The adaptive cruise control (ACC) system as a typical advanced driver assistant system (ADAS) has been commercially application in automotive industry for decades. An innovative method is proposed in this paper for scene recognition and target tracking for ACC application in some complex traffic environment. Firstly, a multi-sensor fusion method is established to estimate the curvature integrated by the quadratic programing (QP)-based lane boundaries detection, vehicle dynamics of lateral motion, and an improved Kalman filter (IKF) to introduce more measurement information into the feedback correction process. Then, the closet in-path vehicle (CIPV) can be selected according to the statistical distance between the tracked targets and the predicted driving path of ego vehicle. To distinguish the lane changing and curve driving behaviors, the trajectory models of obstacles are established as an ellipsoid domain equation and transformed into a regression model, which is recast as a standardized QP problem. Hence, the behaviors and scenes can be recognized effectively. To restrain the disturbance and improve the accuracy and robustness of target tracking, an [Formula: see text]-based switched tracking method is proposed by combining of the low pass filter (LPF) and [Formula: see text] theory. Finally, an accurate and robust tracker is provided for the CIPV by incorporating with four steps: IKF-based curvature fusion estimation, CIPV selection, QP-based scene recognition, and the [Formula: see text]-based observer. Moreover, two real car experiments are adopted and the results verify the effectiveness and real-time performance of the proposed method.
For the purpose of track maintenance in a cluttered environment with some false alarms, false dismissals and measurement disturbances, this paper presents a new multi-object tracking procedure with uncertainty of the source of the measurement data returned from multi-sensor. The tracking method is a thrice deeply-fusion approach constructed by a primary fusion based on an improved probabilistic data association filter (IPDAF), a secondary fusion with historical motion trajectories, and a thrice fusion with road-markings. It incorporates the existence probabilities of the individual tracks and the variable number of objects based on the Bayesian estimation theory, which can improve the tracking performances effectively in an environment with high clutter density. A binary 2-D assignment is adopted for the optimal data association, which is established as a nonlinear optimization problem. In the motion modeling, it introduces multiple measurement models for different sensors into the method. Then, the estimation could be performed with greater reliability. The computational efficiency is satisfying and it can be used for real-time application, which is verified by two real test scenarios.
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