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Drive Horizon: An Artificial Intelligent Approach to Predict Vehicle Speed for Realizing Predictive Powertrain Control
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Demand for predictive powertrain control is rapidly increasing with the recent advancement of Advanced Driving Assistance Systems (ADAS) and Autonomous Driving (AD). The full or semi-autonomous functions could be leveraged to realize better user acceptance as well as powertrain efficiency of the connected vehicle utilizing the proposed Drive Horizon. The sensors of automated driving provide perception of surrounding driving environment which is required to safely navigate the vehicle in real-world driving scenarios. The proposed Drive Horizon provides real-time forecast of driving environment that a vehicle will encounter during its entire travel. This paper summarizes the vehicle’s future speed prediction technique which is an integral part of Drive Horizon for optimized energy control of the vehicle. The prediction model has been developed that integrates information from multiple sources including vehicle GPS, traffic information and map data. Recurrent Neural Networks and Bayesian approaches including generative models have been studied for predicting the vehicle speed. In addition, utilization of connected data (live traffic and map) to enable long prediction horizons has also been considered in this study compared to the conventional using of in-vehicle sensors such as camera or radar. The developed speed prediction technique can be effectively integrated with vehicle’s energy management to improve its energy efficiency. The effectiveness of the proposed speed prediction technique has been verified by testing the prediction accuracy on different routes for the prediction range of 1 kilometer.
CitationLonari, Y., Kundu, S., Agrawal, M., and Bellary, S., "Drive Horizon: An Artificial Intelligent Approach to Predict Vehicle Speed for Realizing Predictive Powertrain Control," SAE Technical Paper 2020-01-0732, 2020, https://doi.org/10.4271/2020-01-0732.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Dadras, S. , “Path Tracking Using Fractional Order Extremum Seeking Controller for Autonomous Ground Vehicle,” SAE Technical Paper 2017-01-0094, 2017, doi:https://doi.org/10.4271/2017-01-0094.
- Dadras, S., Dadras, S., and Winstead, C. , “Resilient Control Design for Vehicular Platooning in an Adversarial Environment,” in 2019 American Control Conference (ACC), Philadelphia, PA, 2019, 533-538.
- Valera, J.J., Heriz, B., Lux, G., Caus, J. et al. , “Driving Cycle and Road Grade On-Board Predictions for the Optimal Energy Management in EV-PHEVs,” in EVS27, Barcelona, Spain, Nov. 17-20, 2013.
- Xiaolei, M., Zhuang, D., Zhengbing, H., Jihui, M. et al. , “Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction,” Sensors, 2017.
- Liu, K., Asher, Z., Gong, X., and Huang, M. , “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, doi:https://doi.org/10.4271/2019-01-1051.
- Altché, F. and de La Fortelle, A. , “An LSTM Network for Highway Trajectory Prediction,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, 2017, 353-359.
- Gal, Y. and Ghahramani, Z. , “A Theoretically Grounded Application of Dropout in Recurrent Neural Networks,” in Advances in Neural Information Processing Systems (2016), 1019-1027.
- Zhu, L. and Laptev, N. , “Deep and Confident Prediction for Time Series at Uber,” in 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, 2017, 103-110.
- Amini, A., Wilko, S., Guy, R., Brandon, A. et al. , “Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-Biasing,” in IEEE/IROS, 2018.
- Shieh, S., Ersal, T., and Peng, H. , “Pulse-and-Glide Operation for Parallel Hybrid Electric Vehicles with Step-Gear Transmission in Automated Car-Following Scenario with Ride Comfort Consideration,” in American Control Conference (ACC), 2019.
- Gaikwad, T., Asher, Z., Liu, K., and Huang, M. , “Vehicle Velocity Prediction and Energy Management Strategy Part 2: Integration of Machine Learning Vehicle Velocity Prediction with Optimal Energy Management to Improve Fuel Economy,” SAE Technical Paper 2019-01-1212, 2019, doi:https://doi.org/10.4271/2019-01-1212.
- Hochreiter, S., Schmidhuber, J. , “Long short-term memory. Neural computation.” (pp. 1735-1780), 1997.
- Sutskever, I., Vinyals, O., and Le, Q.V. , “Sequence to Sequence Learning with Neural Networks,” in Advances in neural information processing systems (2014) 3104-3112.
- Michael, W.D., Dustin, T., Edward, C., Jonas, K. et al. , “Analyzing the Role of Model Uncertainty for Electronic Health Records,” in ICML Workshop on Uncertainty & Robustness in Deep Learning, 2019.
- Deo, N. and Trivedi, M. , “Convolutional Social Pooling for Vehicle Trajectory Prediction,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, 1468-1476.
- Böhme, T.J. and Frank, B. , “Predictive Real-Time Energy Management,” in Frank, B. andBöhme, T.J. (eds), Hybrid Systems, Optimal Control and Hybrid Vehicles (Springer, 2017), 429-480.
- Kingma, D. and Welling, M. , “Auto-Encoding Variational Bayes,” in The 2nd International Conference on Learning Representations (ICLR), 2013.
- Yao, R., Liu, C., Zhang, L., and Peng, P. , “Unsupervised Anomaly Detection Using Variational Auto-Encoder Based Feature Extraction,” in 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), San Francisco, CA, 2019, 1-7.