This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Forecasting Short to Mid-Length Speed Trajectories of Preceding Vehicle Using V2X Connectivity for Eco-Driving of Electric Vehicles
Technical Paper
2021-01-0431
ISSN: 2641-9637, e-ISSN: 2641-9645
This content contains downloadable datasets
Annotation ability available
Sector:
Event:
SAE WCX Digital Summit
Language:
English
Abstract
In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory. The simulation results show that the proposed predictor’s improved predictions can reduce energy consumption by 5% over eco-driving with the baseline predictor.
Authors
Topic
Citation
Hyeon, E., Shen, D., Karbowski, D., and Rousseau, A., "Forecasting Short to Mid-Length Speed Trajectories of Preceding Vehicle Using V2X Connectivity for Eco-Driving of Electric Vehicles," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(4):1801-1809, 2021, https://doi.org/10.4271/2021-01-0431.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 |
Also In
SAE International Journal of Advances and Current Practices in Mobility
Number: V130-99EJ; Published: 2021-08-31
Number: V130-99EJ; Published: 2021-08-31
References
- Kamal , M.A.S. et al. Ecological Driving Based on Preceding Vehicle Prediction Using MPC IFAC Proceedings Volumes 44. 1 3843 3848 2011
- Lefèvre , S. , Sun , C. , Bajcsy , R. , and Laugier , C. Comparison of Parametric and Non-Parametric Approaches for Vehicle Speed Prediction 2014 American Control Conference 3494 3499 IEEE Jun 2014
- He , C.R. and Orosz , G. Saving Fuel Using Wireless Vehicle-To-Vehicle Communication 2017 American Control Conference (ACC) IEEE 4946 4951 2017
- Hyeon , E. , Kim , Y. , Prakash , N. , and Stefanopoulou , A.G. Influence of Speed Forecasting on the Performance of Ecological Adaptive Cruise Control ASME 2019 Dynamic Systems and Control Conference American Society of Mechanical Engineers Digital Collection Oct. 2019
- Vajedi , M. and Azad , N.L. Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control IEEE Transactions on Intelligent Transportation Systems 17 1 113 122 2015
- Kamal , M.A.S. , Mukai , M. , Murata , J. , and Kawabe , T. Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy IEEE Transactions on Control Systems Technology 21 3 831 841 2012
- Gipps , P.G. A Behavioral Car-Following Model for Computer Simulation Transportation Research Part B: Methodological 15 2 105 111 1981
- Treiber , M. , Hennecke , A. , and Helbing , D. Congested Traffic States in Empirical Observations and Microscopic Simulations Physical Review E 62 2 1805 2000
- 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 Transactions on Control Systems Technology 23 3 1075 1086 2014
- Moser , D. , Waschl , H. , Schmied , R. , Efendic , H. , and del Re , L. Short Term Prediction of a Vehicle’s Velocity Trajectory Using ITS SAE International Journal of Passenger Cars—Electronic and Electrical Systems 8 2015-01-0295 364 370 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
- Hyeon , E. , Kim , Y. , Prakash , N. , and Stefanopoulou , A.G. Short-Term Speed Forecasting Using Vehicle Wireless Communications 2019 American Control Conference 736 741 IEEE Jul. 2019
- Kim , N. , Karbowski , D. , and Rousseau , A. A Modeling Framework for Connectivity and Automation Co-Simulation SAE Technical Paper 2018-01-0607 2018
- Shen , D. , Han , J. , Jeong , J. , Karbowski , D. , and Rousseau , A. Receding Horizon Reference Governor for Implementable and Optimal Powertrain Aware Eco-Driving IFAC World Congr. Berlin, Germany Jul. 2020
- Atkeson , C.G. , Moore , A.W. , and Schaal , S. Locally Weighted Learning Lazy Learning Dordrecht Springer 1997 11 73
- SAE International Dedicated Short Range Communications (DSRC) Message Set Dictionary 2016
- Shen , D. , Karbowski , D. , and Rousseau , A. A Minimum Principle-Based Algorithm for Energy-Efficient Eco-Driving of Electric Vehicles in Various Traffic and Road Conditions IEEE Transactions on Intelligent Vehicles 2020
- Argonne National Laboratory 2017
- Kim , N. , Duoba , M. , Kim , N. , and Rousseau , A. Validating Volt PHEV Model with Dynamometer Test Data Using Autonomie SAE International Journal of Passenger Cars—Mechanical Systems 6 2 985 992 Apr. 2013
- Jeong , J. , Choi , S. , Kim , N. , Lee , H. , Stutenberg , K. , and Rousseau , A. Model Validation of the Chevrolet Volt 2016 2018
- Han , J. , Karbowski , D. , Kim , N. , and Rousseau , A. Human Driver Modeling Based on Analytical Optimal Solutions: Stopping Behaviors at the Intersections Dynamic Systems and Control Conference 59162 V003T18A010 American Society of Mechanical Engineers 2019