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Sato Lab
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The lab measures and predicts vehicle exhaust emissions and driving behavior on real roads.
AboutAbout the lab
Measuring vehicle emissions on real roads
The Sato Lab researches ways to reduce the environmental burden of automobiles.
It measures, analyzes, and predicts the amounts of air pollutants and substances that cause global warming.
It also analyzes and predicts the operations and movements of the people who drive vehicles.
In its experiments, a vehicle equipped with instruments that measure exhaust emissions is driven on real roads to collect data.
Key terms
- Neural network
- A computational mechanism that imitates the connections between nerve cells in the brain and learns features from many examples
- Traffic flow simulator
- Software that recreates the flow of many vehicles on roads inside a computer
- Machine learning
- A method that lets computers find rules and patterns in large amounts of data
NewsLab news
A master's student won an Excellent Presentation Award at the three-university joint presentation meeting
A student won the Best Presentation Award at the Bachelor's Specific Project Presentation
The lab held a lab tour for third-year undergraduate students in the Department of Systems and Control Engineering
A paper by Associate Professor Sato and colleagues won the Paper Award of the Society of Automotive Engineers of Japan (JSAE) Awards
A master's student won the Best Paper Presentation Award of the Hakuseikai Systems and Control Course
ResearchResearch themes
The Sato Lab aims to reduce the environmental burden of automobiles within transportation systems. To that end, it works on measuring, analyzing, and predicting air pollutants and global warming substances, and on analyzing and predicting driver behavior.
*Illustrative imageComparing emissions in Tokyo and Los Angeles
How do emissions on real roads change depending on the driving environment? The lab compares the cases of Tokyo and Los Angeles.
*Illustrative imagePredicting ammonia emissions
This research uses neural networks to predict the amount of ammonia emitted by automobiles.
*Illustrative imageExhaust emission prediction model for gasoline vehicles
The lab is building a model that predicts the exhaust emissions of gasoline-engine vehicles, for use in traffic flow simulators.
*Illustrative imageDriver model reflecting driving characteristics
This research uses machine learning to build a driver model (a model representing the operations of the person driving) that reflects driving characteristics.
MembersFaculty
The members of the Sato Lab consist of faculty members, master's and undergraduate students, and assistant staff.As of 2025
- Associate ProfessorSusumu SatoPublications ↗
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Automobile exhaust gas” Ruben de Rijcke / CC BY-SA 3.0 (cropped)
- Comparing emissions in Tokyo and Los Angeles: “Los Angeles Freeway Traffic” 4X4 Blazer 1776 / CC BY 2.0 (cropped)
- Predicting ammonia emissions: “Artificial Neural Network with Chip” mikemacmarketing / photo on flickr / CC BY 2.0 (cropped)
- Exhaust emission prediction model for gasoline vehicles: “Salt Lake City smog haze skyline 03” Eltiempo10 / CC BY-SA 4.0 (cropped)
- Driver model reflecting driving characteristics: “Car dashboard on highway (Unsplash)” Arkady Lifshits overdriv3 / CC0 (cropped)
- Related lab (小酒研究室): “Cutaway of a MAN V8 Diesel engine” Olivier Cleynen / CC BY-SA 3.0 (cropped)
- Related lab (奥富・紋野研究室): “Monmouth castle point cloud, created with Photosynth 01” John Cummings / CC BY-SA 3.0 (cropped)
- Related lab (中臺研究室): “RF Fully Anechoic Room 2” Mihaelawojcik / CC BY 4.0 (cropped)
- Related lab (田中研究室): “Пиксели матрицы видеорегистратора” Gribkov / CC BY 4.0 (cropped)
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