MPC Builder for Autonomous Drive: Automatic Generation of MPCs for Motion Planning and Control

Honda, Kohei; Okuda, Hiroyuki; Suzuki, Tatsuya; Ito, Akira

doi:10.1109/iv55152.2023.10186711

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Thus, the optimal control input sequence U * t is obtained by sampling V k from the optimal action distribution Q * and taking its expected value, as in (6). However, since sampling directly from Q * is not possible, importance sampling [23] is used with the q * (V k | Ũ, Σ Σ Σ) in ( 5), as follows:…”

Section: Mppi Reviewmentioning

confidence: 99%

“…These tasks become especially challenging for fast maneuvering vehicles because the optimal action distribution may be multimodal and rapidly shifting. To solve these tasks, samplingbased Model Predictive Control (MPC) [1], [2] is a widely adopted approach that can handle the non-linearity and nondifferentiability of the environment, such as system dynamics and cost maps, in contrast to gradient-based methods [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

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Connection of nonlinear model predictive controllers for smooth task switching in autonomous driving

Honda

Okuda

Suzuki

et al. 2022

Asian Journal of Control

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Motion planning, decision making, and control are vital functions in autonomous driving for accomplishing the desired driving task while considering passenger comfort, road infrastructure, and surrounding traffic participants. Model predictive control (MPC) is a promising method for simultaneously realizing these functions. However, formulating a single MPC that can run through all driving scenarios is difficult, and previous research has often been conducted to design an MPC for a specific driving task. To extend the availability of MPC for all driving tasks, smooth switching between different MPCs designed for each driving task must be addressed. One of the difficulties in switching between MPCs is guiding the state to a feasible set of optimization problems after switching. In this paper, we present a new framework to realize the smooth connection of MPCs, that is, to reduce the optimization infeasibility at the time of MPC switching. In our proposed method, two general nonlinear MPCs with different state spaces, cost functions, constraints, and formulations can be systematically switched via automatically generated intermediate‐MPCs without requiring any particular alterations. This can help reduce the system complexity of the hybrid MPC system.

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Section: Mppi Reviewmentioning

confidence: 99%