This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Vehicle Velocity Prediction Using Artificial Neural Network and Effect of Real World Signals on Prediction Window
Technical Paper
2020-01-0729
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado. Synchronous data was gathered using a test vehicle equipped with sensors to measure ego vehicle position and velocity, ADAS-derived near-neighbor relative position and velocity, and infrastructure-derived transit time and signal phase and timing. The effect of different groups of input datasets on forward velocity prediction windows of 10, 15, 20, and 30 seconds was studied. The developed algorithm was tested on an NVIDIA DRIVE PX2. This research shows that the lowest Mean Absolute Error (MAE) of future velocity prediction is with a fully inclusive dataset in 10-second velocity prediction windows. It was observed that GPS data, current vehicle velocity data, and vehicle-to-infrastructure data were the most influential parameters for prediction accuracy. Additionally, we have demonstrated that the LSTM neural network used for velocity prediction can be implemented in real-time using an NVIDIA DRIVE PX2. Integration of velocity prediction into fuel economy strategies and autonomous vehicle technology have the potential to improve fuel economy and safety. Future work involves demonstrating these two use cases in a physical vehicle using NVIDIA DRIVE PX2.
Recommended Content
Authors
- Tushar Gaikwad - Western Michigan University
- Farhang Motallebiaraghi - Western Michigan University
- Zachary Asher - Western Michigan University
- Alvis Fong - Western Michigan University
- Rick Meyer - Western Michigan University
- Aaron Rabinowitz - Colorado State University
- Thomas Bradley - Colorado State University
Topic
Citation
Gaikwad, T., Rabinowitz, A., Motallebiaraghi, F., Bradley, T. et al., "Vehicle Velocity Prediction Using Artificial Neural Network and Effect of Real World Signals on Prediction Window," SAE Technical Paper 2020-01-0729, 2020, https://doi.org/10.4271/2020-01-0729.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 |
Also In
References
- Fagnant , D.J. and Kockelman , K. Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations Transportation Research Part A: Policy and Practice 77 167 181 2015
- Waschl , H. , Kolmanovsky , I. , and Willems , F. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions Springer Verlag 2019
- Birky , A. , Laughlin , M. , Tartaglia , K. , Price , R. et al. 2018
- Nkoro , A.B. and Vershinin , Y.A. Current and Future Trends in Applications of Intelligent Transport Systems on Cars and Infrastructure IEEE 17th International Conference on Intelligent Transportation Systems 2014 10.1109/ITSC.2014.6957741
- Dadras , S. , Jamshidi , H. , Dadras , S. , and Pilutti , T.E. Novel Stop Sign Detection Algorithm Based on Vehicle Speed Profile 2019 American Control Conference (ACC) 2019 3994 3999
- Dadras , S. Path Tracking Using Fractional Order Extremum Seeking Controller for Autonomous Ground Vehicle SAE Technical Paper 2017-01-0094 2017 https://doi.org/10.4271/2017-01-0094
- Demuth , H.B. , Beale , M.H. , De Jess , O. , and Hagan , M.T. Neural Network Design Second Martin Hagan USA 2014
- Bender , F.A. , Kaszynski , M. , and Sawodny , O. Drive Cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles IEEE Trans Veh Technol 62 3581 3592 2013
- Donateo , T. , Pacella , D. , and Laforgia , D. Development of an Energy Management Strategy for Plug-In Series Hybrid Electric Vehicle Based on the Prediction of the Future Driving Cycles by ICT Technologies and Optimized Maps SAE Technical Paper 2011-01-0892 2011 https://doi.org/10.4271/2011-01-0892
- Sun , C. , Moura , S.J. , Hu , X. , Hedrick , J.K. , and Sun , F. Dynamic Traffic Feedback Data Enabled Energy Management in Plug-In Hybrid Electric Vehicles IEEE Trans Control Syst Technol 23 1075 1086 2015
- Sun , C. , Hu , X. , Moura , S.J. , and Sun , F. Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles IEEE Trans Control Syst Technol 23 1197 1204 2015
- Sun , C. , Hu , X. , Moura , S.J. , and Sun , F. Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles IEEE Trans Control Syst Technol 23 1197 1204 2015
- Gong , Q. , Li , Y. , and Peng , Z. Power Management of Plug-In Hybrid Electric Vehicles Using Neural Network Based trip Modeling 2009 American Control Conference 2009 4601 4606
- Rezaei , A. and Burl , J.B. Prediction of Vehicle Velocity for Model Predictive Control IFAC-PapersOnLine 48 257 262 2015
- Gong , Q. , Li , Y. , and Peng , Z.R. Trip-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles IEEE Trans Veh Technol 57 3393 3401 2008
- Mohd Zulkefli , M.A. , Zheng , J. , Sun , Z. , and Liu , H.X. Hybrid Powertrain Optimization with Trajectory Prediction Based on Inter-Vehicle-Communication and Vehicle-Infrastructure-Integration Transp Res Part C: Emerg Technol 45 41 63 2014
- Gaikwad , T.D. , Asher , Z.D. , Liu , K. , Huang , M. , and Kolmanovsky , I.
- Lefèvre , S. , Sun , C. , Bajcsy , R. , and Laugier , C. Comparison of Parametric and Non-Parametric Approaches for Vehicle Speed Prediction American Control Conference June 4-6 2014 10.1109/ACC.2014.6858871
- Olabiyi , O. , Martinson , E. , Chintalapudi , V. , and Guo , R. 2017
- Lemieux , J. and Ma , Y. Vehicle Speed Prediction Using Deep Learning IEEE Vehicle Power and Propulsion Conference 2015 1 5
- Zhang , F. , Xi , J. , and Langari , R. Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications IEEE Transactions on Intelligent Transportation Systems 18 416 430 2017
- Sun , C. , Sun , F. , Hu , X. , Hedrick , J. , et al. Integrating Traffic Velocity Data into Predictive Energy Management of Plug-In Hybrid Electric Vehicles American Control Conference Chicago, IL 2015
- Hellstrom , E. and Jankovic , M. A Driver Model for Velocity Tracking with Look-Ahead American Control Conference Chicago, IL 2015
- Liu , K. , Asher , Z. , Gong , X. , Huang , M. , and Kolmanovsky , I. Vehicle Velocity Prediction and Energy Management Strategy Part 1: Deterministic and Stochastic Vehicle Velocity Prediction Using Machine Learning SAE Technical Paper 2019-01-1051 2019 https://doi.org/10.4271/2019-01-1051
- Olah , C. et al. 2015 http://colah.github.io/posts/2015-08-Understanding-LSTMs/
- https://github.com/omerbsezer/LSTM_RNN_Tutorials_with_Demo
- Huang , C.-J. and Kuo , P.-H. A Deep CNN-LSTM Model for Particulate Matter (PM2. 5) Forecasting in Smart Cities Sensors 18 7 2220 2018
- https://developer.nvidia.com/drive/documentation
- Asher , Z.D. , Tunnell , J.A. , Baker , D.A. , Fitzgerald , R.J. et al. Enabling Prediction for Optimal Fuel Economy Vehicle Control SAE Technical Paper 2018-01-1015 2018 https://doi.org/10.4271/2018-01-1015