This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Cloud-Based Vehicle Velocity Prediction Based on Seasonal Autoregressive Integrated Moving Average Processes
Technical Paper
2018-01-1178
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Intelligent transportation systems (ITSs) and advanced driver assistance systems (ADASs) are considered as key technologies for improving road safety, fuel economy and driving comfort. For various ITSs and ADASs, e.g. for energy management systems in hybrid electric vehicles and adaptive cruise control systems, the velocity prediction of the ego vehicle and the target vehicles can substantially improve the system performance and is therefore an important building block. In this paper a novel concept for cloud-based vehicle velocity prediction using seasonal autoregressive integrated moving average (SARIMA) processes is proposed. The concept relies on collecting velocity profiles and estimating SARIMA processes using the collected velocity profiles for distinct road segments in a cloud (offboard). When a vehicle enters a road segment, the SARIMA model for the road segment is transmitted from the cloud to the vehicle for velocity prediction (onboard). The actual velocity profiles are transmitted from the vehicle back to the cloud for updating the SARIMA models. For quantifying the prediction uncertainty, an analytical formulation of the prediction bounds is provided. Such an analytical formulation is essential for robust control design but not available in most existing concepts. Throughout the paper the theoretical findings are evaluated utilizing real measurement data from highway driving. Moreover, the proposed concept is compared with a concept from the literature relying on artificial neural networks. The evaluation and comparison indicate that the concept based on SARIMA processes provides a good compromise between prediction accuracy and computational effort. Particularly real-time requirements on velocity prediction in many ITSs and ADASs can be satisfied.
Recommended Content
| Technical Paper | The Application of Advanced Vehicle Navigation in BMW Driver Assistance Systems |
| Technical Paper | Impact of Connectivity and Automation on Vehicle Energy Use |
| Technical Paper | Architectural Concepts for Fail-Operational Automotive Systems |
Authors
Topic
Citation
Lin, X. and Görges, D., "Cloud-Based Vehicle Velocity Prediction Based on Seasonal Autoregressive Integrated Moving Average Processes," SAE Technical Paper 2018-01-1178, 2018, https://doi.org/10.4271/2018-01-1178.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| [Unnamed Dataset 1] | ||
| [Unnamed Dataset 2] |
Also In
References
- Sun, G. , “Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles,” IEEE Transactions on Control Systems Technology 23(3):1197-1204, 2014, doi:10.1109/TCST.2014.2359176.
- Lang, D., Schmied, R., and del Re, L. , “Prediction of Preceding Driver Behavior for Fuel Efficient Cooperative Adaptive Cruise Control,” SAE Int. J. Engines 7(1):14-20, 2014, doi:10.4271/2014-01-0298.
- Ovcharova, N., Fausten, M. and Gauterin, F. , “Effectiveness of Forward Collision Warnings for Different Driver Attention States,” Proceedings of the 2012 Intelligent Vehicles Symposium, 2012, 944-949, doi:10.1109/IVS.2012.6232232.
- Moser, D., Waschl, H., Schmied, R., Efendic, H. et al. , “Short Term Prediction of a Vehicle's Velocity Trajectory Using ITS,” SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 8(2):364-370, 2015, doi:10.4271/2015-01-0295.
- Di Cairano, S., Bernardini, D., Bemporad, A., and Kolmanovsky, I. , “Stochastic MPC with Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management,” IEEE Transactions on Control Systems Technology 22(3):1018-1031, 2014, doi:10.1109/TCST.2013.2272179.
- Vogel, K. , “A Comparison of Headway and Time to Collision as Safety Indicators,” Accident Analysis & Prevention 35(3):427-433, 2003.
- Krajzewicz, D. , “Traffic Simulation with SUMO-Simulation of Urban Mobility,” . In: Barceló J. , editor. Fundamentals of Traffic Simulation. (New York, Springer, 2010), 269-294, doi:10.1007/978-1-4419-6142-6_7.
- Treiber, M., Hennecke, A., and Helbing, D. , “Congested Traffic States in Empirical Observations and Microscopic Simulations,” Physical Review E 62(2):1805-1824, 2000.
- Zhang, G. , “Time Series Forecasting Using a Hybrid Arima and Neural Network Model,” Neurocomputing 50:159-175, 2003.
- Ghahramani, Z. and Jordan, M. , “Supervised Learning from Incomplete Data Via an EM Approach,” Advances in Neural Information Processing Systems 6:120-127, 1994.
- Lefèvre, S., Sun, C., Bajcsy, R. and Laugier, C. , “Comparison of Parametric and Non-parametric Approaches for Vehicle Speed Prediction,” American Control Conference, 2014, 3494-3499, doi: 10.1109/ACC.2014.6858871.
- Brockwell, P.J. and Davis, R.A. , “Time Series: Theory and Methods,” 2nd ed., (New York: Springer, 1991), doi: 10.1007/978-1-4899-0004-3.
- Brockwell, P.J. and Davis, R.A. , “Introduction to Time Series and Forecasting,” 2nd ed., (New York, Springer, 2002), ISBN:978-0-387-95351-9.
- Hyndman, R. J., Athanasopoulos, G., Razbash, S., Schmidt, D. et al. , Package ‘forecast’, May 2015. [Online]. Available: http://github.com/robjhyndman/forecast.
- Box, G.E. and Cox, D.R. , “An Analysis of Transformation,” Journal of the Royal Statistical Society. Series B (Methodological) 26:211-252, 1964.
- Hurvich, C.M. and Tsai, C.-L. , “Regression and Time Series Model Selection in Small Samples,” Biometrika 76(2):297-307, 1989, doi:10.1093/biomet/76.2.297.
- Chatfield, C. , “Calculating Interval Forecasts,” Journal of Business & Economic Statistics 11(2):121-135, 1993.
- Guerrero, V.M. , “Time-Series Analysis Supported by Power Transformations,” Journal of Forecasting 12(1):37-48, 1993, doi:10.1002/for.3980120104.
- Box, G.E.P. and Jenkins, G.M. , “Time Series Analysis: Forecasting and Control,” (San Francisco, Holden-Day, 1976).
- Kalabis, M. , “Steigerung der Energieeffizienz von Kraftfahrzeugen durch modellbasierte prädiktive Geschwindigkeits- und Abstandsregelung,” (München, Dr. Hut, 2013).