Task Assignment Strategy for Platoons in Cooperative Driving

Changle LI; Yunfeng ZHANG; Yao ZHANG; Guoqiang MAO; Cunxing JIA

doi:10.11999/JEIT190557

Volume 42 Issue 1

Jan. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(1): 65-73

Changle LI, Yunfeng ZHANG, Yao ZHANG, Guoqiang MAO, Cunxing JIA. Task Assignment Strategy for Platoons in Cooperative Driving[J]. Journal of Electronics & Information Technology, 2020, 42(1): 65-73. doi: 10.11999/JEIT190557

Citation:

Changle LI, Yunfeng ZHANG, Yao ZHANG, Guoqiang MAO, Cunxing JIA. Task Assignment Strategy for Platoons in Cooperative Driving[J]. Journal of Electronics & Information Technology, 2020, 42(1): 65-73. doi: 10.11999/JEIT190557

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Task Assignment Strategy for Platoons in Cooperative Driving

doi: 10.11999/JEIT190557

1.
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
2.
School of Electrical and Data Engineering, University of Technology Sydney, Sydney 2007, Australia
3.
Hebei Expressway Preparation and Construction Office for Beijing-Xiongan Expressway, Xiongan 071799, China

Funds: The National Natural Science Foundation of China (U1801266), The Key Research and Development Program of Shaanxi (2018ZDXM-GY-038, 2018ZDCXL-GY-04-02), The Youth Innovation Team of Shaanxi Universities, The Science and Technology Projects of Xi’an (201809170CX11JC12)

Received Date: 2019-07-25
Rev Recd Date: 2019-11-28

Available Online: 2019-11-29

Publish Date: 2020-01-21

Abstract

Abstract

Autonomous vehicles are equipped with multiple on-board sensors to achieve self-driving functions. However, a tremendous amount of data is generated by autonomous vehicles, which significantly challenges the real-time task processing. Through multiple-vehicle cooperation, which makes the best of vehicle onboard computing resources, autonomous and cooperative driving becomes a promising candidate to solve the aforementioned problem. In this case, it is vital for autonomous and cooperative driving to form a driving platoon and allocate driving tasks efficiently. In this paper, a more general analytical model is developed based on G/G/1 queueing theory to model the topology of platoons. Next, Support Vector Machine (SVM) method is adopted to classify the “idle” and “busy” categories of the vehicles in the platoon based on their computing load and task processing capacity. Finally, based on the analysis above, an efficient task balancing strategy of platoons in autonomous and cooperative driving called Classification based Greed Balancing Strategy (C-GBS) is proposed, in order to balance the task burden among vehicles and cooperate more efficiently. Extensive simulations demonstrate that the proposed technique can reduce the processing delay of driving tasks in platoons with high computing load, which will improve the processing efficiency in autonomous vehicles.
- Autonomous and cooperative driving,
- Platoon,
- Task assignment,
- Queuing theory,
- Support Vector Machine (SVM)

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References

CESARI G, SCHILDBACH G, CARVALHO A, et al. Scenario model predictive control for lane change assistance and autonomous driving on highways[J]. IEEE Intelligent Transportation Systems Magazine, 2017, 9(3): 23–35. doi: 10.1109/MITS.2017.2709782

CHANG L and DORMEHL L. 6 self-driving car crashes that tapped the brakes on the autonomous revolution[EB/OL]. https://www.digitaltrends.com/cool-tech/most-significant-self-driving-car-crashes/, 2018.

SU Zhou, HUI Yilong, XU Qichao, et al. An edge caching scheme to distribute content in vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2018, 67(6): 5346–5356. doi: 10.1109/TVT.2018.2824345

AISSIOUI A, KSENTINI A, GUEROUI A M, et al. On enabling 5G automotive systems using follow me edge-cloud concept[J]. IEEE Transactions on Vehicular Technology, 2018, 67(6): 5302–5316. doi: 10.1109/TVT.2018.2805369

TRAN T X, HAJISAMI A, PANDEY P, et al. Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges[J]. IEEE Communications Magazine, 2017, 55(4): 54–61. doi: 10.1109/MCOM.2017.1600863

LUAN T H, CAI L X, CHEN Jiming, et al. Engineering a distributed infrastructure for large-scale cost-effective content dissemination over urban vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2014, 63(3): 1419–1435. doi: 10.1109/TVT.2013.2251924

SU Zhou, HUI Yilong, and GUO Song. D2D-based content delivery with parked vehicles in vehicular social networks[J]. IEEE Wireless Communications, 2016, 23(4): 90–95. doi: 10.1109/MWC.2016.7553031

LI S E, GAO Feng, LI Keqiang, et al. Robust longitudinal control of multi-vehicle systems-a distributed h-infinity method[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(9): 2779–2788. doi: 10.1109/TITS.2017.2760910

JAIN A, GHOSE D, and MENON P P. Multi-vehicle formation in a controllable force field with non-identical controller gains[J]. IET Control Theory & Applications, 2018, 12(6): 802–811.

LI Xin and ZHU Daqi. An adaptive SOM neural network method for distributed formation control of a group of AUVs[J]. IEEE Transactions on Industrial Electronics, 2018, 65(10): 8260–8270.

LIU Yuanchang and BUCKNALL R. A survey of formation control and motion planning of multiple unmanned vehicles[J]. Robotica, 2018, 36(7): 1019–1047. doi: 10.1017/S0263574718000218

ZHU Daqi, CAO Xiang, SUN Bing, et al. Biologically inspired self-organizing map applied to task assignment and path planning of an AUV system[J]. IEEE Transactions on Cognitive and Developmental Systems, 2018, 10(2): 304–313. doi: 10.1109/TCDS.2017.2727678

GODFREY G A and POWELL W B. An adaptive dynamic programming algorithm for dynamic fleet management, II: Multiperiod travel times[J]. Transportation Science, 2002, 36(1): 40–54. doi: 10.1287/trsc.36.1.40.572

VIEGAS D, BATISTA P, OLIVEIRA P, et al. Discrete-time distributed Kalman filter design for formations of autonomous vehicles[J]. Control Engineering Practice, 2018, 75: 55–68. doi: 10.1016/j.conengprac.2018.03.014

The 5G Infrastructure Public Private Partnership (5G PPP). 5G PPP white paper on automotive vertical sectors[EB/OL]. https://5g-ppp.eu/wp-content/uploads/2014/02/5G-PPP-White-Paper-on-Automotive-Vertical-Sectors.pdf, 2015.

PENG Haixia, LI Dazhou, ABBOUD K, et al. Performance analysis of IEEE 802.11p DCF for multiplatooning communications with autonomous vehicles[J]. IEEE Transactions on Vehicular Technology, 2017, 66(3): 2485–2498. doi: 10.1109/TVT.2016.2571696

3GPP. Study on LTE-based V2X services[EB/OL]. https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=2934.

PIRO G, ORSINO A, CAMPOLO C, et al. D2D in LTE vehicular networking: System model and upper bound performance[C]. The 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, Brno, Czech Republic, 2015: 281–286.

WHITT W. The queueing network analyzer[J]. The Bell System Technical Journal, 1983, 62(9): 2779–2815. doi: 10.1002/j.1538-7305.1983.tb03204.x

DAVIS J L, MASSEY W A, and WHITT W. Sensitivity to the service-time distribution in the nonstationary erlang loss model[J]. Management Science, 1995, 41(6): 1107–1116. doi: 10.1287/mnsc.41.6.1107

LI Changle, ZHANG Yao, LUAN T H, et al. Building transmission backbone for highway vehicular networks: Framework and analysis[J]. IEEE Transactions on Vehicular Technology, 2018, 67(9): 8709–8722. doi: 10.1109/TVT.2018.2844471

WHITT W. Approximating a point process by a renewal process, I: Two basic methods[J]. Operations Research, 1982, 30(1): 125–147. doi: 10.1287/opre.30.1.125

RAY W D. Basic queueing theory[J]. Journal of the Royal Statistical Society: Series A, 1988, 151(3): 550–684.

AVIDAN S. Support vector tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(8): 1064–1072. doi: 10.1109/TPAMI.2004.53

MOLINA-MASEGOSA R and GOZALVEZ J. LTE-V for sidelink 5G V2X vehicular communications: A new 5G technology for short-range vehicle-to-everything communications[J]. IEEE Vehicular Technology Magazine, 2017, 12(4): 30–39. doi: 10.1109/MVT.2017.2752798

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