Introduction of the Benchmark Problem of Optimal Motion and Energy Control of a 4-in-Wheel-Motor Car

Yutaka Hirano (HIRANO Reseach Lab.)･Rui Gao (Modelon)･Junichi Kako (Toyota Motor)･Fuguo Xu (Chiba University)･Tielong Shen (Sophia University)

In order to promote industry-academia collaboration, JSAE Technical Division Committee of the Automotive Control and Model Research will set benchmark problems that apply the latest machine learning and control theory to automobile control. As one of them, an optimal control problem for the motion and energy of a four-wheel in-wheel motor vehicle was settled. The object to be controlled is an autonomous vehicle equipped with a four-wheel in-wheel motor and a steer-by-wire system. Tasks are set to start, accelerate, decelerate and stop on rough roads, and double lane change on rough roads. The vehicle model is a full vehicle model with a three-dimensional multi-body mechanism written in Modelica, and the body movement can be controlled by the suspension reaction force of braking/driving force. In two tasks, challengers are required to solve an optimal control problem that minimizes the power consumption of a four-wheel in-wheel motor while suppressing body vibration below a predetermined value. This benchmark problem has already been developed as the Autonomous Driving Control Benchmark Challenge at IEEE CDC2023, which was held in December 2023, and we will now start accepting applications in Japan. Challengers will be provided with a Modelica model, a Simulink model using its FMU, and a free license for the Modelica tool (Modelon Impact).

AI Formula

Masaya Okada･Yuki Akimoto･Atsushi Kato･Yuji Yasui (Honda R&D)

AI Formula is a technical challenge where robot car drives autonomously in mission course. In purpose, it can acquire the necessary technology for next-generation mobility research. thruough, autonomous racing competing speed and intelligence in the real world. Starting from 2025 in SICE and JSAE.

Optimization Problem of Mobility Service in Smart Satellite City
-SICE-JSAE-πMAP Benchmark Problem-

Yuji Yasui (Honda R&D)･Masakazu Mukai (Kogakuin University)･Yutaka Hirano (Hirano Research Lab)･Toshihiro Aono (Hitachi Astemo)･Yoshihiro Mizoguchi (Kyusyu University)･Wenjing Cao (Sophia University)･Shinkichi Kawai･Taisei Ito (Solize)･Chisa Kobayashi (Honda R&D)

SICE-JSAE Automotive Control and Modeling Committee and Post Advanced Innovation powered by Mathematics Platform have conducted benchmark problem activities in order to educate students and young engineers by providing research subjects and support their research activities. In this paper, the new benchmark problem of “Optimization Problem of Mobility Service in Smart Satellite City” will be introduced.

A Case Study of 'Mobility Service Optimization Problems using Intelligent Mobility'

Shinkichi Kawai･Taisei Ito･Shinji Minami･Nobuki Hiramine (Solize)･Yuji Yasui (Honda R&D)

As a case study of the "Mobility Service Optimization Problem using Intelligent Mobility" presented as a JSAE-SICE benchmark problem, we report a case in which we attempted to optimize the operation route of intelligent mobility in a satellite city according to revenue, operation cost, and customer satisfaction by using the "Smart Satellite City Simulator".

Maximizing Smart Mobility Service Revenues through Dynamic Programming (First Report)

Takehito Kobayashi･Wenjing Cao (Sophia University)

With the advance of automated driving technology and vehicle electrification, a wide variety of mobility services are being developed. Under these circumstances, service providers need to ensure sustainable operations while meeting customer needs. Therefore, it is important to analyze the relationship between pricing plans, utilization rates, and revenues using a smart satellite city simulator to develop optimal pricing and advertising strategies.

[Keynote Address] Logical Manifestation of Specifications, Requirements and Responsibilities
-Approaches from Software Science-

Ichiro Hasuo (NII/SOKENDAI)･James Haydon (NII)･Sota Sato (NII/SOKENDAI)･Clovis Eberhart (NII/JFLI)･Masaki Waga (Kyoto University/NII)･Zhenya Zhang (Kyushu University/NII)･Jeremy Dubut (AIST)･Naoki Ueda･Yosuke Yokoyama (Mitsubishi Electric)･Kenji Kamijo･Yoshiyuki Shinya･Takamasa Suetomi (Mazda)･Nayuta Yanagisawa (Toyota Motor)

An overview is given of two recent bodies of logical technologies. One is a toolsuite based on temporal logics; this contributes to the manifestation and exploitation of specifications and requirements all the way through design, production, quality management, and maintenance. The other is on logical proofs of safety of automated driving; this explicates each traffic participant's responsibility and thus contributes to a safe and accountable automated driving ecosystem.