Shigeyuki Yamabe

Study on Emergency-Avoidance Braking for the Automatic Platooning of Trucks

In developing automatic platooning of trucks as an energy-saving technology, the reliable driving of the platooned trucks is a primary objective for public implementation and future applications. At the same time, there is also an emergency requirement to ensure the safety of the driving experiment in the automatic platooning of trucks, including the conditions of a system failure. This paper presents a detailed experimental study on emergency avoidance braking for the automatic platooning of trucks using a driving simulator (DS) and an actual-vehicle experiment. In addition, a modification on the braking capability of the trucks of a platoon was applied for safety control. Therefore, human drivers can brake without risking a rear-end collision, in the case of an emergency for a failure in automatic platooning. Initially, an experimental platform was built to reproduce the automatic platooning of trucks in an advanced DS system. Assuming system failure and the emergency deceleration of the preceding truck without warning, the behavior of the driver in the following truck was studied in terms of emergency avoidance of a collision. In particular, with different settings for the mean maximum decelerations of the brake system of the following truck, the stopping gap distances and driver reaction times were analyzed in the driving experiment using the advanced DS and an actual vehicle. The experimental results indicated that emergency braking is an effective method for avoiding a rear-end collision when there is a system failure in the automatic platooning, resulting in the mean maximum deceleration for the following truck being higher than that for the preceding truck.

Biosignal Analysis to Assess Mental Stress in Automatic Driving of Trucks: Palmar Perspiration and Masseter Electromyography

Nowadays insight into human-machine interaction is a critical topic with the large-scale development of intelligent vehicles. Biosignal analysis can provide a deeper understanding of driver behaviors that may indicate rationally practical use of the automatic technology. Therefore, this study concentrates on biosignal analysis to quantitatively evaluate mental stress of drivers during automatic driving of trucks, with vehicles set at a closed gap distance apart to reduce air resistance to save energy consumption. By application of two wearable sensor systems, a continuous measurement was realized for palmar perspiration and masseter electromyography, and a biosignal processing method was proposed to assess mental stress levels. In a driving simulator experiment, ten participants completed automatic driving with 4, 8, and 12 m gap distances from the preceding vehicle, and manual driving with about 25 m gap distance as a reference. It was found that mental stress significantly increased when the gap distances decreased, and an abrupt increase in mental stress of drivers was also observed accompanying a sudden change of the gap distance during automatic driving, which corresponded to significantly higher ride discomfort according to subjective reports.

Driving skill analysis using machine learning The full curve and curve segmented cases

Analysis of driving skill/driver state can be used in building driver support and infotainment systems that can be adapted to individual needs of a driver. In this paper we present a machine learning approach to analyzing driving maneuver skills of a driver that covers both longitudinal and lateral controls. The concept is to learn a driver model from sensor data that are related to driving environment, driving behavior and vehicle response. Once the model is built, driving skills of an unknown run can be classified automatically. In this paper, we demonstrate the feasibility of the proposed method for driving skill analysis based on a driving simulator experiment in a curve driving scene for both the full curve and curve segmented cases.

Estimation of Road Surface Conditions From Tire Vibration

This paper proposes the estimation algorithm of road conditions from tire vibration. The basic concept of estimation is that the tire vibration is determined by road irregularity and road friction condition. Because of difficulty of direct measurement of tire vibration, it is estimated from body acceleration using known tire model and vehicle properties between tire and body. To estimate the road irregularity, the Independent Component Analysis (ICA) method is used. The elastic deformation of the tire belt is modeled using the finite element absolute nodal coordinate formulation which allows for modeling large rotational motion and the nonlinear inertia effects. With comparison between estimated tire vibration from road irregularity with ICA and tire model and actual tire vibration from road irregularity and road surface condition with known vehicle properties, road surface condition could be estimated. So, tire vibration power spectral density (PSD) calculated by using the transmission characteristics becomes the function of the road surface, the road ruggedness, and the tire characteristic. These are clarified by the PSD ratio, ICA and the tire model, and road surface condition become able to estimate.Copyright

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.

Driver Risk Perception and Physiological State During Car-Following Experiments Using a Driving Simulator

For passengers to become comfortable with automatic driving, it is important to clarify when they sense danger. For the sake of safety and reproducibility of dangerous situations, experiments using a driving simulator are preferred to experiments in actual traffic. However, drivers’ sense of danger in a driving simulator has not been well studied. We conducted two experiments using a driving simulator to examine drivers’ sense of danger. In Experiment 1, drivers followed a car. We measured driver perspiration when the forward car suddenly decelerated. Experiment 2 was a simulation of two platooning trucks. We observed the perspiration of the driver in the following truck. Results indicated a correlation between inverse Time-To-Collision and perspiration rate change. We analyzed the correlation between perspiration and car motion variables for practical application to automatic driving.

Safety Testing of an Improved Brake System for Automatic Platooning of Trucks

An automatic platooning technology for energy-saving logistics utilizing intelligent transportation systems (ITS) technologies have been developed in the Energy-Saving ITS Project of Japan. The automatic platooning of trucks is studied to save energy consumption by reducing air resistance. Considering the experimental testing and practical application for automatic platooning, it is important to ensure driver safety, and high levels of safety and efficacy are required for truck braking systems. Therefore, a new brake system for automatic platooning needs to be developed. In this study, two approaches are proposed. One is to design a secondary brake system working in a fail situation with the main brake system, the other is to adjust for different maximum decelerations for the preceding and following trucks. In the first step, the proposed measure was investigated using an actual vehicle and a simulation experiment. Based on this, the proposed brake system was developed and mounted on the experimental trucks, and full brake testing was carried out to evaluate its effectiveness. The experimental result showed that the improved brake system was effective in ensuring brake safety by incorporating these functions in the trucks.

Dominant Driving Operations in Curve Sections Differentiating Skilled and Unskilled Drivers

Our objective is to develop a new driving assist system that can help low-skilled drivers improve their driving skill. In this paper, we describe a statistical method we have developed to extract distinctions between high- and low-skilled drivers. There are three key contributions. The first is the introduction of wavelet transform to analyze the frequency character of driver operations. The second is a feature extraction technology based on AdaBoost, which selects a small number of critical operation features between high- and low-skilled drivers. The third is a simple definition for high- and low-skilled drivers. We performed a series of experiments using a driving simulator on a specially designed course including several curves and then used the proposed method to extract driving operation features showing the difference between the two groups.

Learning characteristic driving operations in curve sections that reflect drivers' skill levels

Our main objective was to develop a new driving assistance system that could help less experienced drivers improve their driving skills. We describe a statistical method we developed to extract distinctions between experienced and less experienced drivers. This paper makes three key contributions. The first involves a technology for feature extraction based on AdaBoost, which selects a small number of features critical for operation between experienced and less experienced drivers. The second involves a simple definition for experienced and less experienced drivers. The third involves the introduction of wavelet transforms that were used to analyze the frequency characteristics of driver operations. We performed a series of experiments using a driving simulator on a specially designed course that included several curves and then used the proposed method to extract features of driving operations that demonstrated the differences between the two groups.

Parameter Identification of a Vehicle for Automatic Platooning Control

Automatic platooning control of heavy-duty trucks requires accurate vehicle parameters because control performance is significantly affected by the load. We propose new identification methods for the identification of parameters such as vehicle mass and center of gravity using experiments with a real vehicle. The validity of these methods was confirmed with high accuracy comparisons between measured and estimated results.