Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits

Serna, Citlalli Gámez; Ruichek, Yassine

doi:10.3390/s17061383

Cited by 39 publications

(16 citation statements)

References 33 publications

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“…Thus, Elbanhawi et al proposed that a smooth vehicle lateral control system can solve the MS issue [11]. Improvement in the lateral control system for an autonomous vehicle can be achieved by integrating a path tracking controller with a path planner [12], dynamic speed adaptation (DSA) [13] and torque vectoring control [14]. Besides combining the path tracking controller with a high-computation control system, the lateral system can also be improved by simply adding a corrective wheel angle to the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle

et al. 2020

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In terms of vehicle dynamics, motion sickness (MS) occurs because of the large lateral acceleration produced by inappropriate wheel turning. In terms of passenger behavior, subjects experience MS because they normally tilt their heads towards the direction of lateral acceleration. Relating these viewpoints, the increment of MS originates from the large lateral acceleration produced by the inappropriate wheel’s turn, which then causes greater head movement with respect to the lateral acceleration direction. Therefore, this study proposes the utilization of fuzzy-proportional integral derivative (PID) controller for an MS minimization control structure, where the interaction of the lateral acceleration and head tilt concept is adopted to diminish the lateral acceleration. Here, the head movement is used as the controlled variable to compute the corrective wheel angle. The estimation of the head movement is carried out by an estimation model developed by the radial basis function neural network (RBFNN) method. An experiment involving a driving simulator was conducted, to verify the proposed control system’s performance in regard to the autonomous vehicle’s passengers. The results show that the averages of motion sickness incidence (MSI) index can be lowered by 3.95% for single lap and 11.49% for ten laps.

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Section: Introductionmentioning

confidence: 99%

Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle

et al. 2020

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“…An AV can be defined as a vehicle that is capable of intelligent motion and action without human input [20]. AVs have attracted the attention of the scientific community due to the large number of applications in which they can be involved (for instance, target tracking [21], surveillance [22], transportation [23], self-driving cars [24], etc.). In our work, a target tracking application is developed, where the AV concretely is a differential robot.…”

Section: Introductionmentioning

confidence: 99%

A Remote Control Strategy for an Autonomous Vehicle with Slow Sensor Using Kalman Filtering and Dual-Rate Control

Cuenca

Zhan

Salt

et al. 2019

Sensors

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This work presents a novel remote control solution for an Autonomous Vehicle (AV), where the system structure is split into two sides. Both sides are assumed to be synchronized and linked through a communication network, which introduces time-varying delays and packet disorder. An Extended Kalman Filter (EKF) is used to cope with the non-linearities that appear in the global model of the AV. The EKF fuses the data provided by the sensing devices of the AV in order to estimate the AV state, reducing the noise effect. Additionally, the EKF includes an h-step-ahead state prediction stage, which, together with the consideration of a packet-based control strategy, enables facing the network-induced delays. Since the AV position is provided by a camera, which is a slow sensing device, a dual-rate controller is required to achieve certain desired (nominal) dynamic control performance. The use of a dual-rate control framework additionally enables saving network bandwidth and deals with packet disorder. As the path-tracking control algorithm, pure pursuit is used. Application results show that, despite existing communication problems and slow-rate measurements, the AV is able to track the desired path, keeping the nominal control performance.

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“…These cases require that the car reaches a certain point on the path in the time window to avoid collision. However, most of the existing methods [ 3 , 4 , 7 , 8 , 14 ] ignore these constraints, which make their methods applicable only in static environments.…”

Section: Introductionmentioning

confidence: 99%

Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization

Zhang

Chen

Waslander

et al. 2018

Sensors

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In this paper, we present a complete, flexible and safe convex-optimization-based method to solve speed planning problems over a fixed path for autonomous driving in both static and dynamic environments. Our contributions are five fold. First, we summarize the most common constraints raised in various autonomous driving scenarios as the requirements for speed planner developments and metrics to measure the capacity of existing speed planners roughly for autonomous driving. Second, we introduce a more general, flexible and complete speed planning mathematical model including all the summarized constraints compared to the state-of-the-art speed planners, which addresses limitations of existing methods and is able to provide smooth, safety-guaranteed, dynamic-feasible, and time-efficient speed profiles. Third, we emphasize comfort while guaranteeing fundamental motion safety without sacrificing the mobility of cars by treating the comfort box constraint as a semi-hard constraint in optimization via slack variables and penalty functions, which distinguishes our method from existing ones. Fourth, we demonstrate that our problem preserves convexity with the added constraints, thus global optimality of solutions is guaranteed. Fifth, we showcase how our formulation can be used in various autonomous driving scenarios by providing several challenging case studies in both static and dynamic environments. A range of numerical experiments and challenging realistic speed planning case studies have depicted that the proposed method outperforms existing speed planners for autonomous driving in terms of constraint type covered, optimality, safety, mobility and flexibility.

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Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits

Cited by 39 publications

References 33 publications

Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle

Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle

A Remote Control Strategy for an Autonomous Vehicle with Slow Sensor Using Kalman Filtering and Dual-Rate Control

Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization

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