To improve the operation efficiency of the superhighway and to ensure its driving safety, this paper comprehensively uses the methods of theoretical modeling and simulation analysis to explore the trajectory planning and collision avoidance control model of the superhighway. Considering vehicle dynamics constraints and surrounding vehicle constraints, a yaw rate model and a lane change duration boundary model are established to generate a safe and feasible lane change trajectory. The collision avoidance conditions of static obstacles and dynamic obstacles are divided, and the triggering conditions of braking priority and lane change priority are determined based on the braking collision avoidance distance, so as to improve the collision avoidance ability of the vehicle under different driving conditions. An adaptive model predictive control algorithm is designed to realize the tracking control of the vehicle lane-changing trajectory. The CarSim/Simulink cosimulation model was built, and three sets of simulation scenarios were designed based on the superhighway static and dynamic obstacle lane-changing collision avoidance. The effectiveness of the trajectory-tracking control model is verified by the vehicle dynamic parameters and trajectory-tracking accuracy.
NaidenkoS.ChistopolovaM.Hernandez-BlancoJ. A.ErofeevaM.RozhnovV.The Effect of Highway on Spatial Distribution and Daily Activity of Mammals. Transportation Research Part D: Transport and Environment, Vol. 94, 2021, p. 102808. https://doi.org/10.1016/j.trd.2021.102808.
2.
TscharaktschiewS.Why Are Highway Speed Limits Really Justified? An Equilibrium Speed Choice Analysis. Transportation Research Part B: Methodological, Vol. 138, 2020, pp. 317–351. https://doi.org/10.1016/j.trb.2020.05.009.
3.
La TorreF.MeocciM.DomenichiniL.BranziV.TanziN.PaliottoA.Development of an Accident Prediction Model for Italian Freeways. Accident Analysis & Prevention, Vol. 124, 2019, pp. 1–11. https://doi.org/10.1016/j.aap.2018.12.023.
4.
RomanowskaA.BudzynskiM.Investigating the Impact of Weather Conditions and Time of Day on Traffic Flow Characteristics. Weather Climate and Society, Vol. 14, No. 3, 2022, pp. 823–833. https://doi.org/10.1175/WCAS-D-22-0012.1.
5.
HeY.PeiY.Feasibility Study and Necessity Study on the Development of Superhighways. Highway, Vol. 61, No. 1, 2016, pp. 158–162.
6.
National Highway Traffic Safety Administration. Traffic Safety Facts 2020: A Compilation of Motor Vehicle Crash Data. U.S. Department of Transportation, National Highway Traffic Safety Administration, Washington, D.C., 2022.
7.
WangX.LiuS.ZhangJ.NiD.Real-Time Risk Identification and Prediction for the Target Lane’s Following Vehicle during Lane Change. Transportation Research Record: Journal of the Transportation Research Board, 2024. 2678(12): 1785–1798. https://doi.org/10.1177/03611981241252794.
8.
LuJ.LiY. S.Review and Prospect of Modeling Vehicle Lane Changing Behavior. Transportation System Engineering and Information, Vol. 17, No. 4, 2017, pp. 48–55. https://doi.org/10.16097/j.cnki.1009-6744.2017.04.008.
9.
LiuX.WangW.LiX.LiuF.HeZ.YaoY.RuanH.ZhangT.MPC-Based High-Speed Trajectory Tracking for 4WIS Robot. ISA Transactions, Vol. 123, 2022, pp. 413–424. https://doi.org/10.1016/j.isatra.2021.05.018.
10.
ChenG.YaoJ.HuH.GaoZ.HeL.ZhengX.Design and Experimental Evaluation of an Efficient MPC-Based Lateral Motion Controller Considering Path Preview for Autonomous Vehicles. Control Engineering Practice, Vol. 123, 2022, p. 105164. https://doi.org/10.1016/j.conengprac.2022.105164.
11.
UtkinV.Variable Structure Systems with Sliding Modes. IEEE Transactions on Automatic Control, Vol. 22, No. 2, 1977, pp. 212–222. https://doi.org/10.1109/TAC.1977.1101446.
12.
ZhangJ.FuW.An Integrated Framework for Obstacle Avoidance Path Planning and Tracking of Autonomous Vehicles Considering Risk Potential Fields. Robotics and Autonomous Systems, Vol. 194, 2025, p. 105153. https://doi.org/10.1016/j.robot.2025.105153.
13.
LiZ.WangP.LiuH.HuY.ChenH.Coordinated Longitudinal and Lateral Vehicle Stability Control Based on the Combined-Slip Tire Model in the MPC Framework. Mechanical Systems and Signal Processing, Vol. 161, 2021, p. 107947. https://doi.org/10.1016/j.ymssp.2021.107947.
14.
ZhouT.GaoR.SunZ.ZhanY.DaiL.XiaY.Workflow-Based Fast Model Predictive Cloud Control Method for Vehicle Kinematics Trajectory Tracking Problem. IEEE Transactions on Vehicular Technology, Vol. 72, No. 12, 2023, pp. 15365–15374. https://doi.org/10.1109/TVT.2023.3296980.
15.
WangJ.YuY.SongY.ZhaoL.A Novel Adaptive Trajectory Tracking Control for Autonomous Vehicles Based on State Expansion. Journal of Mechanical Science and Technology, Vol. 38, No. 6, 2024, pp. 3143–3154. https://doi.org/10.1007/s12206-024-0532-z.
16.
TengF.WangJ.JinL.WangZ.ZhouZ.LiuZ.Cooperative Control Strategy of Trajectory Tracking and Driving Stability for Distributed-Drive Vehicles under Extreme Conditions. Control Engineering Practice, Vol. 147, 2024, p. 105905. https://doi.org/10.1016/j.conengprac.2024.105905.
17.
PengJ.LiuX.WuC.PiD.ZhouJ.Deep Reinforcement Learning-Tuning Hierarchical Vehicle Trajectory Tracking Framework Based on Improved Kinematic Model Predictive Control. Engineering Applications of Artificial Intelligence, Vol. 162, 2025, p. 112551. https://doi.org/10.1016/j.engappai.2025.112551.
18.
Collares PereiraG.WahlbergB.PetterssonH.MårtenssonJ.Adaptive Reference Aware MPC for Lateral Control of Autonomous Vehicles. Control Engineering Practice, Vol. 132, 2023, p. 105403. https://doi.org/10.1016/j.conengprac.2022.105403.
19.
ZhangF.WuS.ZhangY.LiuH.NieS.DuanJ.LiuR.XiangC.Off-Road Distributed-Drive Electric Vehicle Trajectory Tracking Control with Constrained Transformer MPC. Control Engineering Practice, Vol. 165, 2025, p. 106608. https://doi.org/10.1016/j.conengprac.2025.106608.
20.
AdewaleA.LeeC.Prediction of Car-Following Behavior of Autonomous Vehicle and Human-Driven Vehicle Based on Drivers’ Memory and Cooperation with Lead Vehicle. Transportation Research Record: Journal of the Transportation Research Board, 2024. 2678(6): 248–266. https://doi.org/10.1177/03611981231195051.
BealC. E.GerdesJ. C.Model Predictive Control for Vehicle Stabilization at the Limits of Handling. IEEE Transactions on Control Systems Technology, Vol. 21, No. 4, 2013, pp. 1258–1269. https://doi.org/10.1109/TCST.2012.2200826.
23.
ErlienS. M.FujitaS.GerdesJ. C.Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability. IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 2, 2016, pp. 441–451. https://doi.org/10.1109/TITS.2015.2453404.
24.
Abdi KordaniA.RahmaniO.Abdollahzadeh NasiriA. S.BoroomandradS. M.Effect of Adverse Weather Conditions on Vehicle Braking Distance of Highways. Civil Engineering Journal, Vol. 4, No. 1, 2018, pp. 46–57. https://doi.org/10.28991/cej-030967.
25.
Abdollahzadeh NasiriA. S.RahmaniO.Abdi KordaniA.KarballaeezadehN.MosaviA.Evaluation of Safety in Horizontal Curves of Roads Using a Multi-Body Dynamic Simulation Process. International Journal of Environmental Research and Public Health, Vol. 17, No. 16, 2020, p. 5975. https://doi.org/10.3390/ijerph17165975.
26.
RahmaniO.AghayanI.Abdollahzadeh NasiriA. S.KaneM.HadadiF.A Nonlinear Analytical Approach for Estimating Vehicle Braking Distance Based on Multi-Body Dynamic Simulation. Sādhanā, Vol. 49, No. 1, 2024, p. 20. https://doi.org/10.1007/s12046-023-02381-z.
27.
NieZ.FarzanehH.Energy-Efficient Lane-Change Motion Planning for Personalized Autonomous Driving. Applied Energy, Vol. 338, 2023, p. 120926. https://doi.org/10.1016/j.apenergy.2023.120926.
