Event-Triggered Model Predictive Control for Autonomous Vehicle Path Tracking: Validation Using CARLA Simulator

Zhou, Zhaodong; Rother, Christopher; Chen, Jun

doi:10.1109/tiv.2023.3266941

Cited by 31 publications

(4 citation statements)

References 40 publications

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“…The virtual world is generally constructed through various simulation software and frameworks. According to the testing requirements and the necessary content for the virtual world in VIL, the prevalent simulation software in the market is divided into three distinct categories in this article: open‐source frameworks based on game engines (e.g., CARLA and AirSim), [ 59,60 ] commercial software designed for ADAS functionality testing (such as PreScan, [ 61 ] CarSim, [ 62 ] CarMaker, [ 63 ] and etc. ), and traffic flow simulation software like SUMO and VISSIM.…”

Section: Testbench‐based Vehicle‐in‐the‐loop For Av Testingmentioning

confidence: 99%

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Cheng,

Wang,

Zhao

et al. 2024

Advanced Intelligent Systems

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Autonomous vehicles (AVs) must be thoroughly tested to ensure safety and reliability before marketing. Simulation‐based testing has gained widespread recognition as the essential approach for AV testing by providing sufficient testing scenarios in the virtual environment. Vehicle‐in‐the‐loop (VIL) simulation has the ability to perform comprehensive tests and validations for the AVs’ overall behaviors while keeping significant testing accuracy and efficiency through the combination of the virtual scenarios and the physical AV. This article provides an overview of representative studies on testbench‐based VIL simulation testing for AVs, mainly focusing on utilizing testbenches to simulate realistic road conditions, and using physical signal stimulation methods and related equipment to generate sensors’ physical signals. This article first summarizes current AV testing studies, identifying existing issues and flaws of the state‐of‐the‐art methods and tools. Afterward, the testbench‐based VIL is addressed around architecture, principles, advantages, and characteristics. Then, the road condition simulation and the sensor physical signal generation in VIL are discussed in depth from structure, principle, and corresponding advanced equipment. Finally, research gaps between cutting‐edge technologies and AV testing applications in industrialization are identified to facilitate future research in this direction.

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Section: Testbench‐based Vehicle‐in‐the‐loop For Av Testingmentioning

confidence: 99%

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Cheng,

Wang,

Zhao

et al. 2024

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Batt < I Batt < I Low Batt (31) The charge current represents a positive value, while the discharge current represents a negative value. For this reason, the discharge current limit is shown as the lower limit, and the charging current is shown as the upper limit.…”

Section: Lowmentioning

confidence: 99%

“…The potential benefits of using event-triggered MPC strategies are demonstrated for BEVs and HEVs, including improved fuel economy, battery life, and reduced computational complexity, in [30][31][32]. MPC and DQN are different approaches and require different problem-solving methods.…”

Section: Introductionmentioning

confidence: 99%

Energy Management of P2 Hybrid Electric Vehicle Based on Event-Triggered Nonlinear Model Predictive Control and Deep Q Network

Haspolat

Yalçın

2023

WEVJ

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Hybrid electric vehicles (HEVs) are used as a bridge during the transition to battery electric vehicles (BEVs) and to make energy consumption more efficient. The main problem in improving the efficiency of HEV energy consumption is torque management. In this study, a novel approach based on a nonlinear model predictive controller to solve the reference tracking and torque distribution problem is proposed. That is to say, in order to increase the efficiency of torque distribution, the weights of nonlinear model predictive control (NMPC) are trained with a Deep Q Network (DQN), and an event-triggered mechanism is designed with DQN to reduce the computational cost of MPC. The considered torque distribution problem varies according to the type and structure of the HEV. In this study, a parallel type 2 hybrid electric vehicle (P2 HEV) is considered and modeled via publicly shared passenger vehicle data of the engine, motor, high-voltage battery, transmission, clutch, differential, and wheel characteristics. NMPC is formulated so that the torque values remain within the physical limits of the engine, and the battery also operates at its physical limits. Namely, it is guaranteed that the battery works according to a certain state of charge (SOC) window and current limits. The state of health (SOH) of the battery is also considered in the optimization. The motor and engine efficiencies increase by 3.61% and 2.86%, respectively, with the proposed control structure, while the computational cost is reduced by 52.01% when utilizing the proposed event-triggering mechanism in the NMPC controller.

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“…Another approach, event-triggered MPC (eMPC), triggers the MPC controller to determine a new set of control actions when the defined trigger conditions are met, and otherwise, it follows the predicted optimal control sequence [11][12][13][14][15][16]. The stability of event-triggered MPC is discussed in detail in [12,17], which provide essential insights and methodologies for ensuring the stability of event-triggered MPC.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Event-Triggered Active-Battery-Cell-Balancing Control for Electric Vehicle Range Extension

Flessner,

Chen,

Xiong

2024

Electronics

Self Cite

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Optimal control techniques such as model predictive control (MPC) have been widely studied and successfully applied across a diverse field of applications. However, the large computational requirements for these methods result in a significant challenge for embedded applications. While event-triggered MPC (eMPC) is one solution that could address this issue by taking advantage of the prediction horizon, one obstacle that arises with this approach is that the event-trigger policy is complex to design to fulfill both throughput and control performance requirements. To address this challenge, this paper proposes to design the event trigger by training a deep Q-network reinforcement learning agent (RLeMPC) to learn the optimal event-trigger policy. This control technique was applied to an active-cell-balancing controller for the range extension of an electric vehicle battery. Simulation results with MPC, eMPC, and RLeMPC control policies are presented along with a discussion of the challenges of implementing RLeMPC.

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Event-Triggered Model Predictive Control for Autonomous Vehicle Path Tracking: Validation Using CARLA Simulator

Cited by 31 publications

References 40 publications

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Energy Management of P2 Hybrid Electric Vehicle Based on Event-Triggered Nonlinear Model Predictive Control and Deep Q Network

Reinforcement Learning-Based Event-Triggered Active-Battery-Cell-Balancing Control for Electric Vehicle Range Extension

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