Cloud-based predictive adaptive cruise control considering preceding vehicle and slope information

doi:10.23919/JSEE.2024.000108

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (6): 1542-1562.doi: 10.23919/JSEE.2024.000108

• CONTROL THEORY AND APPLICATION • Previous Articles

Cloud-based predictive adaptive cruise control considering preceding vehicle and slope information

Bolin GAO¹^,²(), Luyao WANG³(), Shuyan LI³(), Keke WAN³(), Xuepeng WANG⁴^,⁵(), Jin ZHANG⁴^,⁵(), Chen WANG⁵(), Yanbin LIU¹(), Wei ZHONG²^,*()

¹ School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
² State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
³ College of Engineering, China Agricultural University, Beijing 100083, China
⁴ College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an 710049, China
⁵ Weichai Intelligent Technology Co., Ltd, Weifang 261041, China

Received:2023-10-31 Online:2024-12-18 Published:2025-01-14
Contact: Wei ZHONG E-mail:gaobolin@tsinghua.edu.cn;wangluyao13@cau.edu.cn;lishuyan@cau.edu.cn;wankeke@cau.edu.cn;wangxuepeng@sinotruk.com;zhangjin@shig.com.cn;wangchen@tongxin.cn;lyb20@mails.tsinghua.edu.cn;zhongwei@mail.tsinghua.edu.cn
About author:
GAO Bolin was born in 1986. He received his B.S. and M.S. degrees in vehicle engineering from Jilin University, China in 2007 and 2009, respectively, and Ph.D. degree in vehicle engineering from Tongji University, China in 2013. He is now an associate research professor with the School of Vehicle and Mobility, Tsinghua University, China. His research interests include collaborative perception and tracking method in cloud control system, intelligent predictive cruise control system on commercial trucks with cloud control mode, and the test and evaluation of intelligent vehicle driving system. E-mail: gaobolin@tsinghua.edu.cn

WANG Luyao was born in 2000. She received her B.S. degree in vehicle engineering from Hubei University of Automotive Technology, Shiyan, China, in 2022. She is currently a master’s candidate at the College of Engineering, China Agricultural University, Beijing, China. Her research interests include predictive adaptive cruise control and ecological cruise control. E-mail: wangluyao13@cau.edu.cn

LI Shuyan was born in 1972. She received her B.S. and M.S. degrees in vehicle engineering from Jilin University, China in 1995 and 1998. She is an associate professor at the College of Engineering, China Agricultural University. Her research interests are intelligent connected vehicle, predictive cruise control, vehicle power saving and new energy technology, and intelligent testing of agricultural machinery and equipment. E-mail: lishuyan@cau.edu.cn

WAN Keke was born in 1998. He received his B.S. degree in vehicle engineering from Henan University of Engineering, Zhengzhou, China, in 2020 and M.S. degree in vehicle engineering from China Agricultural University, Beijing, China, in 2023. He is currently a joint Ph.D. candidate with the Intelligent and Connected Vehicle of Tsinghua Group and School of Vehicle and Mobility, Tsinghua University. His research interests include cloud-based predictive cruise control, vehicle-road-cloud collaborative control, and cloud control architecture. E-mail: wankeke@cau.edu.cn

WANG Xuepeng was born in 1984. He received his M.S. degree in vehicle engineering from Wuhan University of Technology, China. He is a deputy senior engineering at Weichai Power Co., Ltd. His research interests include intelligent driving path planning to enable vehicles to make flexible decisions based on real-time environmental data, ensuring smooth and safe driving under various road conditions. E-mail: wangxuepeng@sinotruk.com

ZHANG Jin was born in 1982. He received his M.S. degree in materials engineering from Shandong University, China. He is an engineer at Weichai Power Co., Ltd. His research interests are intelligent driving simulation test, time and cost reduction of testing autonomous driving systems and design simulation platform for algorithms. E-mail: zhangjin@shig.com.cn

WANG Chen was born in 1984. He received his B.S. degree from Tsinghua University and M.S. degree from the London School of Economics and Political Science. He is the Director of new business development and assistant economist at Weichai Power Co., Ltd. His research interests include autonomous driving closed-course testing, test results analysis, and validation and correction of algorithms. E-mail: wangchen@tongxin.cn

LIU Yanbin was born in 1986. He received his M.S. degree in software engineering from Zhejiang University, China in 2010. He is pursuing a doctoral degree in innovation leading engineering, School of Vehicle and Transportation, Tsinghua University. His main research interests are the theoretical research and engineering application of data and artificial intelligence driven cloud control systems. E-mail: lyb20@mails.tsinghua.edu.cn

ZHONG Wei was born in 1988. She received her B.S. and Ph.D. degrees from Tsinghua University in 2010 and 2016, respectively. In 2013, she went to the University of Michigan (Ann Arbor) for a research visit at the Department of Mechanical Engineering. From 2019 to 2021, she conducted postdoctoral research at Tsinghua University. She is currently an assistant researcher at the School of Vehicle and Mobility at Tsinghua University. Her research interests mainly focus on intelligent connected vehicle, including system architecture, multi-objective optimization and cloud control platforms. E-mail: zhongwei@mail.tsinghua.edu.cn
Supported by:
This work was supported by the National Key R&D Program of China (2021YFB2501000), the Consultancy Research Project on the Strategic Study of the Integration and Innovative Development of Intelligent Connected Vehicles and New Energy Ecology in Zhejiang Province (2023ZL0007), the Hetao Shenzhen-HongKong Science and Technology Innovation Cooperation Zone (HZQB-KCZYZ-2021055), and the Open Project of the Key Laboratory of Modern Measurement and Control Technology of the Ministry of Education (KF20221123202).

Abstract

Abstract:

With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess, the cloud control system (CCS) has exhibited formidable potential in the realm of connected assisted driving, such as the adaptive cruise control (ACC). Based on the CCS architecture, this paper proposes a cloud-based predictive ACC (PACC) strategy, which fully considers the road slope information and the preceding vehicle status. In the cloud, based on the dynamic programming (DP), the long-term economic speed planning is carried out by using the slope information. At the vehicle side, the real-time fusion planning of the economic speed and the preceding vehicle state is realized based on the model predictive control (MPC), taking into account the safety and economy of driving. In order to ensure the safety and stability of the vehicle-cloud cooperative control system, an event-triggered cruise mode switching method is proposed based on the state of each subsystem of the vehicle-cloud-network-map. Simulation results indicate that the PACC system can still ensure stable cruising under delays and some complex conditions. Moreover, under normal conditions, compared to the ACC system, the PACC system can further improve economy while ensuring safety and improve the overall energy efficiency of the vehicle, thus achieving fuel savings of 3% to 8%.

Key words: predictive adaptive cruise control (PACC), cloud control system (CCS), economic driving

Bolin GAO, Luyao WANG, Shuyan LI, Keke WAN, Xuepeng WANG, Jin ZHANG, Chen WANG, Yanbin LIU, Wei ZHONG. Cloud-based predictive adaptive cruise control considering preceding vehicle and slope information[J]. Journal of Systems Engineering and Electronics, 2024, 35(6): 1542-1562.

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References 45

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Condition series	Value of relevant signal
Condition 1	Cruise switch=1
Condition 2	Constant switch=0, or brake/clutch=1
Condition 3	Network status=1, and map information=1, and relative distance>$ {d}_{\mathrm{m}\mathrm{a}\mathrm{x}} $or $ {v}_{{\mathrm{p}}} > {v}_{{\mathrm{cc}}} $
Condition 4	Network status=0, or map information=0
Condition 5	Network status=1, and map information=1, and relative distance<$ {d}_{\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}} $, and $ {v}_{{\mathrm{h}}} > {v}_{{\mathrm{p}}} $
Condition 6	Same as condition 3
Condition 7	Same as condition 4
Condition 8	Same as condition 5

Parameter	Value
Vehicle curb mass $ m $/kg	37000
Effective tire radius $ r $/m	0.51
Maximum engine torque $ {T}_{\mathrm{m}\mathrm{a}\mathrm{x}} $/($ \mathrm{N}\cdot \mathrm{m} $)	2100
Transmission ratio $ {i}_{g} $	1
Final drive ratio $ {i}_{0} $	3.7
Transmission system efficiency $ {\eta }_{t} $	0.99
Rolling resistance coefficient $ f $	0.006
Air drag coefficient $ {C}_{D} $	0.69
Gravitational acceleration $ g $/($ \mathrm{m}/{\mathrm{s}}^{2} $)	9.81
Frontal area $ A $/$ {\mathrm{m}}^{2} $	9.8

Parameter	Value
Constant term $ {\xi }_{\mathrm{0,0}} $	0.001766
First order term of rotary speed $ {\xi }_{\mathrm{1,0}} $	−2.387e−06
First order term of torque $ {\xi }_{\mathrm{0,1}} $	−1.222e−06
Second order term of torque $ {\xi }_{\mathrm{0,2}} $	1.473e−09
First order term of mixed term $ {\xi }_{\mathrm{1,1}} $	5.05e−09
Second order term of rotary speed $ {\xi }_{\mathrm{2,0}} $	6.49e−10

Parameter	Value
Predictive horizon Y/m	2000
Iteration distance X/m	200
Discrete interval $ \Delta v $/(m/s)	0.2
Reference velocity $ {v}_{cc} $/(km/h)	75
Engine speed $ {n}_{\mathrm{m}\mathrm{i}\mathrm{n}} $/rpm	1000
Engine speed $ {n}_{\mathrm{m}\mathrm{a}\mathrm{x}} $/rpm	1800
Gear position $ g $	15
Maximum acceleration $ {a}_{\mathrm{m}\mathrm{a}\mathrm{x}} $/(m/s²)	0.3
Minimum acceleration $ {a}_{\mathrm{m}\mathrm{i}\mathrm{n}} $/(m/s²)	−0.3
Control step size $ m $	10
Prediction step size $ p $	10
Sample time $ {T}_{s} $/s	0.2
Time constant $ \tau $	0.45
Time headway $ {t}_{h} $/s	2
Space constant term $ {d}_{0} $/m	37
Weight matrix $ \boldsymbol{Q} $	$ {\mathrm{diag}}(\mathrm{0.5,0.1,40,1},10) $
Weight coefficient $ R $	1
Attenuation coefficient $ \rho $	0.97
Time constant $ {\alpha }_{\delta } $	6.67

Reference speed/(km/h)	Cruise mode	Average speed/(km/h)	Distance/km	Fuel consumption/L	Fuel consumption per kilometer/(L/km)	Fuel-saving rate/%
65	PACC	62.40	8.736	2.018	23.10×10⁻²	6.49
65	ACC	65.19	9.126	2.254	24.70×10⁻²	6.49
70	PACC	67.98	9.52	2.415	25.38×10⁻²	7.35
70	ACC	70.19	9.827	2.692	27.39×10⁻²	7.35
75	PACC	70.83	9.916	2.549	25.71×10⁻²	6.06
75	ACC	70.88	9.924	2.716	27.37×10⁻²	6.06
80	PACC	70.84	9.917	2.615	26.37×10⁻²	4.16
80	ACC	70.88	9.924	2.733	27.54×10⁻²	4.16

Cloud-based predictive adaptive cruise control considering preceding vehicle and slope information

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