A safe and efficient decision algorithm for ramp merging based on a hierarchical planner and model predictive control

Abstract

Due to highly dynamic interactions between vehicles, ramp merging decision is extraordinarily challenging in dense traffic. To deal with the non-convexity and inter-coupling of the decision-making problem, a top-level behavioural planner is proposed, which considers both the overall traffic situation and the individual characteristics of other interacting drivers. To ensure the implementability and efficiency of planning, a bottom-level motion planner is further designed with the guide of the top-level behavioural planner. The validation simulation using a naturalistic driving dataset shows that the proposed planning algorithm can achieve a success rate of 97.65% and has similar gap selection decision as human drivers. Then to track the planned vehicle motion, a nonlinear model predictive controller considering actuator delay and lag characteristics is proposed. Finally, the proposed planning and control modules are deployed in a turbocharged test vehicle, with satisfactory vehicle lateral and speed tracking errors, which validate the implementability of the proposed ramp merging decision algorithms.

Keywords

level-k game theory behavioural planner motion planner model predictive control ramp merging

Get full access to this article

View all access options for this article.

References

National Highway Traffic Safety Administration. Traffic safety facts 2015. Technical report, National Highway Traffic Safety Administration, Washington, DC, 2015. https://crashstats.nhtsa.dot.gov/Api/Public/Publication/812384 (accessed 02 March 2022).

Gipps

PG.

A model for the structure of lane-changing decisions. Transportation Res B 1986; 20(5): 403–414.

Ahmed

. Modeling drivers’ acceleration and lane changing behavior. Thesis, Massachusetts Institute of Technology, Cambridge, MA, 1999. https://dspace.mit.edu/handle/1721.1/9662 (accessed 16 August 2022).

Trautman

Krause

. Unfreezing the robot: navigation in dense, interacting crowds. In: 2010 IEEE/RSJ International conference on intelligent robots and systems, pp.797–803. New York: IEEE. DOI:10.1109/IROS.2010.5654369

Wang

Chan

de La Fortelle

. A reinforcement learning based approach for automated lane change maneuvers. In: 2018 IEEE intelligent vehicles symposium (IV), pp.1379–1384. New York: IEEE. DOI:10.1109/IVS.2018.8500556.

Wang

Chan

. Continuous control for automated lane change behavior based on deep deterministic policy gradient algorithm. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp.1454–1460. New York: IEEE. DOI:10.1109/IVS.2019.8813903.

Bouton

Nakhaei

Fujimura

, et al. Cooperation-aware reinforcement learning for merging in dense traffic. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp.3441–3447. New York: IEEE. DOI:10.1109/ITSC.2019.8916924.

Brito

Agarwal

Alonso-Mora

Learning interaction-aware guidance policies for motion planning in dense traffic scenarios. arXiv preprint, arXiv:210704538 [cs] 2021.

Levinson

A review of game theory models of lane changing. Transportmetrica 2020; 16(3): 1628–1647.

10.

Liu

Xin

Adam

, et al. A game theoretical approach for modelling merging and yielding behaviour at freeway on-ramp sections. In: Allsop

Richard E.

Bell

Michael G. H.

Heydecker

Benjamin G.

(eds) Transportation and Traffic Theory. Amsterdam: Elsevier, 2007, pp.197–211, https://www.researchgate.net/publication/231093972 (accessed 23 July 2021).

11.

Bansal

Cosgun

Nakhaei

, et al. Collaborative planning for mixed-autonomy lane merging. In: 2018 IEEE/RSJ International conference on intelligent robots and systems (IROS), pp.4449–4455. New York: IEEE. DOI: 10.1109/IROS.2018.8594197

12.

Garzón

Spalanzani

. Game theoretic decision making based on real sensor data for autonomous vehicles’ maneuvers in high traffic. In: 2020 IEEE international conference on robotics and automation (ICRA), pp.5378–5384. New York: IEEE. DOI: 10.1109/ICRA40945.2020.9197430

13.

Evens

Schuurmans

Patrinos

, et al. Learning MPC for interaction-aware autonomous driving: a game-theoretic approach. In: 2022 European Control Conference (ECC), pp.34–39. New York: IEEE. DOI: 10.23919/ECC55457.2022.9838517

14.

Cleac’h

Schwager

Manchester

ALGAMES: a fast solver for constrained dynamic games. In: Robotics: Science and systems, 2020. DOI: 10.15607/RSS.2020.XVI.091

15.

Cleac’h

Schwager

Manchester

ALGAMES: a fast augmented Lagrangian solver for constrained dynamic games. Auton Robots 2022; 46(1): 201–215.

16.

Liu

. Interaction strategy for automated driving considering the risk of multi-vehicle conflict. Master’s Thesis, Zhejiang University, Hangzhou, China, 2022. DOI: 10.27461/d.cnki.gzjdx.2022.000216

17.

Schwarting

Pierson

Alonso Mora

, et al. Social behavior for autonomous vehicles. Proc Natl Acad Sciences United States America 2019; 116(50): 24972–24978.

18.

Oyler

Zhang

, et al. Hierarchical reasoning game theory based approach for evaluation and testing of autonomous vehicle control systems. In: 2016 IEEE 55th Conference on Decision and Control (CDC), pp.727–733. New York: IEEE. DOI: 10.1109/CDC.2016.7798354

19.

Tian

, et al. Adaptive game-theoretic decision making for autonomous vehicle control at roundabouts. In: 2018 IEEE conference on decision and control (CDC), pp.321–326. New York: IEEE. DOI: 10.1109/CDC.2018.8619275

20.

Wang

Zhang

Peng

Comprehensive safety evaluation of highly automated vehicles at the roundabout scenario. IEEE Trans Intell Transp Syst 2022; 23(11): 20873–20888.

21.

Pan

Two-lane two-way overtaking decision model with driving style awareness based on a game-theoretic framework. Transportmetrica. Epub ahead of print 25 May 2022. DOI: 10.1080/23249935.2022.2076755

22.

Wang

Pan

Xiao

, et al. Social-aware decision algorithm for on-ramp merging based on level-k gaming. In: 2022 IEEE 18th international conference on automation scienceand engineering (CASE), pp.1753–1758. New York: IEEE. DOI: 10.1109/CASE49997.2022.9926461

23.

Ming

Deng

Zhang

, et al. MPC-based trajectory tracking control for intelligent vehicles. SAE technical paper 2016-01-0452, SAE International, Warrendale, PA, 2016. DOI: 10.4271/2016-01-0452

24.

Morato

Normey-Rico

Sename

Model predictive control design for linear parameter varying systems: a survey. Annu Rev Control 2020; 49: 64–80.

25.

Abbas

Milman

Eklund

JM.

Obstacle avoidance in real time with nonlinear model predictive control of autonomous vehicles. Can J Eng Comput Eng 2017; 40(1): 12–22.

26.

Peng

Tang

Preview path tracking control with delay compensation for autonomous vehicles. IEEE Trans Intell Transp Syst 2021; 22(5): 2979–2989.

27.

US Department of Transportation Federal Highway Administration. Next generation simulation (NGSIM) vehicle trajectories and supporting data, 2016. DOI: 10.21949/1504477

28.

Julier

Uhlmann

Durrant-Whyte

. A new approach for filtering nonlinear systems. In: Proceedings of 1995 American Control Conference - ACC’95, pp.1628–1632. New York: IEEE. DOI: 10.1109/ACC.1995.529783.

29.

Balas

MM.

Driver assisting by inverse time to collision. In: 2006 World automation congress, pp. 1–6. New York: IEEE. DOI:10.1109/WAC.2006.376059.

30.

Minderhoud

Bovy

PHL

. Extended time-to-collision measures for road traffic safety assessment. Accid Anal Prev 2001; 33(1): 89–97.

31.

Lin

Liu

Lane departure prediction based on closed-loop vehicle dynamics. Proc IMechE, Part J 2022; 236(5): 913–926.

32.

Wächter

Biegler

LT.

On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math Program 2006; 106(1): 25–57.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB