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Vehicles
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Path Tracking for Automated Driving
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Path Tracking for Automated Driving

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 25 November 2024 | Viewed by 10076

Special Issue Editors

Dr. Zejiang Wang

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN, USA
Interests: vehicle dynamics and control; intelligent transportation; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Wenshuo Wang

E-Mail Website
Guest Editor
Department of Civil Engineering, McGill University, Montreal, QC, Canada
Interests: autonomous driving; human-robot interaction; human factors; bayesian learning; optimization
Dr. Anh-Tu Nguyen

E-Mail Website
Guest Editor
INSA HdF, Polytechnic University of Hauts-de-France, 59313 Valenciennes, France
Interests: robust control and estimation; cybernetics control systems; human-machine shared control; vehicle dynamics estimation and control; intelligent vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Moad Kissai

E-Mail Website
Guest Editor
Group Renault, 78280 Guyancourt, France
Interests: vehicle dynamics; robust control; optimal control; preictive control; learning-based control; advanced driver assistance systems; autonomous driving
Special Issues, Collections and Topics in MDPI journals
Dr. Umar Zakir Abdul Hamid

E-Mail Website
Guest Editor
China Euro Vehicle Technology (CEVT AB), Gothenburg, Sweden
Interests: ADAS; autonomous driving; collision avoidance; future mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ground vehicle path-tracking control constitutes the cornerstone of fully autonomous driving. Advanced modeling, estimation, and control techniques have been continuously invented and implemented to achieve high-performance path tracking for automated driving, especially under adversary situations, such as high-speed collision avoidance, actuation failure, and sudden drop of tire–road friction. In addition to the safety requirements, energy-saving should also be accounted for during path-tracking controller design to achieve sustainable transportation, especially when autonomous vehicles are deployed on a large scale. Finally, objective and systematic evaluation frameworks to compare the strengths and weaknesses of various path-tracking algorithms are still severely lacking.

This Special Issue is devoted to theoretical breakthroughs, practical solutions, and comprehensive evaluations of novel modeling, estimation, and control algorithms for ground vehicle path tracking. Topics include, but are not limited to, the following: automated driving systems, path tracking for collision avoidance, energy-optimal trajectory following, and path tracking controller evaluation.

Dr. Zejiang Wang
Dr. Wenshuo Wang
Dr. Anh-Tu Nguyen
Dr. Moad Kissai
Dr. Umar Zakir Abdul Hamid
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vehicles is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • automated driving
  • collision avoidance
  • control evaluation
  • path-tracking control
  • safety/energy-saving co-design

Published Papers (6 papers)

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Research

17 pages, 5149 KiB  
Article
Effectiveness of the Autonomous Braking and Evasive Steering System OPREVU-AES in Simulated Vehicle-to-Pedestrian Collisions
by Ángel Losada, Francisco Javier Páez, Francisco Luque and Luca Piovano
Vehicles 2023, 5(4), 1553-1569; https://doi.org/10.3390/vehicles5040084 - 02 Nov 2023
Viewed by 1324
Abstract
This paper proposes a combined system (OPREVU-AES) that integrates optimized AEB and Automatic Emergency Steering (AES) to generate evasive maneuvers, and it provides an assessment of its effectiveness when compared to a commercial AEB system. The optimized AEB system regulates the braking response [...] Read more.
This paper proposes a combined system (OPREVU-AES) that integrates optimized AEB and Automatic Emergency Steering (AES) to generate evasive maneuvers, and it provides an assessment of its effectiveness when compared to a commercial AEB system. The optimized AEB system regulates the braking response through a collision prediction model. OPREVU is a research project in which INSIA-UPM and CEDINT-UPM cooperate to improve driving assistance systems and to characterize pedestrians’ behavior through virtual reality (VR) techniques. The kinematic and dynamic analysis of OPREVU-AES is conducted using CarSim© software v2020.1. The avoidance trajectories are predefined for speeds above 40 km/h, which controls the speed and lateral stability during the overtaking and lane re-entry process. In addition, the decision algorithm integrates information from the lane and the blind spot detectors. The effectiveness evaluation is based on the reconstruction of a sample of vehicle-to-pedestrian crashes (INSIA-UPM database), using PCCrash© software v. 2013, and it considers the probability of head injury severity (ISP) as an indicator. The incorporation of AEB can avoid 53.8% of accidents, with an additional 2.5–3.5% avoided by incorporating automatic steering. By increasing the lateral activation range, the total avoidance rate is increased to 61.8–69.8%. The average ISP reduction is 65%, with significant reductions achieved in most cases where avoidance is not possible. Full article
(This article belongs to the Special Issue Path Tracking for Automated Driving)
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25 pages, 3636 KiB  
Article
Vehicle State Estimation and Prediction for Autonomous Driving in a Round Intersection
by Xinchen Li, Levent Guvenc and Bilin Aksun-Guvenc
Vehicles 2023, 5(4), 1328-1352; https://doi.org/10.3390/vehicles5040073 - 05 Oct 2023
Viewed by 1303
Abstract
This paper presents methods for vehicle state estimation and prediction for autonomous driving. A round intersection is chosen for application of the methods and to illustrate the results as autonomous vehicles have difficulty in handling round intersections. State estimation based on the unscented [...] Read more.
This paper presents methods for vehicle state estimation and prediction for autonomous driving. A round intersection is chosen for application of the methods and to illustrate the results as autonomous vehicles have difficulty in handling round intersections. State estimation based on the unscented Kalman filter (UKF) is presented in the paper and then applied to state estimation of vehicles in a round intersection. The microscopic traffic simulator SUMO (Simulation of Urban Mobility) is used to generate realistic traffic in the round intersection for the simulation experiments. Change point detection-based driving behavior prediction using a multipolicy approach is then introduced and evaluated for the round intersection. Finally, these methods are combined for vehicle trajectory estimation based on UKF and policy prediction and demonstrated using the round intersection. Full article
(This article belongs to the Special Issue Path Tracking for Automated Driving)
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20 pages, 6902 KiB  
Article
Real-Time Hardware-in-the-Loop Emulation of Path Tracking in Low-Cost Agricultural Robots
by Ingrid J. Moreno, Dina Ouardani, Daniel Chaparro-Arce and Alben Cardenas
Vehicles 2023, 5(3), 894-913; https://doi.org/10.3390/vehicles5030049 - 29 Jul 2023
Cited by 2 | Viewed by 1411
Abstract
Reducing costs and time spent in experiments in the early development stages of vehicular technology such as off-road and agricultural semi-autonomous robots could help progress in this research area. In particular, evaluating path tracking strategies in the semi-autonomous operation of robots becomes challenging [...] Read more.
Reducing costs and time spent in experiments in the early development stages of vehicular technology such as off-road and agricultural semi-autonomous robots could help progress in this research area. In particular, evaluating path tracking strategies in the semi-autonomous operation of robots becomes challenging because of hardware costs, the time required for preparation and tests, and constraints associated with external aspects such as meteorological or weather conditions or limited space in research laboratories. This paper proposes a methodology for the real-time hardware-in-the-loop emulation of path tracking strategies in low-cost agricultural robots. This methodology enables the real-time validation of path tracking strategies before their implementation on the robot. To validate this, we propose implementing a path tracking strategy using only the information of motor’s angular speed and robot yaw velocity obtained from encoders and a low-cost inertial measurement unit (IMU), respectively. This paper provides a simulation with MATLAB/Simulink, hardware-in-the-loop with Qube-servo (Quanser), and experimental results with an Agribot platform to confirm its validity. Full article
(This article belongs to the Special Issue Path Tracking for Automated Driving)
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18 pages, 24374 KiB  
Article
Path Planning for Perpendicular Parking of Large Articulated Vehicles Based on Qualitative Kinematics and Geometric Methods
by Inhwan Han
Vehicles 2023, 5(3), 876-893; https://doi.org/10.3390/vehicles5030048 - 19 Jul 2023
Cited by 2 | Viewed by 1226
Abstract
Since large articulated vehicles have uncertainties in trailer articulation angle as well as dynamic complexity, it is not easy to accurately establish a reliable motion plan. In this paper, two geometric path plans constructed based on the empirical rules of driving experts are [...] Read more.
Since large articulated vehicles have uncertainties in trailer articulation angle as well as dynamic complexity, it is not easy to accurately establish a reliable motion plan. In this paper, two geometric path plans constructed based on the empirical rules of driving experts are presented so that articulated vehicles can automatically perform perpendicular parking on a reverse path. By analyzing the empirical parking methods of professional drivers, these path plans were constructed by appropriately combining several standardized simple basic motions to facilitate implementation in real vehicles. In addition, the path plans included appropriate complementary motions to effectively respond to uncertainties arising from articulation angles, etc. The complementary motions developed in this study are based on the results of qualitative analysis on the behavior of articulated vehicles. The usefulness of the proposed articulated vehicle parking method has been proven through hundreds of experimental tests using a scaled model automated vehicle. Full article
(This article belongs to the Special Issue Path Tracking for Automated Driving)
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20 pages, 9477 KiB  
Article
Speed-Adaptive Model-Free Path-Tracking Control for Autonomous Vehicles: Analysis and Design
by Marcos Moreno-Gonzalez, Antonio Artuñedo, Jorge Villagra, Cédric Join and Michel Fliess
Vehicles 2023, 5(2), 698-717; https://doi.org/10.3390/vehicles5020038 - 13 Jun 2023
Cited by 6 | Viewed by 1644
Abstract
One of the challenges of autonomous driving is to increase the number of situations in which an intelligent vehicle can continue to operate without human intervention. This requires path-tracking control to keep the vehicle stable while following the road, regardless of the shape [...] Read more.
One of the challenges of autonomous driving is to increase the number of situations in which an intelligent vehicle can continue to operate without human intervention. This requires path-tracking control to keep the vehicle stable while following the road, regardless of the shape of the road or the longitudinal speed at which it is moving. In this work, a control strategy framed in the Model-Free Control paradigm is presented to control the lateral vehicle dynamics in a decoupled control architecture. This strategy is designed to guide the vehicle through trajectories with diverse dynamic constraints and over a wide speed range. A design method for this control strategy is proposed, and metrics for trajectory tracking quality, system stability, and passenger comfort are applied to evaluate the controller’s performance. Finally, simulation and real-world tests show that the developed strategy is able to track realistic trajectories with a high degree of accuracy, safety, and comfort. Full article
(This article belongs to the Special Issue Path Tracking for Automated Driving)
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22 pages, 1604 KiB  
Article
Pragmatic and Effective Enhancements for Stanley Path-Tracking Controller by Considering System Delay
by Alexander Seiffer, Michael Frey and Frank Gauterin
Vehicles 2023, 5(2), 615-636; https://doi.org/10.3390/vehicles5020034 - 23 May 2023
Cited by 1 | Viewed by 2024
Abstract
The Stanley controller is a proven approach for path tracking control in automated vehicles. If time delays occur, for example, in signal processing and steering angle control, precision and stability decrease. In this article, enhancements for the Stanley controller are proposed to achieve [...] Read more.
The Stanley controller is a proven approach for path tracking control in automated vehicles. If time delays occur, for example, in signal processing and steering angle control, precision and stability decrease. In this article, enhancements for the Stanley controller are proposed to achieve stable behavior with improved tracking accuracy. The approach uses the curvature of the path as feedforward, whereby the reference point for the feedforward input differs from that of the controller setpoints. By choosing a point further along the path, the negative effects of system delay are reduced. First, the parameters of the Stanley controller are calibrated using a straight line and circle maneuver. Then, the newly introduced feedforward parameter is optimized on a dynamic circuit. The approach was evaluated in simulation and validated on a demonstrator vehicle. The validation tests with the demonstrator vehicle on the dynamic circuit revealed a reduction of the root-mean-square cross-track error from 0.11 m to 0.03 m compared to the Stanley controller. We proved that the proposed approach optimizes the Stanley controller in terms of compensating for the negative effects of system delay. This allows it to be used in a wider range of applications that would otherwise require a more complex control approach. Full article
(This article belongs to the Special Issue Path Tracking for Automated Driving)
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