A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach

Huang, Yanjun; Ding, Heng; Zhang, Yubiao; Wang, Hong; Cao, Dongpu; Xu, Nan; Hu, Chuan

doi:10.1109/tie.2019.2898599

Cited by 282 publications

(126 citation statements)

References 39 publications

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“…Reference [51] proposed a MPC path tracking control method for mining articulated vehicle based on preview distance. In order to achieve the goal of expressway emergency collision avoidance, the collision avoidance path planning and path tracking control method based on the MPC are proposed [52][53][54][55]. Luqi Tang et al proposed a cascade control method, a MPC controller is designed based on the vehicle kinematics in upper layer to ensure the prediction accuracy and calculation efficiency and a PID controller is designed to track the upper layer control information [56].…”

Section: G Mpc Control Methodsmentioning

confidence: 99%

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

Yao

Tian

Wang³

et al. 2020

IEEE Access

108

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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Section: G Mpc Control Methodsmentioning

confidence: 99%

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

Yao

Tian

Wang³

et al. 2020

IEEE Access

108

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“…It is combined with the discriminative appearance model to form the dual correlation filter (DCF). Artificial potential field-elaborated resistance (APFE-RN) [18] is a new path planning and tracking technology in the field of autonomous driving. It plans the optimal path based on the local current comparison method (LCCM).…”

Section: Tracking Methodsmentioning

confidence: 99%

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors

Zhao

Zhou

et al. 2020

Sensors

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The main task for real-time vehicle tracking is establishing associations with objects in consecutive frames. After occlusion occurs between vehicles during the tracking process, the vehicle is given a new ID when it is tracked again. In this study, a novel method to track vehicles between video frames was constructed. This method was applied on driving recorder sensors. The neural network model was trained by YOLO v3 and the system collects video of vehicles on the road using a driving data recorder (DDR). We used the modified Deep SORT algorithm with a Kalman filter to predict the position of the vehicles and to calculate the Mahalanobis, cosine, and Euclidean distances. Appearance metrics were incorporated into the cosine distances. The experiments proved that our algorithm can effectively reduce the number of ID switches by 29.95% on the model trained on the BDD100K dataset, and it can reduce the number of ID switches by 32.16% on the model trained on the COCO dataset.

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“…Theorem 1: Consider the vehicle lane keeping and roll dynamics given by (9), under the controller given in (32) with the modified NN weight updated in (34), the closed-loop system is semiglobally stable. Furthermore, the tracking errors asymptotically converge to zero (i.e., ε 1 (t) → 0 as t → ∞), and the transient trajectory of ε 1 (t) can be maintained in the prescribed performance constraints (10).…”

Section: B Ppf and Simplified Nn-based Rise Control Designmentioning

confidence: 99%

“…Complex traffic environment and challenging driving scenarios have brought about higher requirements for AGVs in terms of safety, intelligence, and efficiency [6]- [9]. In this sense, high-performance motion control becomes increasingly important for guaranteeing a safe and robust autonomous driving maneuver [10]. Especially, the integrated vehicle dynamics control in the yaw and roll planes which can further improve the vehicle's overall stability and safety have obtained more research focus and effort [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

RISE-Based Integrated Motion Control of Autonomous Ground Vehicles With Asymptotic Prescribed Performance

Gao

Guo

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach

Cited by 282 publications

References 39 publications

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

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

A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors

RISE-Based Integrated Motion Control of Autonomous Ground Vehicles With Asymptotic Prescribed Performance

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