Takeki Ogitsu

Automatic Driving Control for Passing through Intersection without Stopping

A control method for passing through an intersection for automated vehicles is proposed. If automated vehicles approaching an intersection communicate with each other and schedule the time of entering the intersection by small deceleration and acceleration, the vehicles can pass the through the intersection without stopping thereby enabling energy-saving by reducing unnecessary deceleration. This study proposes a method of categorizing intersection patterns, and extracting the required conditions for the realization of a non-stop intersection for each intersection pattern, performing control to realize such an intersection and transmitting information to vehicles entering the intersection for the creation of a virtual platoon. The proposed method is validated by experiments at an intersection of one-way traffic in single lanes.

Control Procedures and Exchanged Information for Cooperative Adaptive Cruise Control of Heavy-Duty Vehicles Using Broadcast Inter-Vehicle Communication

The Cooperative Adaptive Cruise Control system (CACC) is an enhancement of the Adaptive Cruise Control system (ACC) that uses inter-vehicle communication to realize safe cruising at shorter inter-vehicle distance. To effectively communicate via broadcasting, a vehicle must identify the ID of the preceding vehicle, and extract that vehicle’s information from the information sent by surrounding vehicles. In addition, the vehicle should ideally be able to reference information from other members of the platoon. In this manner, the system can evolve towards platooning with a very short inter-vehicle distance. On the other hand, if CACC is to acquire the same flexibility as ACC, the vehicle should handle common driving maneuvers such as lane-change of the preceding vehicle, cut-in of vehicles, and change of control mode of the preceding vehicle. Given the above requirements, this paper proposes control procedures and inter-vehicle communication schemes for implementing CACC of heavy-duty vehicles developed as part of the NEDO’s Development of Energy-Saving ITS Technologies project.

Development of Stewart platform type ankle-foot device for trip prevention support

This paper presents an ankle-foot device using a Stewart platform, which is a type of parallel-link mechanism, for trip prevention support. The developed device can reproduce the input motions of the ankle joint in six degrees of freedom by controlling six pneumatic cylinders at the same time. The root mean square errors of the 3-D position and rotation angle of the reproduced motions with the input motions (dorsiflexion and plantar flexion) were 6.3 mm and 3.0°, respectively. Verification experiments for trip prevention support performance were conducted by comparing motions in each walking condition measured using a motion capture system. The experimental results showed that the minimum foot clearance during mid-swing and initial swing increased significantly by the trip prevention support offered by the developed device. The developed device can perform passive exercises for ankle rehabilitation and support walking for trip prevention.

Spacing control of cooperative adaptive cruise control for heavy-duty vehicles

Abstract This study proposes a spacing control algorithm for the Adaptive Cruise Control system (ACC) using inter-vehicle communication. An important consideration in ACC controller design is string stability, which should be guaranteed in the control algorithm; that is, the amplitudes of the spacing vibration should not become amplified as they propagate upstream from vehicle to vehicle. When the controller uses inter-vehicle distance and relative velocity information, heavy-duty vehicles may lose string stability under short time-headway control on account of their slow-response acceleration characteristics. For example, the limiting headway time of a heavy-duty vehicle (with time constant of acceleration response 0.5 s) is 1 s. Under limiting conditions, spacing vibrations and acceleration vibrations are neither amplified nor attenuated. As an energy saving technique, our algorithm effectively attenuates acceleration vibrations under the limiting conditions of ACC by spacing control using information obtained by inter-vehicle communication. The proposed controller is validated by simulations.

State Observation and Communication for Cloud Vehicle Control

In this study, a new method was developed to automate vehicles. Numerous vehicle control systems have been realized for electronic steering, driving, and braking. Higher-standard vehicle control systems have increased costs. This study focused on cloud vehicle control using roadside sensors and computers. If a high-standard vehicle control system can be realized by using vehicles with no sensors or computers, this can create a new service model for transport. The key point was to obtain state quantities of vehicles and to maintain vehicle-to-roadside communication. The proposed method obtains state quantities of vehicles with roadside sensors by using the iterative closest point algorithm. The vehicle positions are correlated with communication information. The control quantities of the vehicles are received by roadside computers. Experiments using electric vehicles showed that the proposed method can realize high-standard vehicle control for self-driving vehicles.

3D Point Cloud-Based Virtual Environment for Safe Testing of Robot Control Programs: Measurement Range Expansion through Linking of Multiple Kinect v2 Sensors

Virtual environments are required for safe testing of robot control programs. However, it is important that the virtual environment of the robot simulator adequately reproduces the color and shape information of a real environment. In this study, we use point cloud data obtained from multiple Kinect v2 sensors for building a virtual environment. The coordinate systems of the point clouds obtained from the sensors are unified using the ICP algorithm. Then, a 3D model of the virtual environment consisting of color and shape information of the real environment is generated using Kinect Fusion. Finally, based on the 3D model, the virtual environment is built using the robot simulator Open HRP3. Experiments are conducted to compare the measurement range of the virtual environment that has been reproduced using a single Kinect v2 sensor and four Kinect v2 sensors. Moreover, we calculate the correlation coefficients of the images obtained by a camera sensor in the virtual environment and those obtained by a camera sensor in the real environment. The results show that the measurement range is expanded, and the images of the virtual environment and the real environment exhibit high similarity.

Real-time eyelid open/closed state recognition based on HLAC towards driver drowsiness detection

This paper proposes a real-time eyelid state recognition method based on a video sequence. The human eye strongly reflects the mental state of an individual, such as attention, drowsiness, stress and confusion. In recent times, the automatic identification of such mental states using non-contact eyelid state recognition technology is proving to be a promising avenue for the development of such systems. In the field of Intelligent Transport Systems (ITS), high accuracy and real-time processing are necessary for detecting driver drowsiness in order to prevent accidents. To develop such a recognition method, we use Higher-order Local Auto-Correlation (HLAC). HLAC can represent the shape feature in images clearly without incurring a high computational cost, and it implements position invariability. A Support Vector Machine (SVM) is used to differentiate between open and closed eyelids on the basis of the HLAC feature. The proposed method can achieve open and closed eyelid recognition rates of 98.77% and 98.98%, respectively. We also verify the real-time processing capabilities of our method, thus confirming that it is effective for eyelid state recognition.

Human-machine interface system for simulation-based automatic platooning of trucks

Human factors in intelligent transportation systems is an important topic for the application of automatic platooning technology, since drivers behaviors play a critical role in the safety of the normal driving as well as in an emergent situation during automatic platooning. In our research group, a novel truck-driving-simulator for the automatic platooning of trucks was constructed for the evaluation of the drivers behaviors in the different driving conditions. In virtue of the truck-driving-simulator, three types of human-machine interface (HMI) systems, including numerics, graphics, and numerics and graphics, were utilized in the driving experiment of the automatic platooning, and the three types of HMI systems were evaluated by 10 professional truck drivers cooperated the driving experiment.

Recovery function for human following robot losing target

This paper presents following continuation function for human following robot losing target at corner. To realize such function, the authors propose a human trajectory model of turning the corner. Using the proposed model, the robot can predict trajectory of the target human when he/she turns the corner and disappears from the robots view. To move on the target trajectory, the robot can rediscovery the target and continue to follow. In order to confirm effectiveness of the proposed function and the following robot using it, the authors conduct experiment. In the experiment, the robot can continue human following. Experimental result proves the effectiveness and supports our idea successfully.

Distributed Driving System for Coupled Small EV Using Neural Network and Load Cell

Japan is facing difficulties with providing general public transport systems in depopulated cities. This paper proposes a new means of transport for application to such cities. The system is based on small electric vehicles with mechanical couplers that are designed to attach to devices installed at each home. Unfortunately, this system incurs an issue of enhancing the operability for drivers of the coupled EVs. Therefore, this study set out to develop a distributed driving system that ensures that a group of vehicles maintains the same driving characteristics as those of a single vehicle, regardless of the number of coupled vehicles. This system suppresses fluctuations in the driving characteristics by means of online learning by the vehicle model using a neural network and by feedback control using a force gauge. This paper describes the details of this system and evaluation experiments using two small EVs.