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Electric-motor-driven vehicles have attractive performance attributes not only for environmental solutions but also for vehicle motion control. An electric motor offers significant advantages such as fast torque response, accurate estimation of torque output and the possibility of distributing motors independently in each wheel. These advantages effectively contribute to advanced motion control. In this study, two feedback controllers were constructed for skid prevention during vehicle acceleration. A model-following controller was used to consider the influence of back electromotive force and a slip ratio controller was used to consider the interaction between traction wheels.

This paper describes an image processing system for tracking a traffic lane by recognizing white lines on the road ahead. The system utilizes the features of the white lines and the Hough transformation to detect white line candidate points in images taken with a CCD camera. The parameters of the road configuration and vehicle attitude are estimated with an extended Kalman filter. This system has been applied to achieve a lane-keeping assistance system that provides steering control based on the host vehicle’s lateral position in its lane.

This paper describes an image processing method for recognizing white lines on the road ahead to accomplish automatic lane tracking. The behavior of detected points on the white lines can be regarded as multi-channel time-series data. The stochastic behavior of each point correlates with not only its own past behavior but also the behavior of other points around it. Behavior is identified by using a multi-variable auto-regressive (AR) model. This method can estimate the road configuration ahead logically even if the points cannot be detected clearly because a line is discontinuous or is hidden by a forward vehicle.