Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao, Qiangqiang; Tian, Ying; Wang, Qun; Wang, Shengyuan

doi:10.1109/access.2020.3020075

Cited by 106 publications

(62 citation statements)

References 123 publications

(165 reference statements)

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“…The algorithms used in this research are Pure pursuit and Stanley controller. Pure pursuit and Stanley controller use the same 2-dimension bicycle model regardless of vehicle type, so that the calculation is simple and easy to apply compared to other path tracking algorithms [10], [11]. Unfortunately, it was reported that these two algorithms work well only on low-speed and general roads [12].…”

Section: Related Work a Path Tracking Algorithmmentioning

confidence: 99%

Hybrid Tracker Based Optimal Path Tracking System of Autonomous Driving for Complex Road Environments

Seo

Lee²,

Shin³

et al. 2021

IEEE Access

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Path tracking system plays a key technology in autonomous driving. The system should be driven accurately along the lane and be careful not to cause any inconvenience to passengers. To address such tasks, this research proposes hybrid tracker based optimal path tracking system. By applying a deep learning based lane detection algorithm and a designated fast lane fitting algorithm, this research developed a lane processing algorithm that shows a match rate with actual lanes with minimal computational cost. In addition, three modified path tracking algorithms were designed using the GPS based path or the vision based path. In the driving system, a match rate for the correct ideal path does not necessarily represent driving stability. This research proposes hybrid tracker based optimal path tracking system by applying the concept of an observer that selects the optimal tracker appropriately in complex road environments. The driving stability has been studied in complex road environments such as straight road with multiple 3way junctions, roundabouts, intersections, and tunnels. Consequently, the proposed system experimentally showed the high performance with consistent driving comfort by maintaining the vehicle within the lanes accurately even in the presence of high complexity of road conditions. Code will be available in https://github.com/DGIST-ARTIV.

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Section: Related Work a Path Tracking Algorithmmentioning

confidence: 99%

Hybrid Tracker Based Optimal Path Tracking System of Autonomous Driving for Complex Road Environments

Seo

Lee²,

Shin³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

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“…Hierarchical control structure is widely used in vehicle dynamics chassis integrated control and the intelligent driving control system. In the design of the intelligent driving control system, the whole system is often divided into perception layer, decision-making and planning layer, motion control layer, execution layer and so on [23,24]. At present, in DDEVs, LSC is mainly implemented by a hierarchical mechanism, which is generally divided into three layers: vehicle motion state prediction layer, vehicle motion tracking layer, and torque distribution and executive layer [2,3,[25][26][27].…”

Section: Stability-tracking Hierarchical Control Structurementioning

confidence: 99%

Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

Tang

Wang

et al. 2021

Electronics

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For distributed drive autonomous vehicles, adding lateral stability control (LSC) to the trajectory tracking control (TTC) can optimize the distribution of the driving torque of each wheel, so that the vehicle can track the planned trajectory while maintaining stable lateral motion. However, the influence of adding LSC on the TTC system is still unclear. Firstly, a stability-track hierarchical control structure composed of LSC and TTC was established, and the interaction between the two layers was identified as the key of this paper. Then, the Intrinsic Mechanistic framework of the stability-tracking control (STC) was proposed by establishing and analyzing the vehicle dynamic model and control process of two layers. Finally, through simulation experiments, it was found that the change in the curvature of the target trajectory will make the tracking target trajectory and maintaining the lateral stability of the vehicle appear to conflict; in addition, in the LSC layer, the steering characteristics and delay characteristics of different reference models have a greater impact on the lateral stability and trajectory tracking performance; moreover, adjusting the preview time has a more obvious effect on trajectory tracking and lateral stability than the stability correction intensity coefficient.

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“…Since current autonomous vehicles use an electric power steering (EPS), a path tracking controller has been designed for a front wheel steering (FWS) vehicle. Recent advances in path tracking control were summarized in the literature [27][28][29][30][31][32]. Among them, the path tracking control was investigated for a vehicle with 4WS or 4WD [4,9,16,[33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Path Tracking Control With Four-Wheel Independent Steering, Driving and Braking Systems for Autonomous Electric Vehicles

Jeong

Yim

2022

IEEE Access

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This paper presents a method to design a path tracking controller with four-wheel independent braking (4WIB), drive (4WID) and steering (4WIS) systems equipped in in-wheel motordriven electric vehicles (IWM-EVs). Generally, it is difficult to calculate the steering angles of 4WIS and the braking/traction torques of 4WIB/4WID for path tracking control. Moreover, there have been limitations of an error dynamics-based path tracking controller, which requires assumptions on a target path. To cope with these problems, the path tracking problem on a target path is converted into the yaw rate tracking one with a reference yaw rate in this paper. Two methods are adopted for the purpose of calculating a reference yaw rate. The first is to use a pure pursuit method, which generates a steering angle for path tracking. From the steering angle, a reference yaw rate is calculated. The second is to derive a reference yaw rate from a target path and a current vehicle position. For yaw rate tracking, direct yaw moment control is adopted to generate a control yaw moment. A control allocation method is adopted to distribute a control yaw moment into tire forces, generated by 4WIS, 4WID and 4WIB. Several actuator combinations are represented by various sets of virtual weights in the control allocation method. A simulation with a vehicle simulation program, CarSim ® , shows that the proposed path tracking controller is effective in enhancing the path tracking performance with 4WIS, 4WID and 4WIB. From the simulation, effects of actuator combinations on path tracking performance are analyzed.INDEX TERMS Path tracking control, Yaw rate tracking control, 4-wheel independent steering (4WIS), 4wheel independent drive (4WID), 4-wheel independent braking (4WIB), In-wheel motor (IWM) system

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Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Cited by 106 publications

References 123 publications

Hybrid Tracker Based Optimal Path Tracking System of Autonomous Driving for Complex Road Environments

Hybrid Tracker Based Optimal Path Tracking System of Autonomous Driving for Complex Road Environments

Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

Path Tracking Control With Four-Wheel Independent Steering, Driving and Braking Systems for Autonomous Electric Vehicles

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