Scenario-based stochastic MPC for vehicle speed control considering the interaction with pedestrians

Tran, Anh-Tuan; Muraleedharan, Arun; Okuda, Hiroyuki; Suzuki, Tatsuya

doi:10.1016/j.ifacol.2020.12.2341

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…The iMPC, however, is also effective for switching between various driving tasks with different state spaces and constraints other than ACC/LK and LC tasks. For example, let us consider the case of switching from the lane‐keeping (LK) task to the pedestrian avoidance (PA) task as described in [20]. The PA‐MPC, which performs pedestrian avoidance, has the state of the pedestrian and imposes an inequality constraint such that it cannot approach the pedestrian more than a safe distance away.…”

Section: Discussion On Generalitymentioning

confidence: 99%

“…On the contrary, Figure 1b demonstrates the latter method that does not design an external prediction module but extends the state space of the MPC to describe the interaction with others and optimize its own-vehicle behavior considering the interactive prediction [17][18][19][20]. In this method, the MPC is formulated in the extended state space, including the state of the ego vehicle and others in the surrounding area.…”

Section: Introductionmentioning

confidence: 99%

“…However, in this framework, the state space has to be changed and the optimization problem reformulated according to the change in the driving task. Previous studies have often focused only on a single driving task, such as obstacle avoidance [17,20] and changing lanes [18,19].…”

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: Discussion On Generalitymentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…MPC performs well particularly in dynamic environments by embedding the prediction models of surrounding road users into the constraints of the optimization problem [2]. Consequently, MPC has been applied to complex driving scenarios in the presence of other road users, such as lane changing [3]- [5], obstacle avoidance [6], [7], pedestrian avoidance [8], [9], adaptive cruise control (ACC) [10], and turning and crossing at intersections [11], [12].…”

Section: Introductionmentioning

confidence: 99%

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

Honda¹,

Okuda²,

Itō³

et al. 2022

Preprint

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This study presents a new framework for vehicle motion planning and control based on the automatic generation of model predictive controllers (MPC) named MPC Builder. In this framework, several components necessary for MPC, such as models, constraints, and cost functions, are prepared in advance. The MPC Builder then online generates various MPCs according to traffic situations in a unified manner. This scheme enabled us to represent various driving tasks with minimal design effort. The proposed framework was implemented considering the continuation/generalized minimum residual (C/GMRES) method optimization solver, which can reduce computational costs. Finally, numerical experiments on multiple driving scenarios were presented.

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Can automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks

Zhu

Han

Alhajyaseen

et al. 2022

Accident Analysis & Prevention

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Scenario-based stochastic MPC for vehicle speed control considering the interaction with pedestrians

Cited by 13 publications

References 11 publications

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

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

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

Can automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks

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