Shun Taguchi

Identification of Probability weighted multiple ARX models and its application to behavior analysis

This paper proposes a Probability weighted ARX (PrARX) model wherein the multiple ARX models are composed by the probabilistic weighting functions. As the probabilistic weighting function, a ‘softmax’ function is introduced. Then, the parameter estimation problem for the proposed model is formulated as a single optimization problem. Furthermore, the identified PrARX model can be easily transformed to the corresponding PWARX model with complete partitions between regions. Finally, the proposed model is applied to the modeling of the driving behavior, and the usefulness of the model is verified and discussed.

Efficient vehicle driving on multi-lane roads using model predictive control under a connected vehicle environment

Anticipative control of vehicles is a potential approach for improving travel efficiency of individual vehicles, smoothing traffic flows on urban roads, alleviating impacts on the environment and elevating comforts of the users in various respects. This paper presents such a vehicle driving system in a model predictive control (MPC) framework to efficiently drive a vehicle on multi-lane roads. Anticipation enhances the driving intelligence and strengthens the vehicles ability in taking advance action, e.g., lane change, speed adjustment, in a dynamically varying traffic environment. More elaborately, presuming a connected vehicle environment, the system receives the information form the surrounding vehicles and infrastructure instantly through V2X communication systems and, using dynamical models, predicts the future road-traffic states. Considering relevant constraints and a performance index, the system generates the optimal acceleration and executes lane change maneuver optimally if long term advantages are anticipated. Numerical simulation in realistic traffic flow conditions reveals that the vehicles with the proposed driving system improve their travel efficiency significantly.

Model predictive assisting control of vehicle following task based on driver model

A personalized driver assisting system that makes use of the drivers behavior model is developed. As a model of driving behavior, the Probability-weighted ARX (PrARX) model, a type of hybrid dynamical system models, is introduced. A PrARX model that describes the drivers vehicle-following skill on expressways is identified using a simple gradient descent algorithm from actual driving data collected on a driving simulator. The obtained PrARX model describes the drivers logical decision making as well as continuous maneuver in a uniform manner. Finally, the optimization of the braking assist is formulated as a mixed-integer linear programming (MILP) problem using the identified driver model, and computed online in the model predictive control framework.

Efficient Driving on Multilane Roads Under a Connected Vehicle Environment

Traffic anticipation enhances driving intelligence and strengthens the ability to take early vehicle control action, e.g., lane change and speed adjustment, in a dynamically varying traffic environment. This paper presents an efficient vehicle driving system, based on detailed anticipation of surrounding traffic, that aims at optimizing the driving performance of individual vehicles and smoothening traffic flows on multilane roads. More elaborately, under a connected vehicle environment, the system receives the states of all vehicles that exist within its communication range. Based on their predicted states in a look forward horizon, the system generates the optimal acceleration and makes lane change decision simultaneously in the model predictive control framework. A fast hierarchical optimization scheme is used in the framework for its onboard implementation. The proposed efficient driving system is applied to a fraction of traffic, and both the individual and overall traffic performances are evaluated using a microscopic traffic simulator. It is revealed that the vehicles under the proposed efficient driving system improve their fuel economy and travel efficiency, significantly. In the mixed traffic, by the influence of the vehicle with the proposed driving system, the other traditionally driven vehicles also improve their performance.

Intersection Vehicle Cooperative Eco-Driving in the Context of Partially Connected Vehicle Environment

Vehicles with communication functionality are appearing on the roads and transition towards a fully connected vehicle environment will be gradual. Infra-vehicle communication can play a major role in promoting traffic performance in a partially connected vehicle environment. This paper addresses such a real traffic context to present a vehicle control system for eco-driving based on intersection vehicle cooperation. More specifically, the proposed system measures the state of the preceding vehicle by own sensors, and receives information from upcoming intersection signal that exists within the communication range. Next, based on the predicted behavior of the preceding vehicle in a look forward horizon and traffic signal timing, the optimal acceleration of the vehicle is generated in a model predictive control framework. The velocity of the vehicle is dynamically tuned to reduce or avoid idling in red signals either by speeding up or slowing down early, considering constraint imposed by any unconnected preceding vehicles. The proposed eco-driving system is evaluated through microscopic simulation.

COMS-VR: Mobile virtual reality entertainment system using electric car and head-mounted display

We propose a novel virtual reality entertainment system using a car as a motion platform. Motion platforms present a sensation of motion to the user using powerful actuators. Combined with virtual reality content, including surrounding visual, auditory and tactile displays, such systems can provide and immersive experience. However, the space and cost requirements for installation of motion platforms are large. To overcome this issue, we propose to use a car as a motion platform. We developed a prototype system composed of a head mounted display, a one-person electric car and an automatic driving algorithm. We developed and tested immersive content in which users ride on a trolley in a virtual space. All users responded quite positively to the experience.