real-Time Power Management of the Plug-in Hybrid Vehicle.
in IEEE transactions on neural networks and learning systems
Zhou J
(2022)
Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback
in IEEE Transactions on Intelligent Transportation Systems
Zhao D
(2020)
Real-Time Energy Management of the Electric Turbocharger Based on Explicit Model Predictive Control
in IEEE Transactions on Industrial Electronics
Zhao D
(2018)
An Integrated Framework on Characterization, Control, and Testing of an Electrical Turbocharger Assist
in IEEE Transactions on Industrial Electronics
Zhang R
(2022)
A Multi-Vehicle Longitudinal Trajectory Collision Avoidance Strategy Using AEBS With Vehicle-Infrastructure Communication
in IEEE Transactions on Vehicular Technology
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 |