Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1587/nolta.13.544

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Asian Journal of Control  

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.

DOI： 10.1002/asjc.2892

Web of Science

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC  

Self-driving electric vehicles are becoming popular, and the demand for safer driving systems is increasing. Electric vehicles equipped with in-wheel motors (EV-IWM) can independently control the torque generated by each of the four wheels. This strong feature makes them suitable for advanced collision avoidance in emergencies. To handle the many control outputs needed to control such vehicles, most conventional research works used rule-based and hierarchical controllers. However, such constraints for making the control easier are considered to degrade the performance. This study presents nonlinear model predictive control (NMPC) -based method that can consider the detailed vehicle dynamics without overall linear approximation to maximize the control performance. The effectiveness of our proposed scheme is demonstrated by simulating obstacle avoidance in the case of steering failure. The recently published proximal averaged Newton-type method for optimal control (PANOC) is used as an optimization solver that reduces the computation time and enables real-time control. The obstacle avoidance task is one example of the utilization of control redundancy. In the future, other tasks such as energy-efficient driving or improving riding comfort are expected to be realized with our proposed scheme.

DOI： 10.1109/ITSC55140.2022.9922126

Web of Science

Scopus

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/TASE.2020.3001060

Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.1103/PhysRevE.93.022220

Language：English   Publishing type：Research paper (scientific journal)  

Other Link： https://doi.org/10.9746/jcmsi.8.52

Language：English   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.9746/jcmsi.8.7

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.9746/sicetr.50.801

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： https://doi.org/10.9746/sicetr.48.872

Authorship：Lead author   Language：Japanese  

DOI： https://doi.org/10.9746/sicetr1965.34.1121

Event date： 2022.9

Language：English   Presentation type：Oral presentation (general)  

Venue：Kanagawa Institute of Technology, Kanagawa, Japan   Country：Japan  

Event date： 2022.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Applicant：株式会社デンソー

Application no：特願2020-70138  Date applied：2020.4

Announcement no：特開2020-175886  Date announced：2020.10

Rights holder：株式会社デンソー

Applicant：株式会社デンソー

Application no：特願2020-28410  Date applied：2020.2

Announcement no：特開2021-135509  Date announced：2021.9

Rights holder：株式会社デンソー

Applicant：株式会社デンソー

Application no：特願2020-28411  Date applied：2020.2

Announcement no：特開2021-133689  Date announced：2021.9

Applicant：株式会社デンソー

Application no：特願2020-24653  Date applied：2020.2

Announcement no：特開2021-127095  Date announced：2021.9

Applicant：株式会社デンソー

Application no：特願2019-196523  Date applied：2019.10

Announcement no：特開2021-71783  Date announced：2021.5

Applicant：株式会社デンソー

Application no：特願2019-81084  Date applied：2019.4

Announcement no：特開2020-175853  Date announced：2020.10

Patent/Registration no：特許7205366  Date registered：2023.1 

Rights holder：株式会社デンソー   Country of applicant：Domestic   Country of acquisition：Domestic

Applicant：株式会社デンソー

Application no：特願2019-6061  Date applied：2019.3

Announcement no：特開2020-161009  Date announced：2020.10

Patent/Registration no：特許7180497  Date registered：2022.11 

Rights holder：株式会社デンソー