This paper presents a path planning method for the lane change maneuver of a full-sized autonomous driving bus in urban environment. A sampling method is incorporated for lane change path planning. Path candidates for lane changes are generated using the pattern of a quintic polynomial function. For optimal path selection among the candidates, a cost function and constraints are designed and applied, considering ride quality, lane change efficiency, and safety. Safety conditions account for lane departure and collision with obstacles. Considering large vehicles such as buses, the region swept by the vehicle body is derived by exploiting the geometrical relationship of the control point and corners, assuming that the control point follows the investigated path candidate. The sweeping region is utilized for accurate and efficient evaluation of the safety condition of the path candidate. Subsequently, longitudinal motion is planned based on Model Predictive Control (MPC) to maintain adequate clearance with surrounding vehicles and follow the desired speed. The proposed algorithm has been evaluated based on closed-loop simulations and vehicle-in-the-loop tests. The test results show that the proposed algorithm plans a safe lane change path, ensuring the vehicle body remains within the safe region. Additionally, the proposed algorithm is shown to enhance real-time performance.
LeeSEOlsenECWierwilleWW, et al. A comprehensive examination of naturalistic lane-changes. Technical Report, National Highway Traffic Safety Administration, USA, 2004.
2.
BaxCLeroyPHagenziekerMP.Road safety knowledge and policy: a historical institutional analysis of the Netherlands. Transp Res Part F Traffic Psychol Behav2014; 25: 127–136.
3.
BishopR. A survey of intelligent vehicle applications worldwide. In: Proceedings of the IEEE intelligent vehicles symposium 2000 (Cat. No. 00TH8511), Dearborn, MI, USA, 5 October 2000, pp.25–30. New York: IEEE.
4.
LeeTKimBYiK, et al. Development of lane change driver model for closed-loop simulation of the active safety system. In: 2011 14th international IEEE conference on intelligent transportation systems (ITSC), Washington, DC, USA, 5–7 October 2011, pp.56–61. New York: IEEE.
5.
ChenB-CTsaiC-TLeeK. Path-following steering controller of automated lane change system with adaptive preview time. In: 2015 IEEE international conference on systems, man, and cybernetics, Hong Kong, China, 9–12 October 2015, pp.2522–2526. New York: IEEE.
6.
HuangXZhangWLiP.A path planning method for vehicle overtaking maneuver using sigmoid functions. IFAC Pap OnLine2019; 52(8): 422–427.
7.
AmmourMOrjuelaRBassetM. Trajectory reference generation and guidance control for autonomous vehicle lane change maneuver. In: 2020 28th Mediterranean conference on control and automation (MED), Saint-Raphaël, France, 15–18 September 2020, pp.13–18. New York: IEEE.
8.
ZieglerJBenderPDangT, et al. Trajectory planning for bertha—a local, continuous method. In: 2014 IEEE intelligent vehicles symposium proceedings, Dearborn, MI, USA, 8–11 June 2014, pp.450–457. New York: IEEE.
9.
LiHLuoYWuJ.Collision-free path planning for intelligent vehicles based on Bézier curve. IEEE Access2019; 7: 123334–123340.
10.
HajilooRAbroshanMKhajepourA, et al. Integrated steering and differential braking for emergency collision avoidance in autonomous vehicles. IEEE Trans Intell Transp Syst2020; 22(5): 3167–3178.
11.
MengYWuYGuQ, et al. A decoupled trajectory planning framework based on the integration of lattice searching and convex optimization. IEEE Access2019; 7: 130530–130551.
12.
LimWLeeSSunwooM, et al. Hierarchical trajectory planning of an autonomous car based on the integration of a sampling and an optimization method. IEEE Trans Intell Transp Syst2018; 19(2): 613–626.
13.
KimDKimGKimH, et al. A hierarchical motion planning framework for autonomous driving in structured highway environments. IEEE Access2022; 10: 20102–20117.
14.
FengZSongWFuM, et al. Decisionmaking and path planning for highway autonomous driving based on spatio-temporal lane-change gaps. IEEE Syst J2021; 16(2): 3249–3259.
15.
LiuYZhouBWangX, et al. Dynamic lane-changing trajectory planning for autonomous vehicles based on discrete global trajectory. IEEE Trans Intell Transp Syst2021; 23(7): 8513–8527.
16.
ChenYHuCWangJ.Motion planning with velocity prediction and composite nonlinear feedback tracking control for lane-change strategy of autonomous vehicles. IEEE Trans Intell Veh2019; 5(1): 63–74.
17.
ZieglerJStillerC. Fast collision checking for intelligent vehicle motion planning. In: 2010 IEEE intelligent vehicles symposium, La Jolla, CA, USA, 21–24 June 2010, pp.518–522. New York: IEEE.
18.
ZhangYChenHWaslanderSL, et al. Hybrid trajectory planning for autonomous driving in highly constrained environments. IEEE Access2018; 6: 32800–32819.
19.
CesariGSchildbachGCarvalhoA, et al. Scenario model predictive control for lane change assistance and autonomous driving on highways. IEEE Intell Transp Syst Mag2017; 9(3): 23–35.
20.
BrüdigamTOlbrichMWollherrD, et al. Stochastic model predictive control with a safety guarantee for automated driving. IEEE Trans Intell Veh2021; 8: 22–36.
21.
ZhangXLinigerABorrelliF.Optimization-based collision avoidance. IEEE Trans Control Syst Technol2020; 29(3): 972–983.
22.
ShengZLiuLXueS, et al. A cooperation-aware lane change method for autonomous vehicles. arXiv preprint arXiv:2201.10746, 2022.
23.
ZhouJZhengHWangJ, et al. Multiobjective optimization of lane-changing strategy for intelligent vehicles in complex driving environments. IEEE Trans Veh Technol2019; 69(2): 1291–1308.
24.
HuJYiSZhuP, et al. Obstacle avoidance decision and trajectory tracking control of intelligent vehicle considering surrounding vehicles. Proc IMechE, Part D: J Automobile Engineering. Epub ahead of print 4January2024. DOI: 10.1177/09544070231204390.
25.
ArtunedoAVillagraJGodoyJ.Real-time motion planning approach for automated driving in urban environments. IEEE Access2019; 7: 180039–180053.
26.
YiBBenderPBonarensF, et al. Model predictive trajectory planning for automated driving. IEEE Trans Intell Veh2018; 4(1): 24–38.
27.
XieSHuJBhowmickP, et al. Distributed motion planning for safe autonomous vehicle overtaking via artificial potential field. IEEE Trans Intell Transp Syst2022; 23(11): 21531–21547.
28.
WangSLiZWangB, et al. Velocity obstacle-based collision avoidance and motion planning framework for connected and automated vehicles. Transp Res Rec2022; 2676(5): 748–766.
29.
WuHZhangYHuangL, et al. Research on vehicle obstacle avoidance path planning based on APF-PSO. Proc IMechE, Part D: J Automobile Engineering2023; 237(6): 1391–1405.
30.
OliveiraRLimaPFPereiraGC, et al. Path planning for autonomous bus driving in highly constrained environments. In: 2019 IEEE intelligent transportation systems conference (ITSC), Auckland, New Zealand, 27–30 October 2019, pp.2743–2749. New York: IEEE.
31.
OliveiraRLimaPCirilloM, et al. Autonomous bus driving: a novel motion-planning approach. IEEE Veh Technol Mag2021; 16(3): 29–37.
32.
KimBYiK.Probabilistic and holistic prediction of vehicle states using sensor fusion for application to integrated vehicle safety systems. IEEE Trans Intell Transp Syst2014; 15(5): 2178–2190.
33.
SuhJChaeHYiK.Stochastic model-predictive control for lane change decision of automated driving vehicles. IEEE Trans Veh Technol2018; 67(6): 4771–4782.
34.
JeongYKimSJoBR, et al. Sampling based vehicle motion planning for autonomous valet parking with moving obstacles. Int J Automot Eng2018; 9(4): 215–222.
35.
ChaeHJeongYLeeH, et al. Design and implementation of human driving data–based active lane change control for autonomous vehicles. Proc IMechE, Part D: J Automobile Engineering2021; 235(1): 55–77.
36.
WangJZhangQZhaoD, et al. Lane change decision-making through deep reinforcement learning with rule-based constraints. In: 2019 international joint conference on neural networks (IJCNN), Budapest, Hungary, 14–19 July 2019, pp.1–6. New York: IEEE.
37.
LiuYWangXLiL, et al. A novel lane change decision-making model of autonomous vehicle based on support vector machine. IEEE Access2019; 7: 26543–26550.
38.
DeoNTrivediMM. Convolutional social pooling for vehicle trajectory prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Salt Lake City, UT, USA, 18–22 June 2018, pp.1468–1476. New York: IEEE.
39.
KimGKimDAhnY, et al. Hybrid approach for vehicle trajectory prediction using weighted integration of multiple models. IEEE Access2021; 9: 78715–78723.
40.
WerlingMZieglerJKammelS, et al. Optimal trajectory generation for dynamic street scenarios in a Frenet Frame. In: 2010 IEEE international conference on robotics and automation, Anchorage, AK, USA, 3–7 May 2010, pp.987–993. New York: IEEE.
41.
ChuKLeeMSunwooM.Local path planning for off-road autonomous driving with avoidance of static obstacles. IEEE Trans Intell Transp Syst2012; 13(4): 1599–1616.
42.
HuangJHeZArakawaY, et al. Trajectory planning in Frenet frame via multi-objective optimization. IEEE Access2023; 11: 70764–70777.
43.
KimMSLeeJHKimTL, et al. Frenet frame based local motion planning in racing environment. In: 2023 23rd international conference on control, automation and systems (ICCAS), Yeosu, Republic of Korea, 17–20 October 2023, pp.951–957. New York: IEEE.
44.
TakahashiAHongoTNinomiyaY, et al. Local path planning and motion control for AGV in positioning. In: Proceedings of the IEEE/RSJ international workshop on intelligent robots and systems’ (IROS’89) The autonomous mobile robots and its applications, Tsukuba, Japan, 4–6 September 1989, pp.392–397. New York: IEEE.
45.
AnderssonJAGillisJHornG, et al. CasADi: a software framework for nonlinear optimization and optimal control. Math Program Comput2019; 11: 1–36.
46.
CoulterRC.Implementation of the pure pursuit path tracking algorithm. Pittsburgh, PA: Carnegie Mellon University, The Robotics Institute, 1992.
