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In recent decades, research and development in the field of autonomous vehicles have rapidly increased throughout the world, and autonomous driving technologies have begun to be applied to mass production vehicles. Especially recently, even affordable mass production vehicles have begun to be equipped with some autonomous driving systems such as a Lane Keeping Assist (LKA) system. In general, mass-produced LKA systems use a lane detection camera as a means of keeping the lane. One of the common limitations of camera-based LKA systems is that the lane keeping performance significantly decreases when the camera cannot detect lane markings for various reasons such as snow coverage or blurred lane markings. To overcome this limitation, we have developed Global Navigation Satellite System (GNSS)-based LKA systems, which are not affected by the surrounding environment such as weather and the condition of lane markings.

Recently, development of vehicle control system targeting Full Driving Automation (autonomous driving level 5) has advanced. Some applications of autonomous driving systems like the Lane Keeping Assist system (LKA) and Auto Lane Change system (ALC) (autonomous driving level 1-3) have been put on the market. However, the conventional system using information from front camera, it is difficult to operate in some situations. For example the road that no line, large curvature and number of lane increases or decreases. We propose an autonomous driving system using high accuracy vehicle position estimation technology and a high definition map. An LKA system calculates the target steering wheel angle based on both vehicle position information from the Global Navigation Satellite System (GNSS) and the target lane of high the definition map, according to the method of front gaze driver model. Then, the system controls steering the wheel angle by Electric Power Steering (EPS).

Mitsubishi Electric has been developing a lane keeping assist system (LKAS). This system consists of our products such as an electric power steering (EPS), a camera, and an electronic control unit (ECU) for ADAS. In this system, the camera detects a lane marker, the ECU estimates reference path and vehicle position, and calculates reference steering wheel angle, and the EPS controls a steering wheel angle based on reference steering wheel angle. In this paper, we explain the calculation method of reference steering wheel angle for path tracking control. We derive a formula of reference steering wheel angle calculation that converges lateral position deviation in desired time by using lateral position deviation change rate control on forward gaze point as path tracking control algorithm. Since the formula is obtained from the vehicle model, we can easily design a controller depending on the vehicle type, by using known vehicle specifications.

In this paper, we suggest a novel algorithm to distinguish semi-steady states from various steering patterns and to estimate the stability factor. The algorithm also estimates each stability factor in left and right turns because there could be a case where they differ based on uneven tire wear and so on. The stability factor, which is the turning characteristic of a vehicle, has been treated as constant for most vehicle control systems. However, in fact, it may change in some situations, for example when a vehicle is overloaded. So there is a chance that a driver may be aware of an unusual sensation when vehicle control is designed based on a constant stability factor. We have succeeded in developing an algorithm to estimate the stability factor accurately enough to be able to compensate for it and have confirmed the effectiveness of the algorithm by simulation and vehicle testing as well.

This article discusses a vehicle stability improvement control method that utilizes an electric power steering system (EPS) with blushless motor. The purpose is to improve the vehicle stability by increasing the steering return torque in a region where the alignment torque is saturated due to the driver's excessive steering maneuver on a slippery road. In this study, a factor analysis was performed for the alignment torque on a slippery road and the basic control to improve the vehicle dynamics stability is studied by using a linear m1odel. Next, a new control algorithm was developed based on these studies. Finally, the new control algorithm was verified to be effective through an on-vehicle test. The proposed strategy can be realized only by adding a steering wheel angle sensor signal to a conventional EPS. That can be easily obtained from electronic stability control system.

This paper proposes a vehicle state detection method for improving the stability of vehicles equipped with electric power steering (EPS) and electronic stability control (ESC) systems. ESC is an effective vehicle stability control system that operates within a vehicle's stability limitations. Generally ESC uses a vehicle state signal such as yaw rate. To enhance the ESC function so that it can alleviate understeer, a process that is capable of detecting understeer is required. This concept motivated us to develop a vehicle state detection algorithm based on estimated self-aligning torque using EPS. It is well known that maximum self-aligning torque occurs before maximum cornering force is reached. We have confirmed that the proposed algorithm can detect understeer earlier than conventional means based on vehicle yaw rate.

We have developed a driving simulator for Electric Power Steering (EPS), which can be used to evaluate steering maneuverability on low μ roads. The simulator calculates an 11 DOF (degrees of freedom) vehicle motion based on the steering wheel angle, the accelerator pedal position and the brake pedal position which are operated by the driver. A reaction torque corresponding to the alignment torque is applied to the steering shaft using motors. A 3D CG reproducing the view from the cockpit is displayed on a forward screen. The simulator also includes column type EPS, which generates the assist torque. Consequently, the driver feels the steering torque with good reality. The tire model we used is non-linear and it enables us to simulate the vehicle dynamics also on slippery roads. We compared driver behavior in vehicle and simulator tests and found the simulator could evaluate the relationship between steering maneuverability and EPS control strategy even when the road was slippery.

This paper proposes a new Electric Power Steering (EPS) control strategy that improves steering maneuverability especially on slippery roads. In a conventional steering system (including mechanical and hydraulic ones), poor steering wheel returnability associated with reduced alignment torque from the road may lead to awkward handling on slippery roads. In experiments with a test driver, we found that this phenomenon occurs because of the delay in the driver turning the steering wheel to avoid spinning the vehicle. This delay comes from a lower steering wheel returnability than driver expected. Increasing the steering wheel returnability will be effective in avoiding this problem. This can be realized by using the steering angle feedback or the estimated alignment torque feedback. However, the simple feedback of such values will provide drivers with poor road information when the road is slippery.

This paper presents a new motor current control strategy for Electric Power Steering (EPS) to reduce current fluctuation. Such current fluctuation may cause undesirable steering torque ripple and acoustic noise, if an inexpensive microprocessor is used. Using a DC-motor, current fluctuation associated with change in the battery voltage, etc., may occur. We have developed a new current control strategy which effectively alleviates current fluctuations of the motor without using higher performance microprocessors. The new controller is based on the estimation of disturbance voltage and compensation for this disturbance voltage. We have bench-tested the performance of this control strategy and confirmed that current fluctuation is reduced below that using conventional PI controller. The PI gain for the proposed controller is the same as that for the conventional controller.

This paper proposes a new Electric Power Steering (EPS) control strategy that enables improvement to steering-wheel returnability. Using a conventional EPS controller, frictional loss torque in the steering mechanism reduces steering-wheel returnability, which drivers occasionally perceive as unpleasant. This phenomena occurs in any EPS system regardless of motor type or mounting location. To improve steering-wheel returnability for EPS-equipped vehicles, we developed a new control strategy based on estimation of alignment torque generated by tires and road surfaces. This proposed control strategy requires no supplemental sensors like steering-wheel angle or motor-angle sensors. We experimented with this proposed control algorithm using a test vehicle and confirmed that it enables improved steering wheel returnability and also better on-center feeling.

We have developed a new series of revolution sensors using Giant Magnetoresistive (GMR) elements. We call these GMR revolution sensors. In automotive applications, revolution sensors have traditionally utilized Hall effect and anisotropic magnetoresistive (AMR) elements. Recently, more sensitive revolution sensors are necessary for improved control of engines, braking systems and automatic transmissions. Since GMR elements have one order higher MR ratio than AMR elements, GMR revolution sensors are much more sensitive. Furthermore, GMR elements have been integrated with circuits on Si substrates. This integration simplify the assembly process and increases the reliability of the GMR revolution sensors. This paper discusses the superiority of GMR sensing elements over Hall and AMR elements. This paper also reports the characteristic results of the GMR sensor.

This paper proposes a new Electric Power Steering (EPS) control strategy that enables remarkable progress on steering maneuverability for stationary vehicles. Using a conventional controller, undesirable steering vibration prevented us from reducing steering torque. To eliminate this vibration, we developed a new control strategy based on damping for specified frequency using a motor angular-velocity estimator. We experimented with this proposed control algorithm using a test vehicle and confirmed that it enables reduced steering torque without any perceived vibration for drivers. Concerning the gradient of the assist-map, the proposed control strategy enabled more than three times higher compared with that of the same type vehicles on the market as the test vehicle. This proposed control strategy requires only the torque sensor signal, supply voltage and current to the motor, which are used in the conventional EPS systems, so no supplemental sensors are required.