Autonomous overtaking control considering the velocity change of obstacle vehicles on two-lane highways

Abstract

Aiming at the speed change of obstacle vehicles in the process of autonomous ground vehicle (AGV) overtaking on two-lane highways, an overtaking control method including trajectory planning and trajectory control is proposed. First, the reference overtaking trajectory of the AGV is planned in stages (lane changing, passing and merging) using fifth order polynomials satisfying stability and comfort constraints. The initial planning trajectory may be unsafe due to the speed change of obstacle vehicles. So a collision prediction algorithm is built to predict two kinds of dangerous situations in real time: AGV collides with the leading vehicle during lane changing, and AGV collides with the oncoming vehicle during merging. Secondly, the potential field of the obstacle vehicles embedded with the relative speed is established and combined with the road potential field as the risk indicator. Then, combining the nonlinear model predictive control algorithm and risk index, the overtaking trajectory is replanned in real time with the initial trajectory as reference. Finally, a lateral path tracking controller based on model predictive control and a longitudinal speed tracking controller based on sliding mode control are established to track the planned trajectory rapidly and stably. To verify the performance of the proposed method, overtaking in three cases is simulated, and vehicle responding curves are obtained. The results show that the proposed control method can achieve safe and stable overtaking.

Keywords

Two-lane highways autonomous overtaking trajectory planning and trajectory replanning tracking control artificial potential field model predictive control sliding mode control

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References

Perumal

Sujasree

Chavhan

, et al. An insight into crash avoidance and overtaking advice systems for autonomous vehicles: A review, challenges and solutions. Eng Appl Artif Intell 2021; 104: 104406.

Zhuang

Yin

, et al. Robust overtaking control of autonomous electric vehicle with parameter uncertainties. Proc IMechE, Part D: J Automobile Engineering 2019; 233: 3358–3376.

Murgovski

Sjöberg

. Predictive cruise control with autonomous overtaking. In: 2015 54th IEEE conference on decision and control, Osaka, Japan, 15–18 December 2015, pp.644–649. New York: IEEE.

Pérez

Milanés

Onieva

, et al. Longitudinal fuzzy control for autonomous overtaking. In: 2011 IEEE international conference on mechatronics, Istanbul, Turkey, 13–15 April 2011, pp.188–193. New York: IEEE.

Karlsson

Murgovski

Sjoberg

. Computationally efficient autonomous overtaking on highways. IEEE Trans Intell Transp Syst 2020; 21: 3169–3183.

Yamada

Bakibillah

ASM

Hashikura

, et al. Autonomous vehicle overtaking: Modeling and an optimal trajectory generation scheme. Sustainability 2022; 14: 1807.

Graf

Speidal

Dietmayer

. Trajectory planning for automated vehicles in overtaking scenarios. In: 2019 IEEE intelligent vehicles symposium. Paris, France, 9–12 June 2019, pp.1653–1659. New York: IEEE.

Németh

Gáspár

Hegedűs

. Optimal control of overtaking maneuver for intelligent vehicles. J Adv Transport 2018; 2018: 2195760.

Lattarulo

Pérez

. A linear model predictive planning approach for overtaking manoeuvres under possible collision circumstances. In: 2018 IEEE intelligent vehicles symposium, Changshu, China, 26–30 June 2018, pp.1340–1345. New York: IEEE.

10.

Lattarulo

Pérez Rastelli

. A hybrid planning approach based on MPC and parametric curves for overtaking maneuvers. Sensors 2021; 21: 595.

11.

Dixit

Montanaro

Dianati

, et al. Trajectory planning for autonomous high-speed overtaking in structured environments using robust MPC. IEEE Trans Intell Transp Syst 2020; 21: 2310–2323.

12.

Palatti

Aksjonov

Alcan

, et al. Planning for safe abortable overtaking maneuvers in autonomous driving. In: 2021 IEEE international intelligent transportation systems conference, Indianapolis, IN, 19–22 September 2021, pp.508–514. New York: IEEE.

13.

Hassein

Diachuk

Easa

. Development of warning system and driving model for overtaking trucks on two-lane highways. Transp Res Rec 2019; 2673: 1–12.

14.

Coskun

. Autonomous overtaking in highways: A receding horizon trajectory generator with embedded safety feature. Eng Sci Technol Int J 2021; 24: 1049–1058.

15.

Shim

Adireddy

Yuan

. Autonomous vehicle collision avoidance system using path planning and model-predictive-control-based active front steering and wheel torque control. Proc IMechE, Part D: J Automobile Engineering 2012; 226: 767–778.

16.

Gao

Lin

Borrelli

, et al. Predictive control of autonomous ground vehicles with obstacle avoidance on slippery roads. In: ASME 2010 dynamic systems and control conference, Cambridge MA, 12–15 September 2010, pp.265–272. New York: ASME.

17.

Yin

Chen

, et al. Research on trajectory planning and tracking strategy of lane-changing and overtaking based on PI-MPC dual controllers. In: Brake colloquium & exhibition 39th annual, Orlando, FL, 17–20 October 2021, pp.2021–01–1262. New York: SAE.

18.

Feng

Qian

Wang

. Collision avoidance method of autonomous vehicle based on improved artificial potential field algorithm. Proc IMechE, Part D: J Automobile Engineering 2021; 235: 3416–3430.

19.

Memering

Meckl

. Comparison of adaptive control techniques applied to diesel engine idle speed regulation. J Dyn Syst Meas Control 2002; 124: 682–688.

20.

Polack

Altché

Novel

, et al. The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles. In 2017 IEEE intelligent vehicles symposium, Los Angeles, CA, 11–14 June 2017, pp.812–818. New York: IEEE.

21.

Zhang

Dong

. Stabilizing vehicle lateral dynamics with considerations of parameter uncertainties and control saturation through robust yaw control. IEEE Trans Vehicular Technol 2010; 59: 2593–2597.

22.

Huang

, et al. An APF and MPC combined collaborative driving controller using vehicular communication technologies. Chaos Solitons Fractals 2016; 89: 232–242.

23.

Huang

Zhang

. A path planning method for vehicle overtaking maneuver using sigmoid functions. IFAC-PapersOnLine 2019; 52: 422–427.

24.

Mocsari

. Analysis of the overtaking behaviour of motor vehicle drivers. Acta Tech Jaurinen 2009; 2: 97–106.

25.

Volpe

Khosla

. Manipulator control with superquadric artificial potential functions: theory and experiments. IEEE Trans Syst Man Cybern 1990; 20: 1423–1436.

26.

Wang

Jiang

, et al. Collision-free navigation of autonomous vehicles using convex quadratic programming-based model predictive control. IEEE/ASME Trans Mechatron 2018; 23: 1103–1113.

27.

Weiskircher

Wang

Ayalew

. Predictive guidance and control framework for (semi-)autonomous vehicles in public traffic. IEEE Trans Control Syst Technol 2017; 25: 2034–2046.

28.

Shamir

. How should an autonomous vehicle overtake a slower moving vehicle: design and analysis of an optimal trajectory. IEEE Trans Automat Contr 2004; 49: 607–610.

29.

Zhu

Chen

Xiong

. A model predictive speed tracking control approach for autonomous ground vehicles. Mech Syst Signal Process 2017; 87: 138–152.

30.

Liu

. Sliding mode control using MATLAB. Amsterdam: Elsevier, 2017.

31.

Feng

Bao

, et al. Research on methods of active steering control based on receding horizon control. Energies 2018; 11: 2243.

32.

Lin

Hao

Sun

, et al. Path tracking of autonomous vehicle with consideration of vehicle stability in sideslip angle phase plane. Proc IMechE, Part D: J Automobile Engineering 2023; 237: 737–753.

33.

Huang

Jiang

Qiu

, et al. Research on intelligent vehicle lane changing and obstacle avoidance control based on road adhesion coefficient. J Vib Control 2022; 28: 3269–3285.