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The objective of this research is to monitor the vehicle motion against its external and internal constraints and remove out the existing safety risk, if any, based on the human-machine cooperative vehicle control. The external constraints mean the environmental entities imposing constraints on a vehicle motion. Such examples include the lane for the vehicle to follow as well as the nearby vehicles and pedestrians to avoid. A perception net based method is proposed to estimate upcoming lane and vehicle pose for automatic lane keeping. The road is modeled by three connected rectangular plates so as to give computational efficiency and robustness in real time sequence. Uncertainties in estimating plate pose parameters and measuring the lane are considered and propagated forward and backward through the perception net to reduce the uncertainties of the resulted plate pose parameters. The constraint condition that the lane has constant width and is always parallel to the plate, is imposed to the optimization problem in the perception net module to adjust plate pose parameters. As a result, vehicle pose and lane to follow under large noise as well as variants embedded in the street images will be estimated. An experimental result is presented, where a series of pictures of the real lane as well as synchronized information on vehicle movement are taken and the proposed method is applied to the real situation.