Performance evaluation of vehicular platoons using Webots

Karoui, Oussama; Guerfala, Emna; Koubâa, Anis; Khalgui, Mohamed; Tovard, Eduardo; Wu, Naiqi; Al-Ahmari, Abdulrahman; Li, Zhiwu

doi:10.1049/iet-its.2017.0036

Cited by 25 publications

(11 citation statements)

References 36 publications

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“…The simulation is composed of three vehicles modeled from a Toyota Prius running a PID-based platooning control model [9] that solely relies on Cooperative Awareness Messages (CAM) to maintain the platooning service, with a safe distance set to 8 meters. Different CAM sending frequencies were evaluated (10 Hz -2.5 Hz).…”

Section: Resultsmentioning

confidence: 99%

“…Although VP can increase fuel efficiency and road capacity, by having vehicle groups traveling close together, VP presents several safety challenges, considering it heavily relies on wireless communications, and upon a set of sensors that can be affected by noise. The ETSI ITS-G5 [10] is considered as the enabler ready-to-go communications technology for such applications, and although there has been extensive analysis of its performance [8], [9], [11], the understanding of its impact upon the safety of these Systems of Systems (SoS) is rather immature. Hence, extensive testing and validation must be carried out to understand the safety limits of such SoS by encompassing communications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards a Realistic Simulation Framework for Vehicular Platooning Applications

Vieira

Severino

Koubâa

et al. 2019

2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC)

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Cooperative vehicle platooning applications increasingly demand realistic simulation tools to ease their validation, and to bridge the gap between development and real-word deployment. However, their complexity and cost, often hinders its validation in the real-world. In this paper we propose a realistic simulation framework for vehicular platoons that integrates Gazebo with OMNeT++ over Robot Operating System (ROS) to support the simulation of realistic scenarios of autonomous vehicular platoons and their cooperative control.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards a Realistic Simulation Framework for Vehicular Platooning Applications

Vieira

Severino

Koubâa

et al. 2019

2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC)

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“…We designed the experimental environment on the Webots simulator [38] [39], as shown in Fig.12. As shown in the experimental scenario, the initial and target positions are set.…”

Section: B Experiments On Webots Simulatormentioning

confidence: 99%

An Experience Aggregative Reinforcement Learning With Multi-Attribute Decision-Making for Obstacle Avoidance of Wheeled Mobile Robot

Ning

Meng

et al. 2020

IEEE Access

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A variety of reinforcement learning (RL) methods are developed to achieve the motion control for the robotic systems, which has been a hot issue. However, the performance of the conventional RL methods often encounters a bottleneck, because the robots have difficulty in choosing an appropriate action in the control task due to the exploration-exploitation dilemma. To address this problem and improve the learning performance, this work introduces an experience aggregative reinforcement learning method with a Multi-Attribute Decision-Making (MADM) to achieve the real-time obstacle avoidance of wheeled mobile robot (WMR). The proposed method employs an experience aggregation method to cluster experiential samples and it can achieve more effective experience storage. Moreover, to achieve the effective action selection using the prior experience, an action selection policy based on a Multi-Attribute Decision-Making is proposed. Inspired by the hierarchical decision-making, this work decomposes the original obstacle avoidance task into two sub-tasks using a divide-and-conquer approach. Each sub-task is trained individually by a double Q-learning using a simple reward function. Each sub-task learns an action policy, which enables the sub-task to selects an appropriate action to achieve a single goal. The standardized rewards of sub-tasks are calculated when fusing these sub-tasks to eliminate differences in rewards for sub-tasks. Then, the proposed method integrates the prior experience of three trained sub-tasks via an action policy based on a MADM to complete the source task. Simulation results show that the proposed method outperforms competitors.

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“…The proposed approach can also be applied to the platooning system which has proved to be effective for increasing traffic efficiency and decreasing automobile fuel consumptions [38]- [41]. This paper studies a simple platooning problem with a leader and a follower, as illustrated in Fig.…”

Section: B Adaptive Cruise Control In a Platooning Programmentioning

confidence: 99%

Control of Black-Box Embedded Systems by Integrating Automaton Learning and Supervisory Control Theory of Discrete-Event Systems

Zhang

Feng

2020

IEEE Trans. Automat. Sci. Eng.

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The paper presents an approach to the control of black-box embedded systems by integrating automaton learning and supervisory control theory (SCT) of discrete-event systems (DES), where automaton models of both the system and requirements are unavailable or hard to obtain. First, the system is tested against the requirements. If all the requirements are satisfied, no supervisor is needed and the process terminates. Otherwise, a supervisor is synthesized to enforce the system to satisfy the requirements. To apply SCT and automaton learning technologies efficiently, the system is abstracted to be a finite-discrete model. Then, a C * learning algorithm is proposed based on the classical L * algorithm to infer a Moore automaton describing both the behavior of the system and the conjunctive behavior of the system and the requirements. Subsequently, a supervisor for the system is derived from the learned Moore automaton and patched on the system. Finally, the controlled system is tested again to check the correctness of the supervisor. If the requirements are still not satisfied, a larger Moore automaton is learned and a refined supervisor is synthesized. The whole process iterates until the requirements hold in the controlled system. The effectiveness of the proposed approach is manifested through two realistic case studies. Note to Practitioners-Supervisory control theory of DES can synthesize maximally permissive supervisory controllers to ensure the correctness of software-controlled processes. The applicationManuscript of supervisory control theory relies on automaton models of the plant and specifications; however, the required models are often unavailable and difficult to obtain for black-box embedded systems. Automaton learning is an effective method for inferring models of black-box systems. This paper integrates the two technologies so that the supervisory control theory is applicable to the development of black-box embedded software systems. The proposed approach is implemented in a toolchain that connects automaton learning algorithms, SCT, and testing algorithms via scripts. The obtained supervisor is implemented as a software patch to monitor and control the original system online.Index Terms-Automaton learning algorithm, black-box embedded system, software testing, supervisory control theory.

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Performance evaluation of vehicular platoons using Webots

Cited by 25 publications

References 36 publications

Towards a Realistic Simulation Framework for Vehicular Platooning Applications

Towards a Realistic Simulation Framework for Vehicular Platooning Applications

An Experience Aggregative Reinforcement Learning With Multi-Attribute Decision-Making for Obstacle Avoidance of Wheeled Mobile Robot

Control of Black-Box Embedded Systems by Integrating Automaton Learning and Supervisory Control Theory of Discrete-Event Systems

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