Application of Baidu Apollo open platform in a course of control simulation experiments

Feng, Minjian; Zhang, Hui

doi:10.1002/cae.22492

Cited by 5 publications

(2 citation statements)

References 34 publications

(40 reference statements)

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“…The MPC-FB (DYC + AFS), MPC (DYC + AFS) and MPC (AFS) are compared with each other. Besides, LQR (Linear Quadratic Regulator) method 33 will be used in the Group A and B comparisons of trajectory following results to show the merit of the MPC method.…”

Section: Simulation and Discussionmentioning

confidence: 99%

Nonlinear model predictive trajectory following control with feedback compensation for autonomous four-wheel independent drive electric vehicles

Zhu

Ao³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Trajectory following is an important function of autonomous vehicles. To enable a four-wheel independent drive electric vehicle to precisely follow a predefined or real-time generated trajectory with good lateral stability and ride comfort at high velocity, a model predictive control (MPC) scheme with feedback compensation considering model mismatch is proposed in this paper to coordinate the direct yaw-moment control and active front steering. The system input signal computed by model predictive control is corrected by the feedback compensation to cope with the model mismatch existing between the controlled vehicle and the nominal model. Moreover, co-simulation is carried out between the software MATLAB/Simulink and Carsim to verify the proposed method. In comparison, the integration of the direct yaw-moment control and active front steering through model predictive control can overcome the non-smooth problem of vehicle dynamics while implementing the active front steering only. However, the value of the external yaw moment computed by the model predictive controller might be too large due to the predictive error in extreme situations, which could endanger the vehicle. The results reveal that the proposed model predictive control scheme with feedback compensation can effectively compensate the front steering angle and enhance the control effect. Therefore, it can significantly improve the trajectory following accuracy and yaw stability.

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Section: Simulation and Discussionmentioning

confidence: 99%

Nonlinear model predictive trajectory following control with feedback compensation for autonomous four-wheel independent drive electric vehicles

Zhu

Ao³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…In [36], the authors present an introduction to the lateral and longitudinal control strategy employed in Apollo. Specifically, the linear quadratic regulator (LQR) controller is used to generate the optimal steering command based on the current vehicle state and planned trajectory.…”

Section: Apollo Platform Integrationmentioning

confidence: 99%

Validation of Automated Driving Function Based on the Apollo Platform: A Milestone for Simulation with Vehicle-in-the-Loop Testbed

Makkapati²,

Li³

et al. 2023

Vehicles

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With the increasing complexity of automated driving features, it is crucial to adopt innovative approaches that combine hardware and software to validate prototype vehicles in the early stages of development. This article demonstrates the effectiveness of a Vehicle-in-the-Loop (ViL) testbed in conducting dynamic tests of vehicles equipped with highly automated driving functions. The tests are designed to replicate critical driving scenarios from real-world environments on the ViL testbed. In this study, the Apollo platform is utilized to develop an automated driving function that can perceive the surrounding traffic in a virtual environment and generate feasible trajectories. This is achieved with the help of a multibody simulation platform. The control commands from the simulated driving function are then transmitted to the real vehicle to execute the planned action. The results demonstrate that critical traffic scenarios can be replicated more safely and repeatedly on the ViL testbed. Meanwhile, the Apollo-based driving function can effectively and comfortably cope with critical scenarios. Importantly, this study marks a significant milestone for the Apollo platform as it is implemented in a real-time system and tested on a ViL testbed.

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Design of Mobile Education Platform for University Network Law Popularization Based on Streaming Media Technology

Zhao,

Zhang

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Application of Baidu Apollo open platform in a course of control simulation experiments

Cited by 5 publications

References 34 publications

Nonlinear model predictive trajectory following control with feedback compensation for autonomous four-wheel independent drive electric vehicles

Nonlinear model predictive trajectory following control with feedback compensation for autonomous four-wheel independent drive electric vehicles

Validation of Automated Driving Function Based on the Apollo Platform: A Milestone for Simulation with Vehicle-in-the-Loop Testbed

Design of Mobile Education Platform for University Network Law Popularization Based on Streaming Media Technology

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