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Predicting Lead Vehicle Velocity for Eco-Driving in the Absence of V2V Information
Technical Paper
2023-01-0220
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Accurately predicting the future behavior of the surrounding traffic, especially the velocity of the lead vehicle is important for optimizing the energy consumption and improve the safety of Connected and Automated Vehicles (CAVs). Several studies report methods to predict short-to-mid-length lead vehicle velocity using stochastic models or other data-driven techniques, which require availability of extensive data and/or Vehicle-to-Vehicle (V2V) communication. In the absence of connectivity, or in data-restricted cases, the prediction must rely only on the measured position and relative velocity of the lead vehicle at the current time. This paper proposes two velocity predictors to predict short-to-mid-length lead vehicle velocity. The first predictor is based on a Constant Acceleration (CA) with an augmented stop mode. The second one is based on a modified Enhanced Driver Model (EDM-LOS) with line-of-sight feature. Both predictors rely only on information on the present values of lead vehicle position and velocity to compute a future velocity estimate. An analysis is done to compare the prediction accuracy of the proposed predictors with different experimental driving data recorded using an OBD2 scanner plugged into a passenger vehicle. Finally, the predicted lead vehicle velocity is utilized to formulate time-gap constraints for the eco-driving optimal control problem, solved via Model Predictive Control (MPC). The energy savings of the considered velocity predictors are evaluated by performing a large-scale simulation study. The proposed velocity predictor provides closest energy savings to a wait-and-see solution for a CAV in absence of V2V communication.
Authors
- Vinith Kumar LAKSHMANAN - IFP Energies Nouvelles
- Shobhit Gupta - Ohio State University
- Stefano D'Alessandro - Ohio State University
- Matteo Spano - Ohio State University
- Dennis Kibalama - Ohio State University
- Stephanie Stockar - Ohio State University
- Marcello Canova - Ohio State University
- Ouafae El Ganaoui-Mourlan - IFP Energies Nouvelles
- Antonio Sciarretta - IFP Energies Nouvelles
Topic
Citation
LAKSHMANAN, V., Gupta, S., D'Alessandro, S., Spano, M. et al., "Predicting Lead Vehicle Velocity for Eco-Driving in the Absence of V2V Information," SAE Technical Paper 2023-01-0220, 2023, https://doi.org/10.4271/2023-01-0220.Also In
References
- Jacome , O. , Gupta , S. , Stockar , S. , and Canova , M. 2022
- Kamal , M.A.S. , Masakazu , M. , Junichi , M. , and Taketoshi , K. Ecological Driving Based on Preceding Vehicle Prediction Using MPC IFAC Proceedings 44 1 2011 3843 3848
- Lakshmanan , V.K. , Sciarretta , A. , and El Ganaoui-Mourlan , O. Cooperative Eco-Driving of Electric Vehicle Platoons for Energy Efficiency and String Stability IFAC-PapersOnLine 54 2 2021 133 139
- Sciarretta , A. and Ardalan , V. Energy Saving Potentials of CAVs Energy-Efficient Driving of Road Vehicles Cham Springer 2020 1 31
- Bhagdikar , P. , Gankov , S. , Rengarajan , S. , Sarlashkar , J. et al. Quantifying System Level Impact of Connected and Automated Vehicles in an Urban Corridor SAE Technical Paper 2022-01-0153 2022 https://doi.org/10.4271/2022-01-0153
- Hyeon , E. , Kim , Y. , Prakash , N. , and Stefanopoulou , A.G. Influence of Speed Forecasting on the Performance of Ecological Adaptive Cruise Control Dynamic Systems and Control Conference Utah 2019
- Asher , Z.D. , Baker , D.A. , and Bradley , T.H. Prediction Error Applied to Hybrid Electric Vehicle Optimal Fuel Economy IEEE Transactions on Control Systems Technology 26 6 2018 2121 2134
- Hyeon , E. 2022
- Treiber , M. and Krestig , A. Car-Following Models Based on Driving Strategies Traffic Flow Dynamics: Data, Models and Simulation Berlin, Heidelberg Springer 2013 181 204
- Gupta , S. , Deshpande S.R. , Tulpule , P. , Canova , M. et al. An Enhanced Driver Model for Evaluating Fuel Economy on Real-World Routes Advances in Automotive Control Orleans 2019 574 579
- Brackstone , M. and McDonald , M. Car-Following: A Historical Review Transportation Research Part F: Traffic Psychology and Behaviour 2 4 1999 181 196
- Li , Y. , Mingnuo , C. , and Wanzhong , Z. Investigating Long-Term Vehicle Speed Prediction Based on BP-LSTM Algorithms IET Intelligent Transport Systems 13 8 2019 1281 1290
- Gupta , S. , and Canova , M. Eco-Driving of Connected and Autonomous Vehicles with Sequence-to-Sequence Prediction of Target Vehicle Velocity IFAC Tokyo, Japan 2021 54 10 430 436
- Deshpande , S.R. , Gupta , S. , Gupta , A. , and Canova , M. Real-Time Eco driving Control in Electrified Connected and Autonomous Vehicles Using Approximate Dynamic Programing Journal of Dynamic Systems, Measurement, and Control 144 1 2022 011111
- Zhu , Z. , Gupta , S. , Pivaro , N. , Deshpande , S.R. et al. A GPU Implementation of a Look-Ahead Optimal Controller for Eco-Driving Based on Dynamic Programming 2021 European Control Conference (ECC) 899 904 2021
- Deshpande , S.R. , Gupta , S. , Kibalama , D. , Pivaro , N. et al. Benchmarking Fuel Economy of Connected and Automated Vehicles in Real World Driving Conditions via Monte Carlo Simulation ASME Dynamic Systems and Control Conference Pittsburgh, PA 2020
- Hegde , B. , O'Keefe , M. , Muldoon , S. , Gonder , J. et al. Real-World Driving Features for Identifying Intelligent Driver Model: Preprint SAE WCX World Congress Experience Digital Summit 2021
- Monteil , J. , O’Hara , N. , Cahill , V. , and Bouroche , M. Real-Time Estimation of Drivers’ Behaviour International Conference on Intelligent Transportation Systems 2015