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We analyzed and improved a collision avoidance strategy, which was supported by Long Term Evolution-Vehicle (LTE-V)-based Vehicle-to-Vehicle (V2V) communication, for automated vehicles. This work was completed in two steps. In the first step, we analyzed the probability distribution of message transmission time, which was conditional on transmission distance and vehicle density. Our analysis revealed that transmission time exhibited a near-linear increase with distance and density. We also quantified the trade-off between high/low resource reselection probabilities to improve the setting of media access parameters. In the second step, we studied the required safety distance in accordance with the response time, i.e., the transmission time, derived on the basis of a novel concept of Responsibility-Sensitive Safety (RSS). We improved the strategy by considering the uncertainty of response time and its dependence on vehicle distance and density. We performed theoretical analysis and numerical testing to illustrate the effectiveness of the improved robust RSS strategy. Our results enhance the practicability of building driverless highways with special lanes reserved for the exclusive use of LTE-V vehicles.


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Collision Avoidance Strategy Supported by LTE-V-Based Vehicle Automation and Communication Systems for Car Following

Show Author's information Jiayang LiYi Zhang( )Mengkai ShiQi LiuYi Chen
Department of Mathematics, Tsinghua University, Beijing 100084, China.
Beijing National Research Center for Information Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing 100084, China
Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, China
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China.
Beijing Nebula Link Tech. Co. Ltd, Beijing 100080, China.
Network Technology Research Institute, China Unicom, Beijing 100044, China.

Abstract

We analyzed and improved a collision avoidance strategy, which was supported by Long Term Evolution-Vehicle (LTE-V)-based Vehicle-to-Vehicle (V2V) communication, for automated vehicles. This work was completed in two steps. In the first step, we analyzed the probability distribution of message transmission time, which was conditional on transmission distance and vehicle density. Our analysis revealed that transmission time exhibited a near-linear increase with distance and density. We also quantified the trade-off between high/low resource reselection probabilities to improve the setting of media access parameters. In the second step, we studied the required safety distance in accordance with the response time, i.e., the transmission time, derived on the basis of a novel concept of Responsibility-Sensitive Safety (RSS). We improved the strategy by considering the uncertainty of response time and its dependence on vehicle distance and density. We performed theoretical analysis and numerical testing to illustrate the effectiveness of the improved robust RSS strategy. Our results enhance the practicability of building driverless highways with special lanes reserved for the exclusive use of LTE-V vehicles.

Keywords: vehicle automation and communication, collision avoidance, Long Term Evolution-Vehicle (LTE-V), Responsibility-Sensitive Safety (RSS)

References(20)

[1]
C. Diakaki, M. Papageorgiou, I. Papamichail, and I. Nikolos, Overview and analysis of vehicle automation and communication systems from a motorway traffic management perspective, Transportation Research Part A: Policy and Practice, vol. 75, pp. 147-165, 2015.
[2]
L. Li and F.-Y. Wang, Advanced Motion Control and Sensing for Intelligent Vehicles. New York, NY, USA: Springer, 2007.
[3]
S. Chen, J. Hu, Y. Shi, and L. Zhao, LTE-V: A TD-LTE-based V2X solution for future vehicular network. IEEE Internet of Things Journal, vol. 3, no. 6, pp. 997-1005, 2016.
[4]
S. Chen, J. Hu, Y. Shi, Y. Peng, J. Fang, R. Zhao, and L. Zhao, Vehicle-to-everything (V2X) services supported by LTE-based systems and 5G, IEEE Communications Standards Magazine, vol. 1, no. 2, pp. 70-76, 2017.
[5]
3GPP, Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); overall description; stage 2 (v14.3.0, Release 14), 3GPP Technical Report 36.300, 2017.
[6]
3GPP, Evolved Universal Terrestrial Radio Access (E-UTRA); physical layer procedures (v14.3.0, Release 14), 3GPP Technical Report 36.213, 2017.
[7]
3GPP, Evolved Universal Terrestrial Radio Access (E-UTRA); physical channels and modulation (v14.3.0, Release 14), 3GPP Technical Report 36.211, 2017.
[8]
3GPP, Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception (v14.4.0, Release 14), 3GPP Technical Report 36.101, 2017.
[9]
3GPP, Evolved Universal Terrestrial Radio Access (E-UTRA); Medium Access Control (MAC) protocol specification (v14.3.0, Release 14), 3GPP Technical Report 36.321, 2017.
[10]
3GPP, Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); protocol specification (v14.3.0, Release 14), 3GPP Technical Report 36.331, 2017.
[11]
3GPP, Architecture enhancements for V2X services (v14.3.0, Re- lease 14), 3GPP Technical Report 23.285, 2017.
[12]
R. Molina-Masegosa and J. Gozalvez, LTE-V for sidelink 5G V2X vehicular communications: A new 5G technology for short-range vehicle-to-everything communications, IEEE Vehicular Technology Magazine, vol. 12, no. 4, pp. 30-39, 2017.
[13]
X. Yang, J. Liu, F. Zhao, and N. H. Vaidya, A vehicle-to-vehicle communication protocol for cooperative collision warning, in Proc. International Conference on Mobile & Ubiquitous Systems: Networking & Services, Boston, MA, USA, 2004, pp. 22-25.
[14]
S. Shalevshwartz, S. Shammah, and A. Shashua, On a formal model of safe and scalable self-driving cars, arXiv preprint arXiv: 1708.06374v5, 2018.
[15]
N. Roohi, R. Kaur, J. Weimer, O. Sokolsky, and I. Lee, Self-driving vehicle verification towards a benchmark, arXiv preprint arXiv:1806.08810v1, 2018.
[16]
M. Naumann, M. Lauer, and C. Stiller, Generating comfortable, safe and comprehensible trajectories for automated vehicles in mixed traffic, arXiv preprint arXiv: 1805.05374v1, 2018.
[17]
R. Mariani, An overview of autonomous vehicles safety, in Proc. 2018 IEEE International Reliability Physics Symposium (IRPS), Burlingame, CA, USA, 2018, .
DOI
[18]
W. van Winsum and A. Heino, Choice of time-headway in car-following and the role of time-to-collision information in braking, Ergonomics, vol. 39, no. 4, pp. 579-592, 1996.
[19]
X. Chen, L. Li, and Y. Zhang, A Markov model for headway/spacing distribution of road traffic, IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 4, pp. 773-785, 2010.
[20]
L. Li, X. Peng, F. Wang, D. Cao, and L. Li, A situation-aware collision avoidance strategy for car-following, IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 5, pp. 1012-1016, 2018.
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Publication history

Published: 22 July 2019
Issue date: February 2020

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© The author(s) 2020

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61673233), Beijing Municipal Science and Technology Program (No. D171100004917001/2), and the Key Technologies Research and Development Program of the Thirteenth Five-Year Plan of China (No. 2018YFB1600600).

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