IndyCar
Publications
​
Rank Position Forecasting in Car Racing
B. Peng, J. Li, S. Akkas, T. Araki, O. Yoshiyuki, J. Qiu
Proceedings of 35th IEEE International Parallel & Distributed Processing Symposium (IPDPS21)
Anomaly Detection Over Streaming Data: Indy500 Case Study
C. Widanage, J. Li, S. Tyagi, R. Teja, B. Peng, S. Kamburugamuve, D. Baum, D. Smith, J. Qiu, and J. Koskey
2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pp. 9–16, IEEE, 2019.
​
A fast video image detection using tensorflow mobile networks for racing cars
S. Akkas and S. S. Maini and J, Qiu
Stream Systems and Real-time Machine Learning (STREAM-ML) Workshop of IEEE Big Data Conference, IEEE, 2019.
Objectives
Equipment Malfunction Prediction and Alerts
Anomaly prediction can help engineers identify issues in the hardware before they become serious. Especially at the speed at which these cars travel, small issue can quickly snowball to become catastrophic.
Performance Tuning During Practice
Performance of hardware can be tuned to minute degrees based on insights provided by AI systems which can go beyond the level of pattern recognition capable by humans.
Recommendations with Evolving Driving Conditions
Track conditions are ever evolving, from humidity, ambient temperature and track temperature. All of these factors affect the way on-board systems perform in different ways. It can be hard for a human to take appropriate action at the appropriate time with such gradual change.
