Improving the safety and reliability of self-driving cars
By Stuart Pallister
SMU Office of Research – Autonomous driving systems (ADSs) are complex as they consist of modules such as perception, localisation, prediction, motion planning and control. Each module performs specific tasks which can enable self-driving cars to operate safely and efficiently.
For Xie Xiaofei, Assistant Professor of Computer Science at Singapore Management University, the perception module is of paramount importance as it effectively serves as the ‘eyes’ of the ADS as it allows the self-driving vehicle to perceive and understand its surroundings.
In their grant application proposal, Professor Xie and his collaborator, Dr Liu Yang of Nanyang Technological University (NTU), state that the perception module serves as a ‘vital link between the vehicle and its environment.’
The research project, funded by a Ministry of Education Academic Research Funding (AcRF) Tier 2 grant, is due to start in August 2024 and is expected to last three years. It aims to assess the reliability and robustness of the perception module, which relies on various sensors including cameras, radar, and light detection (LiDAR) sensors to interpret road and traffic conditions.
The objective of the project, the grant proposal states, will be to develop new technologies that ‘assess the quality and reliability of the perception module in an ADS with respect to vehicle motion and understand the impact of perception errors on other modules of ADSs such as decision-making.’
“Like human beings, self-driving vehicles need to understand the road conditions, the traffic, whether there are other vehicles or obstacles,” Professor Xie told the Office of Research. “So this is the first stage and now the driving system has some basic understanding of the environment. Then you have the planning module. Based on the traffic situation, I need to plan a route to get to my destination. And finally comes the control module, turning left or right based on the perception and plan.”
Understanding ADS
However, software and module issues can have an impact on the robustness of the overall system. Professor Xie points out that, while most studies have focused on the robustness of the perception module, these often overlook the broader impact of perception errors on the entire ADS.
“So, in this project we will test the perception module but at the same time we will also consider the other modules like planning and control.
“You can make some errors with the perception module but in planning we can mitigate them. However, there are some perception errors that have a significant influence on planning and on the whole system, so we need to understand the relationships and influence of the different modules. That’s our focus.”
According to the grant proposal, the researchers aim to develop advanced error prediction methods to ‘enable proactive mitigation strategies … and enhance the quality of reliability of perception modules in ADSs.’
“This is complex. Our focus is the perception module as this is very important, but we will also consider the influence of this module on the others. This is a key difference between our project and other existing projects.”
The project is expected to yield a series of top-tier journal and conference papers but Professor Xie, whose research has previously focused on software quality assurance, hopes they will also be able to “develop a software system to automatically test self-driving systems.”
Driving the Smart City
He hopes this project ‘will help to advance’ the Singapore Government’s Smart Urban Mobility Project, which seeks to enhance the country’s public transport systems.
“Our long-term goal is to contribute to the Singapore smart city.”
Initially, the project will deploy simulator-based software systems. After that, the plan is to move on to conducting tests on a small, unmanned vehicle, before seeking to evaluate the system on a self-driving car provided by the industry collaborators.
“Once we have such a system, we can use it to test the autonomous driving car and then report on potential issues.”
“A lot of companies are developing self-driving systems, but how can you ensure your system is robust and safe? This is our objective as we’ll be developing software to test and evaluate these systems.”
“We won’t distinguish between perception errors, planning or control errors. We just say this is a black box system and, in this project, we will open the black box.”
By Stuart Pallister
SMU Office of Research – Autonomous driving systems (ADSs) are complex as they consist of modules such as perception, localisation, prediction, motion planning and control. Each module performs specific tasks which can enable self-driving cars to operate safely and efficiently.
For Xie Xiaofei, Assistant Professor of Computer Science at Singapore Management University, the perception module is of paramount importance as it effectively serves as the ‘eyes’ of the ADS as it allows the self-driving vehicle to perceive and understand its surroundings.
In their grant application proposal, Professor Xie and his collaborator, Dr Liu Yang of Nanyang Technological University (NTU), state that the perception module serves as a ‘vital link between the vehicle and its environment.’
The research project, funded by a Ministry of Education Academic Research Funding (AcRF) Tier 2 grant, is due to start in August 2024 and is expected to last three years. It aims to assess the reliability and robustness of the perception module, which relies on various sensors including cameras, radar, and light detection (LiDAR) sensors to interpret road and traffic conditions.
The objective of the project, the grant proposal states, will be to develop new technologies that ‘assess the quality and reliability of the perception module in an ADS with respect to vehicle motion and understand the impact of perception errors on other modules of ADSs such as decision-making.’
“Like human beings, self-driving vehicles need to understand the road conditions, the traffic, whether there are other vehicles or obstacles,” Professor Xie told the Office of Research. “So this is the first stage and now the driving system has some basic understanding of the environment. Then you have the planning module. Based on the traffic situation, I need to plan a route to get to my destination. And finally comes the control module, turning left or right based on the perception and plan.”
Understanding ADS
However, software and module issues can have an impact on the robustness of the overall system. Professor Xie points out that, while most studies have focused on the robustness of the perception module, these often overlook the broader impact of perception errors on the entire ADS.
“So, in this project we will test the perception module but at the same time we will also consider the other modules like planning and control.
“You can make some errors with the perception module but in planning we can mitigate them. However, there are some perception errors that have a significant influence on planning and on the whole system, so we need to understand the relationships and influence of the different modules. That’s our focus.”
According to the grant proposal, the researchers aim to develop advanced error prediction methods to ‘enable proactive mitigation strategies … and enhance the quality of reliability of perception modules in ADSs.’
“This is complex. Our focus is the perception module as this is very important, but we will also consider the influence of this module on the others. This is a key difference between our project and other existing projects.”
The project is expected to yield a series of top-tier journal and conference papers but Professor Xie, whose research has previously focused on software quality assurance, hopes they will also be able to “develop a software system to automatically test self-driving systems.”
Driving the Smart City
He hopes this project ‘will help to advance’ the Singapore Government’s Smart Urban Mobility Project, which seeks to enhance the country’s public transport systems.
“Our long-term goal is to contribute to the Singapore smart city.”
Initially, the project will deploy simulator-based software systems. After that, the plan is to move on to conducting tests on a small, unmanned vehicle, before seeking to evaluate the system on a self-driving car provided by the industry collaborators.
“Once we have such a system, we can use it to test the autonomous driving car and then report on potential issues.”
“A lot of companies are developing self-driving systems, but how can you ensure your system is robust and safe? This is our objective as we’ll be developing software to test and evaluate these systems.”
“We won’t distinguish between perception errors, planning or control errors. We just say this is a black box system and, in this project, we will open the black box.”
Fast charging electric vehicles with stable high-energy density lithium-ion batteries
A KERI team led by Dr. Choi Jeong Hee developed an aluminum oxide-based surface coating for anode materials. A simple process for treating the surface, rather than the materials inside the electrode, prevents irreversible lithium loss.
A research team led by Dr. Choi Jeong Hee at the Korea Electrotechnology Research Institute (KERI) Battery Materials and Process Research Center, in cooperation with a Hanyang University team mentored by Professor Lee Jong-Won and a Kyunghee University team mentored by Professor Park Min-Sik, developed a core technology to ensure the charging/discharging stability and long-life of lithium-ion batteries under fast-charging conditions.
A crucial prerequisite for the widespread adoption of electric vehicles (EVs) is the enhancement of lithium-ion battery performance in terms of driving range and safety. Fast charging is also essential for user convenience. However, increasing the energy density of lithium-ion batteries necessitates thicker electrodes, which can lead to battery degradation and performance deterioration during rapid charging.
To address this issue, the KERI team discovered a solution by partially coating the surface of the anode of the lithium-ion battery with aluminum oxide (Al2O3) particles smaller than 1 micrometer (㎛). While many researchers worldwide have concentrated on the materials within the electrode, such as introducing functional nanotechnology into anode materials like graphite, Dr. Choi's team employed a straightforward processing technique to coat the electrode's surface with aluminum oxide.
Low in cost, excellent in electrical insulation and heat resistance, chemically stable, and possessing good mechanical properties, aluminum oxide is widely used in various ceramics. The KERI researchers found that aluminum oxide particles effectively control the interface between the anode and the electrolyte in lithium-ion batteries, forming an interfacial highway for efficient Li+ transport. This prevents the electrodeposition of lithium (an irreversible change that makes the lithium unavailable for additional charging and discharging) during fast charging, thereby ensuring the stability and lifespan of the lithium-ion battery during charging and discharging.
Another advantage of this technology is that it enables an increase in the energy density of lithium-ion batteries. Introducing other functional materials into the electrode's interior to improve performance and stability often complicates the synthesis process and reduces the amount of reversible lithium (initial coulombic efficiency). It also increases the electrode thickness, leading to performance deterioration under fast charging conditions. However, the KERI technology involves surface treatment of the graphite anode, rather than modifying the interior active graphite materials. This approach achieves stable performance even under fast charging conditions for high-energy-density thick-film electrodes without a loss in the amount of reversible lithium.
Through various tests, the team confirmed that the high-energy-density anode coated with aluminum oxide (4.4 mAh/cm²) exhibits world-class performance, maintaining more than 83.4% of its capacity (residual capacity ratio) even after 500 cycles of rapid charging. They have verified this performance with pouch cells of up to 500mAh. The team is now planning to scale up the technology to make it applicable to large-area, medium- to large-capacity cells.
"Convenient fast charging and the energy density of lithium-ion batteries have long been considered a trade-off, which has hindered the widespread adoption of electric vehicles," said Dr. Choi. "Our work will help develop stable, high-energy-density lithium-ion batteries capable of fast charging. This advancement will contribute to the wider adoption of EVs and support the achievement of national carbon neutrality."
The excellence of this work has been demonstrated by patent registrations in both Korea and the United States. The findings were also published in a recent edition of Advanced Functional Materials, an internationally renowned journal in the field of materials engineering (JCR Impact Factor 19, top 3.7%).
KERI is a government-funded research institute under the National Research Council of the Ministry of Science and ICT. This research was funded by the Samsung Future Technology Project and the Ministry of Trade, Industry and Energy's Industrial Technology Innovation Project (high-power battery and charging system technology for EVs). <KERI>
KERI researchers are partially coating aluminum oxide on the surface of the anode of a lithium-ion battery.
Aluminum oxide dispersion (left) and anode for lithium ion battery coating it on.
Aluminum oxide dispersion (left) and anode for lithium ion battery coating it on.
CREDIT
Korea Electrotechnology Research Institute(KERI)
Korea Electrotechnology Research Institute(KERI)
JOURNAL
Advanced Functional Materials
ARTICLE TITLE
Multi-Interface Strategy for Electrode Tailoring Toward Fast-Charging Lithium-Ion Batteries
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