Numerically Stable Dynamic Bicycle Model for Discrete-time Control

Ge, Qiang; Sun, Qi; Li, Shengbo Eben; Zheng, Sifa; Wu, Wei; Chen, Xi

doi:10.1109/ivworkshops54471.2021.9669260

Cited by 24 publications

(3 citation statements)

References 17 publications

(27 reference statements)

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“…The details of the algorithm are described in [51]. This algorithm requires the discretization of the vehicle model, which can be found in [50], and the results of the discretization is…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Learning-Based Safe Control for Robot and Autonomous Vehicle Using Efficient Safety Certificate

Zheng

Chen

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Energy-function-based safety certificates can provide demonstrable safety for complex automatic control systems used in safety control tasks. However, recent studies on learning-based energy function synthesis have only focused on feasibility, which can lead to over-conservatism and reduce controller efficiency. In this study, we propose using magnitude regularization techniques to enhance the efficiency of safe controllers by reducing conservativeness within the energy function while maintaining promising demonstrable safety guarantees. Specifically, we measure conservativeness by the magnitude of the energy function and reduce it by adding a magnitude regularization term to the integrated loss. We present the SafeMR algorithm, which is synthesized using reinforcement learning (RL) to unify the learning process of the safety controller and the energy function. To verify the effectiveness of the algorithm, we conducted two sets of experiments, one in a robot-based environment and the other in an autonomous vehicle environment. The experimental results demonstrate that the proposed approach reduces the conservativeness of the energy function and outperforms the baseline in terms of controller efficiency for the robot, while ensuring safety.

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“…The details of the algorithm are described in [51]. This algorithm requires the discretization of the vehicle model, which can be found in [50], and the results of the discretization is…”

Section: Methodsmentioning

confidence: 99%

“…Vehicles driving in urban road environments such as intersections do not have particularly complex vehicle dynamics. Therefore, a dynamic bicycle model is sufficient to describe the vehicle dynamics constraints in the experimental scene [50].…”

Section: Introduction and Building Of The Vehicle Modelmentioning

confidence: 99%

Learning-Based Safe Control for Robot and Autonomous Vehicle Using Efficient Safety Certificate

Zheng

Chen

et al. 2023

IEEE Open J. Intell. Transp. Syst.

Self Cite

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show abstract

“…Environment model f (•, •, •) is established to predict the transition of driving environment within the predictive horizon in (3), which typically consists of the model of ego vehicle f ego and that of the surrounding participants f other . The former controls the motion of the automated vehicle, and thus the precise dynamic model has been developed and verified completely [33], wherein the state space equation can be written as…”

Section: Environment Model and Uncertaintymentioning

confidence: 99%

Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning

Ren¹,

Zhan²,

Tang³

et al. 2022

Preprint

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Actor-critic objective penalty function method: an adaptive strategy for trajectory tracking in autonomous driving

Wang,

Bai,

Zhang

2023

Complex Intell. Syst.

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Trajectory tracking is a key technology for controlling the autonomous vehicles effectively and stably to track the reference trajectory. How to handle the various constraints in trajectory tracking is very challenging. The recently proposed generalized exterior point method (GEP) shows high computational efficiency and closed-loop performance in solving the constrained trajectory tracking problem. However, the neural networks used in the GEP may suffer from the ill-conditioning issue during model training, which result in a slow or even non-converging training convergence process and the control output of the policy network being suboptimal or even severely constraint-violating. To effectively deal with the large-scale nonlinear state-wise constraints and avoid the ill-conditioning issue, we propose a model-based reinforcement learning (RL) method called the actor-critic objective penalty function method (ACOPFM) for trajectory tracking in autonomous driving. We adopt an integrated decision and control (IDC)-based planning and control scheme to transform the trajectory tracking problem into MPC-based nonlinear programming problems and embed the objective penalty function method into an actor-critic solution framework. The nonlinear programming problem is transformed into an unconstrained optimization problem and employed as a loss function for model updating of the policy network, and the ill-conditioning issue is avoided by alternately performing gradient descent and adaptively adjusting the penalty parameter. The convergence of ACOPFM is proved. The simulation results demonstrate that the ACOPFM converges to the optimal control strategy fast and steadily, and perform well under the multi-lane test scenario.

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Numerically Stable Dynamic Bicycle Model for Discrete-time Control

Cited by 24 publications

References 17 publications

Learning-Based Safe Control for Robot and Autonomous Vehicle Using Efficient Safety Certificate

Learning-Based Safe Control for Robot and Autonomous Vehicle Using Efficient Safety Certificate

Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning

Actor-critic objective penalty function method: an adaptive strategy for trajectory tracking in autonomous driving

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