Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections
To improve the driving efficiency and energy-saving characteristics for large-scale mixed traffic flows under different market penetration rates (MPRs) of intelligent and connected vehicles (ICVs) at unsignalized intersections, considering the cooperative eco-driving performance between ICVs and hum...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/7114792 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559262374559744 |
|---|---|
| author | Jie Yu Yugong Luo Weiwei Kong Fachao Jiang |
| author_facet | Jie Yu Yugong Luo Weiwei Kong Fachao Jiang |
| author_sort | Jie Yu |
| collection | DOAJ |
| description | To improve the driving efficiency and energy-saving characteristics for large-scale mixed traffic flows under different market penetration rates (MPRs) of intelligent and connected vehicles (ICVs) at unsignalized intersections, considering the cooperative eco-driving performance between ICVs and human-driven vehicles (HDVs) with time-varying speed characteristics, the hierarchical and distributed cooperative eco-driving architecture is first established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the multivehicle model-free adaptive predictive cooperative driving (MFAPCD) method is designed by using only the driving data of the HDVs and ICVs formation based on compact form dynamic linearization (CFDL) technique, thereby improving traffic efficiency. Furthermore, the CFDL integral terminal sliding mode predictive control (CFDL-ITSMPC) scheme is utilized to predict the time-varying driving speed of HDVs, and then, the CFDL predictive control (CFDL-PC) scheme is utilized to predict the expected control variables of ICVs formation. For the vehicle control layer, based on the anticipated driving speed obtained from the cloud decision layer, the nonlinear distributed model predictive control (NDMPC) method is utilized for distributed optimal control of each vehicle formation, to achieve optimization in terms of energy saving. Simulation results show that, compared with the signal time assignment strategy, the method can increase the average velocity by about 15.22% and decrease the average fuel consumption by about 36.43% under different MPRs and traffic volumes. |
| format | Article |
| id | doaj-art-1fc7a0ee21144c69985ea090762ccef2 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1fc7a0ee21144c69985ea090762ccef22025-02-03T01:30:24ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/7114792Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized IntersectionsJie Yu0Yugong Luo1Weiwei Kong2Fachao Jiang3College of EngineeringState Key Laboratory of Automotive Safety and EnergyCollege of EngineeringCollege of EngineeringTo improve the driving efficiency and energy-saving characteristics for large-scale mixed traffic flows under different market penetration rates (MPRs) of intelligent and connected vehicles (ICVs) at unsignalized intersections, considering the cooperative eco-driving performance between ICVs and human-driven vehicles (HDVs) with time-varying speed characteristics, the hierarchical and distributed cooperative eco-driving architecture is first established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the multivehicle model-free adaptive predictive cooperative driving (MFAPCD) method is designed by using only the driving data of the HDVs and ICVs formation based on compact form dynamic linearization (CFDL) technique, thereby improving traffic efficiency. Furthermore, the CFDL integral terminal sliding mode predictive control (CFDL-ITSMPC) scheme is utilized to predict the time-varying driving speed of HDVs, and then, the CFDL predictive control (CFDL-PC) scheme is utilized to predict the expected control variables of ICVs formation. For the vehicle control layer, based on the anticipated driving speed obtained from the cloud decision layer, the nonlinear distributed model predictive control (NDMPC) method is utilized for distributed optimal control of each vehicle formation, to achieve optimization in terms of energy saving. Simulation results show that, compared with the signal time assignment strategy, the method can increase the average velocity by about 15.22% and decrease the average fuel consumption by about 36.43% under different MPRs and traffic volumes.http://dx.doi.org/10.1155/2023/7114792 |
| spellingShingle | Jie Yu Yugong Luo Weiwei Kong Fachao Jiang Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections Journal of Advanced Transportation |
| title | Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections |
| title_full | Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections |
| title_fullStr | Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections |
| title_full_unstemmed | Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections |
| title_short | Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections |
| title_sort | hierarchical and distributed eco driving approach for mixed vehicle clusters at unsignalized intersections |
| url | http://dx.doi.org/10.1155/2023/7114792 |
| work_keys_str_mv | AT jieyu hierarchicalanddistributedecodrivingapproachformixedvehicleclustersatunsignalizedintersections AT yugongluo hierarchicalanddistributedecodrivingapproachformixedvehicleclustersatunsignalizedintersections AT weiweikong hierarchicalanddistributedecodrivingapproachformixedvehicleclustersatunsignalizedintersections AT fachaojiang hierarchicalanddistributedecodrivingapproachformixedvehicleclustersatunsignalizedintersections |