This paper proposes a practical probabilistic approach to collision decision making which is necessary for advanced automotive collision warning system (CWS) using FMCW radar. Most decision making algorithms assess the probable collisions based on the predicted collision position which is usually expressed as a nonlinear function of threat vehicle's position and velocity provided by FMCW radar. Since the predicted collision position has highly nonlinear statistics in general, it is one of main obstacles to improving the reliability of the collision probability computation and to developing real-time decision making algorithms. This motivates us to devise a Gaussian mixture method for collision probability calculation with the help of linear recursive time-to-collision (TTC) estimation. The suggested TTC estimator provides an accurate TTC estimate with small estimation error variance hence it enables us to approximate the probability density function of the predicted collision position as the weighted sum of just a few Gaussian distributions. Therefore, our approach could drastically reduce the inherent nonlinearity of collision decision making problem and computational complexity in collision probability calculation. Through the simulations for the typical engagement scenarios between the host and threat vehicles, the performance and effectiveness of the proposed algorithm is compared to those of the existing ones which require heavy computational burden.

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This paper proposes an improved automotive target tracking scheme using FMCW radar which is necessary for the advanced collision warning systems. Since there exist strong nonlinear relationships between the FMCW radar measurements and the target state, the target tracking and data association in dense road clutters have been recognized as a quite challenging problem. It is obvious that the use of accurate range rate measurement might be an excellent choice to improve both target tracking and clutter suppression performances. This motivates us to develop a novel linear recursive automotive target tracking filter based on the measurement conversion in the predicted line-of-sight (LOS) Cartesian coordinate system (PLCCS). Since the x axis of the PLCCS is set by the predicted LOS vector from the host to the target, if the LOS prediction error is imperceptible, the range rate can be approximated to the x component of the relative target velocity in PLCCS. Employing the PLCCS drastically reduces the complexity of the problem and allows us to solve it within the framework of linear recursive Kalman filtering. Through the simulations, the superiority of the proposed method is compared to the existing nonlinear automotive target tracking filters.

A target localisation estimator based on time difference of arrival (TDOA) measurement is proposed. The localisation estimator is designed in the framework of the recently developed robust least-squares (RoLS) estimator, which provides an unbiased estimation result and can be implemented with a recursive filtering structure. However, when the RoLS estimator is applied to the localisation problem, its localisation performance depends on the knowledge of the stochastic information of the TDOA measurement. This dependency means that incorrectly given information causes localisation error. Therefore to complement the dependency of the given stochastic information of the TDOA measurement, we design a compensation procedure based on the constraints on the state variables of the estimator. The performance of the proposition under several cases of incorrectly given stochastic information is verified through computer simulation, and its filtering structure is compared with other existing localisation algorithms mathematically. In addition, the entire process of the proposed localisation estimator is derived as a recursive form for real-time applications.

A robust pinch detection algorithm which can be implemented in a cheap microprocessor is proposed for the development of a safety feature in the automotive power window system. To solve the problems caused by the performance degradation of a Hall sensor or real driving situations, the proposed algorithm makes use of the H ∞ state estimation technique. The motivation of this approach comes from the advantage that the H ∞ filter can minimize or bound the worst-case estimation error energy for all bounded energy disturbances. Herein, the pinch torque rate estimator is derived from applying the steady-state H ∞ filter to the augmented model, which includes the motor dynamics and an additional torque rate state. Then, to redesign an appropriate estimator for real-time implementation, the torque rate estimate can be calculated more efficiently than the previous method [1]. Experimental results verify that, with a small amount of computation, the proposed pinch detection algorithm provides fast pinch detection performance superior to the existing method. Furthermore, it guarantees robustness against the worst-case measurement noises.