International Journal of Automotive Engineering
Online ISSN : 2185-0992
Print ISSN : 2185-0984
ISSN-L : 2185-0992
Research Paper
Stochastic Model Predictive Obstacle Avoidance with Velocity Reduction in Accordance with Chance Constraints in Crowded Environments
Ryuichi MakiIsao OkawaKenichiro Nonaka
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JOURNAL OPEN ACCESS

2022 Volume 13 Issue 3 Pages 114-121

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Abstract

For obstacle avoidance against randomly moving traffic participants, stochastic model predictive control is promising. In crowded environments, however, feasible trajectories satisfying chance constraints do not necessarily exist; crowding induces a relaxation of constraints that causes deterioration of safety. To address this issue, we developed a velocity control method that decelerates the ego vehicle to a speed that satisfies the chance constraints on the prediction horizon. We conducted numerical simulations of obstacle avoidance and experiments of moving through a crowd comprising vehicles and pedestrians to evaluate the performance. The results indicate that the designed controller can generate a trajectory that mitigates the relaxation of constraints and adapts to various traffic participants.

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© 2022 Society of Automotive Engineers of Japan, Inc

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