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This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

This paper presents an advanced yaw stability control system that uses a sensor set including an inertial measurement unit to sense the 6 degrees-of-freedom motions of a vehicle. The full degree of the inertial measurement unit improves and enhances the vehicle motion state estimation over the one in the traditional electronic stability controls. The addition of vehicle state estimation leads to the performance refinement of vehicle stability control that can improve performance in certain situations. The paper provides both detailed system description and test results showing the effectiveness of the system.

This paper proposes a low-cost but indirect method for occupancy detection and occupant counting purpose in current and future automotive systems. It can serve as either a way to determine the number of occupants riding inside a car or a way to complement the other devices in determining the occupancy. The proposed method is useful for various mobility applications including car rental, fleet management, taxi, car sharing, occupancy in autonomous vehicles, etc. It utilizes existing on-board motion sensor measurements, such as those used in the vehicle stability control function, together with door open and closed status. The vehicle’s motion signature in response to an occupant’s boarding and alighting is first extracted from the motion sensors that measure the responses of the vehicle body. Then the weights of the occupants are estimated by fitting the vehicle responses with a transient vehicle dynamics model.

This paper presents two brake control functions which are initiated when there is an impact force applied to a host vehicle. The impact force is generated due to the host vehicle being collided with or by another vehicle or object. The first function - called the post-impact braking assist - initiates emergency brake assistance if the driver is braking during or right after the collision. The second function - called the post-impact braking - initiates autonomous braking up to the level of the anti-lock-brake system if the driver is not braking during or right after the collision. Both functions intend to enhance the current driver assistance features such as emergency brake assistance, electronic stability control, anti-brake-lock system, collision mitigation system, etc.

This paper proposes an approach to determine driver's driving behavior, style or habit during vehicle handling maneuvers and heavy traction and braking events in real-time. It utilizes intelligence inferred from driver's control inputs, vehicle dynamics states, measured signals, and variables processed inside existing control modules such as those of anti-lock braking, traction control, and electronic stability control systems. The algorithm developed for the proposed approach has been experimentally validated and shows the effectiveness in characterizing driver's handling behavior. Such driver behavior can be used for personalizing vehicle electronic controls, driver assistant and active safety systems, and the other vehicle control features.