Aiming at the problem of realizing safe and stable dynamic trajectory planning for intelligent trucks in complex traffic environments, a real-time trajectory planning method for obstacle avoidance based on the Frenet coordinate system is proposed. In this paper, a cubic spline curve is used to fit the reference path, and decoupled longitudinal and lateral trajectory clusters are generated in real-time using polynomial curves. Simultaneously, a 3-DOF dynamic model for the intelligent truck is developed, and the boundary conditions for vehicle stability are derived. Subsequently, the stability criteria for both rollover and yaw are also established. Moreover, the artificial potential field (APF) theory is incorporated to construct a spatiotemporal coupling model for driving risk assessment. Therefore, a multi-objective optimization function is formulated, while integrating the stability criteria and APF. Trajectory optimization is then performed using the interior-point method. Finally, simulation scenarios, including continuous curves and multiple accelerating obstacle vehicles, are designed to evaluate the method, considering the interplay between risk fields and stability in the trajectory planning process for obstacle avoidance. The results demonstrate that the proposed method enables the intelligent truck to achieve obstacle avoidance with greater safety margins, higher efficiency, and minimal load transfer rate.
SinghS. Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Report no. DOT HS 812 115, Traffic Safety Facts - Crash Stats. https://trid.trb.org/View/1346216 (2015, accessed 24 October 2024).
2.
MontanaroUDixitSFallahS, et al. Towards connected autonomous driving: review of use-cases. Veh Syst Dyn2019; 57(6): 779–814.
3.
ShiYHuangYChenY.Trajectory planning of autonomous trucks for collision avoidance with rollover prevention. IEEE Trans Intell Transp Syst2021; 23(7): 8930–8939.
4.
BrezakMPetrovićI.Real-time approximation of clothoids with bounded error for path planning applications. IEEE Trans Robot2013; 30(2): 507–515.
5.
RastelliJLattaruloRNashashibiF.Dynamic trajectory generation using continuous-curvature algorithms for door to door assistance vehicles. IEEE Intell Veh Symp Proc2014; 30(2): 510–515.
6.
PetrovPNashashibiF.Modeling and nonlinear adaptive control for autonomous vehicle overtaking. IEEE Trans Intell Transp Syst2014; 15(4): 1643–1656.
7.
ZhangXLinigerABorrelliF.Optimization-based collision avoidance. IEEE Trans Control Syst Technol2020; 29(3): 972–983.
8.
ZhangJWuJShenX, et al. Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star. Int J Adv Robot Syst2021; 18(5): 17298814211042730.
9.
LiXGuoZSuD, et al. Time-dependent lane change trajectory optimisation considering comfort and efficiency for lateral collision avoidance. IET Intell Transp Syst2021; 15(5): 595–605.
10.
MercyTVanPPipeleersG.Spline-based motion planning for autonomous guided vehicles in a dynamic environment. IEEE Trans Control Syst Technol2017; 26(6): 2182–2189.
11.
JinZLiJWangH, et al. Rollover prevention and motion planning for an intelligent heavy truck. Chin J Mech Eng2021; 34(1): 1–15.
12.
WangHHuangYKhajepourA, et al. Crash mitigation in motion planning for autonomous vehicles. IEEE Trans Intell Transp Syst2019; 20(9): 3313–3323.
13.
QiJYangHSunH.MOD-RRT*: a sampling-based algorithm for robot path planning in dynamic environment. IEEE Trans Ind Electron2020; 68(8): 7244–7251.
14.
ZhangJJianZFuJ, et al. Trajectory planning with comfort and safety in dynamic traffic scenarios for autonomous driving. In: 2021 IEEE intelligent vehicles symposium workshops (IV workshops), Nagoya, Japan, 11–17 July 2021, pp.342–349. New York: IEEE.
15.
YangDZhengSWenC, et al. A dynamic lane-changing trajectory planning model for automated vehicles. Transp Res Part C Emerg Technol2018; 95: 228–247.
16.
ZhouJZhengHWangJ, et al. Multiobjective optimization of lane-changing strategy for intelligent vehicles in complex driving environments. IEEE Trans Veh Technol2019; 69(2): 1291–1308.
17.
YouCLuJFilevD, et al. Autonomous planning and control for intelligent vehicles in traffic. IEEE Trans Intell Transp Syst2019; 21(6): 2339–2349.
18.
WeiSZouYZhangX, et al. An integrated longitudinal and lateral vehicle following control system with radar and vehicle-to-vehicle communication. IEEE Trans Veh Technol2019; 68(2): 1116–1127.
19.
GuoNZhangXZouY, et al. A supervisory control strategy of distributed drive electric vehicles for coordinating handling, lateral stability, and energy efficiency. IEEE Trans Transp Electrif2021; 7(4): 2488–2504.
20.
HanZXuNChenH, et al. Energy-efficient control of electric vehicles based on linear quadratic regulator and phase plane analysis. Appl Energy2018; 213: 639–657.
21.
WangFShenTZhaoM, et al. Lane-change trajectory planning and control based on stability region for distributed drive electric vehicle. IEEE Trans Veh Technol2023; 73(1): 504–521.
22.
LiuWKhajepourAHeH, et al. Integrated torque vectoring control for a three-axle electric bus based on holistic cornering control method. IEEE Trans Veh Technol2017; 67(4): 2921–2933.
23.
ZhangBZhaoWWangC, et al. Layered time-delay robust control strategy for yaw stability of SbW vehicles. IEEE Trans Intell Veh2023; 8(7): 3913–3924.
24.
HajilooRKhajepourAKasaiezadehA, et al. A model predictive control of electronic limited slip differential and differential braking for improving vehicle yaw stability. IEEE Trans Control Syst Technol2022; 31(2): 797–808.
25.
ChiaWKeohSGohC, et al. Risk assessment methodologies for autonomous driving: a survey. IEEE Trans Intell Transp Syst2022; 23(10): 16923–16939.
26.
WangJHuangHLiK, et al. Towards the unified principles for level 5 autonomous vehicles. Engineering2021; 7(9): 1313–1325.
27.
HuangHLiuJZhengX, et al. Probabilistic situation assessment for intelligent vehicles with uncertain trajectory distribution. Transp Res Rec2021; 2675(11): 119–130.
28.
HuangHLiuJYangY, et al. Risk generation and identification of driver–vehicle–road microtraffic system. ASCE ASME J Risk Uncertain Eng Syst A Civ Eng2022; 8(3): 04022029.
29.
YuSMalawadeAMuthirayanD, et al. Scene-graph augmented data-driven risk assessment of autonomous vehicle decisions. IEEE Trans Intell Transp Syst2021; 23(7): 7941–7951.
30.
ZhangYZouYZhangY, et al. Spatiotemporal interaction pattern recognition and risk evolution analysis during lane changes. IEEE Trans Intell Transp Syst2023; 24(6): 6663–6673.
31.
SongDZhaoJZhuB, et al. Subjective driving risk prediction based on spatiotemporal distribution features of human driver’s cognitive risk. IEEE Trans Intell Transp Syst2024; 25(11): 16687–16703.
32.
TanHLuGLiuM.Risk field model of driving and its application in modeling car-following behavior. IEEE Trans Intell Transp Syst2021; 23(8): 11605–11620.
33.
LiLGanJJiX, et al. Dynamic driving risk potential field model under the connected and automated vehicles environment and its application in car-following modeling. IEEE Trans Intell Transp Syst2020; 23(1): 122–141.
34.
ZhuBHanJZhaoJ, et al. Combined hierarchical learning framework for personalized automatic lane-changing. IEEE Trans Intell Transp Syst2020; 22(10): 6275–6285.
35.
HuangZChuDWuC, et al. Path planning and cooperative control for automated vehicle platoon using hybrid automata. IEEE Trans Intell Transp Syst2018; 20(3): 959–974.
36.
YangTFanW.Enhancing robustness of deep reinforcement learning based adaptive traffic signal controllers in mixed traffic environments through data fusion and multi-discrete actions. IEEE Trans Intell Transp Syst2024; 25(10): 14196–14208.
37.
DuYShangGuanWChaiL.A coupled vehicle-signal control method at signalized intersections in mixed traffic environment. IEEE Trans Veh Technol2021; 70(3): 2089–2100.
38.
BobierCGerdesJ.Staying within the nullcline boundary for vehicle envelope control using a sliding surface. Veh Syst Dyn2013; 51(2): 199–217.
39.
NieZZhouYLianY.Trajectory planning and tracking of dynamic lane change for autonomous buses considering vehicle stability in dynamic traffic scenarios. Proc IMechE, Part D: J Automobile Engineering2024; 239(9): 4284–4304.
40.
LiZLiJMinQ, et al. A fast-computing path tracking control strategy for autonomous multi-axle electric vehicle considering safety and stability. IEEE Trans Transp Electrif2024; 10(3): 6024–6038.
