under dynamic environment. Most of the current research in MARL is still concentrated in static and closed environments, but our work is carried out in an open and
dynamic environment, which provides a prospective direction for future research. Our
approach is validated by lane change problems, which have better security and scalability than other approaches.
Acknowledgements. This work was supported in part by the National Science Foundation of
China under Grant No. 61471084, National Key R&D Program of China under Grant
No. 2018YFB1700102 and Fundamental Research Funds for the Central Universities
No. DUT19JCC29.
References
1. Cao P, Hu Y, Miwa T et al (2017) An optimal mandatory lane change decision model for
autonomous vehicles in urban arterials. J Intell Transp Syst 21(4):271–284
2. Fagnant DJ, Kockelman K (2015) Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp Res Part A Policy Pract 77:167–181
3. Guestrin C, Lagoudakis M, Parr R (2002) Coordinated reinforcement learning. ICML, vol 2,
pp 227–234
4. Hult R, Zanon M, Gros S et al (2018) Optimal coordination of automated vehicles at
intersections: theory and experiments. IEEE Trans Control Syst Technol
5. Isele D, Cosgun A, Subramanian K et al (2017) Navigating intersections with autonomous
vehicles using deep reinforcement learning, May 2017. http://arxiv.org/abs/1705.01196
6. Kalantari R, Motro M, Ghosh J et al (2016) A distributed, collective intelligence framework
for collision-free navigation through busy intersections. In: 2016 IEEE 19th international
conference on intelligent transportation systems (ITSC). IEEE, pp 1378–1383
7. Kesting A, Treiber M, Helbing D (2007) General lane-changing model MOBIL for carfollowing models. Transp Res Rec 1999(1):86–94
8. Kok JR, Vlassis N (2006) Collaborative multiagent reinforcement learning by payoff
propagation. J Mach Learn Res 7:1789–1828
9. Li X, Sun JQ (2017) Studies of vehicle lane-changing dynamics and its effect on traffic
efficiency, safety and environmental impact. Physica A 467:41–58
10. Li X, Xu X, Zuo L (2015) Reinforcement learning based overtaking decision-making for
highway autonomous driving. In: 2015 sixth international conference on intelligent control
and information processing (ICICIP). IEEE, pp 336–342
11. Moriarty DE, Langley P (1998) Learning cooperative lane selection strategies for highways.
AAAI/IAAI 1998:684–691
12. Olfati-Saber R, Murray RM (2002) Graph rigidity and distributed formation stabilization of
multi-vehicle systems
13. Plessen MG, Bernardini D, Esen H et al (2016) Multi-automated vehicle coordination using
decoupled prioritized path planning for multi-lane one-and bi-directional traffic flow control.
In: 2016 IEEE 55th conference on decision and control (CDC). IEEE, pp 1582–1588
14. Pan X, You Y, Wang Z et al (2017) Virtual to real reinforcement learning for autonomous
driving. arXiv preprint arXiv:1704.03952
15. Sallab AEL, Abdou M, Perot E et al (2017) Deep reinforcement learning framework for
autonomous driving. Electron Imaging 2017(19):70–76
606
H. Zuo et al.
dynamic environment, which provides a prospective direction for future research. Our
approach is validated by lane change problems, which have better security and scalability than other approaches.
Acknowledgements. This work was supported in part by the National Science Foundation of
China under Grant No. 61471084, National Key R&D Program of China under Grant
No. 2018YFB1700102 and Fundamental Research Funds for the Central Universities
No. DUT19JCC29.
References
1. Cao P, Hu Y, Miwa T et al (2017) An optimal mandatory lane change decision model for
autonomous vehicles in urban arterials. J Intell Transp Syst 21(4):271–284
2. Fagnant DJ, Kockelman K (2015) Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp Res Part A Policy Pract 77:167–181
3. Guestrin C, Lagoudakis M, Parr R (2002) Coordinated reinforcement learning. ICML, vol 2,
pp 227–234
4. Hult R, Zanon M, Gros S et al (2018) Optimal coordination of automated vehicles at
intersections: theory and experiments. IEEE Trans Control Syst Technol
5. Isele D, Cosgun A, Subramanian K et al (2017) Navigating intersections with autonomous
vehicles using deep reinforcement learning, May 2017. http://arxiv.org/abs/1705.01196
6. Kalantari R, Motro M, Ghosh J et al (2016) A distributed, collective intelligence framework
for collision-free navigation through busy intersections. In: 2016 IEEE 19th international
conference on intelligent transportation systems (ITSC). IEEE, pp 1378–1383
7. Kesting A, Treiber M, Helbing D (2007) General lane-changing model MOBIL for carfollowing models. Transp Res Rec 1999(1):86–94
8. Kok JR, Vlassis N (2006) Collaborative multiagent reinforcement learning by payoff
propagation. J Mach Learn Res 7:1789–1828
9. Li X, Sun JQ (2017) Studies of vehicle lane-changing dynamics and its effect on traffic
efficiency, safety and environmental impact. Physica A 467:41–58
10. Li X, Xu X, Zuo L (2015) Reinforcement learning based overtaking decision-making for
highway autonomous driving. In: 2015 sixth international conference on intelligent control
and information processing (ICICIP). IEEE, pp 336–342
11. Moriarty DE, Langley P (1998) Learning cooperative lane selection strategies for highways.
AAAI/IAAI 1998:684–691
12. Olfati-Saber R, Murray RM (2002) Graph rigidity and distributed formation stabilization of
multi-vehicle systems
13. Plessen MG, Bernardini D, Esen H et al (2016) Multi-automated vehicle coordination using
decoupled prioritized path planning for multi-lane one-and bi-directional traffic flow control.
In: 2016 IEEE 55th conference on decision and control (CDC). IEEE, pp 1582–1588
14. Pan X, You Y, Wang Z et al (2017) Virtual to real reinforcement learning for autonomous
driving. arXiv preprint arXiv:1704.03952
15. Sallab AEL, Abdou M, Perot E et al (2017) Deep reinforcement learning framework for
autonomous driving. Electron Imaging 2017(19):70–76
606
H. Zuo et al.