Stochastic Model Predictive Velocity Control for Automobiles Considering Uncertainty of Nearby Vehicles. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Hashimoto, Kentaro; Mizushima, Yoshihide; Shibata, Koji; Okawa, Isao; Nonaka, Kenichiro
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 15596
• Page End: 15601
• Publication Date: 2020
• Subjects: Self-driving vehicle -- Automobile -- Stochastic model predictive control -- Velocity control -- Kalman filter -- Relative velocity -- Expectation -- Risk prediction; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.2493
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, we present vehicle velocity control based on stochastic model predictive control applied to an actual automobile near other vehicles with uncertain motion. Modern external sensors can measure the pose and velocity of transportation participants, whose motion can be predicted utilizing a model; however, observation and process noise results in uncertainty. Thus, near other vehicles, the velocity of the ego vehicle should be reduced to account for the variance of nearby vehicle motion while suppressing the reduction of speed. In this paper, we utilize stochastic model predictive control to reduce the expectation of relative velocity with respect to nearby vehicles during passing. We evaluate the proposed control using numerical simulation and an experiment with an automobile developed for self-driving and equipped with GNSS, radar, and LiDAR. The results show that the vehicle velocity is automatically reduced based on the expectation of relative velocity calculated from its probabilistic distribution.