A method for calibrating vehicle anti-collision parameters, characterized by comprising the following steps: setting a standard value of the latest time to activate braking TTAB and a standard value of the latest time to activate steering TTAS of a present vehicle; determining a calibrated value of TTAB and a calibrated value of TTAS, wherein the calibrated value is associated with at least one of the following attributes: a vehicle kinematics attribute, a vehicle user operation attribute, an object ahead kinematics attribute, and a road ahead attribute; and combining the standard value of TTAB and the standard value of TTAS with respective corresponding calibrated values to determine, for a current user, a corrected value of TTAB and a corrected value of TTAS, characterized in that, the calibrated value of TTAB and the calibrated value of TTAS are determined by utilising a deep learning method, a model of the deep learning method comprises: an input layer, wherein the input layer comprises a plurality of input nodes (A1, A2, A3, A4), and each of the input nodes is associated with at least one of the following attributes: a vehicle kinematics attribute, a vehicle user operation attribute, an object ahead kinematics attribute, and a road ahead attribute; a hidden layer, and an output layer, wherein the output layer comprises an output node about the calibrated value of TTAB and an output node about the calibrated value of TTAS. setting a standard value of the latest time to activate braking TTAB and a standard value of the latest time to activate steering TTAS of a present vehicle; determining a calibrated value of TTAB and a calibrated value of TTAS, wherein the calibrated value is associated with at least one of the following attributes: a vehicle kinematics attribute, a vehicle user operation attribute, an object ahead kinematics attribute, and a road ahead attribute; and combining the standard value of TTAB and the standard value of TTAS with respective corresponding calibrated values to determine, for a current user, a corrected value of TTAB and a corrected value of TTAS, characterized in that, the calibrated value of TTAB and the calibrated value of TTAS are determined by utilising a deep learning method, a model of the deep learning method comprises: an input layer, wherein the input layer comprises a plurality of input nodes (A1, A2, A3, A4), and each of the input nodes is associated with at least one of the following attributes: a vehicle kinematics attribute, a vehicle user operation attribute, an object ahead kinematics attribute, and a road ahead attribute; a hidden layer, and an output layer, wherein the output layer comprises an output node about the calibrated value of TTAB and an output node about the calibrated value of TTAS. an input layer, wherein the input layer comprises a plurality of input nodes (A1, A2, A3, A4), and each of the input nodes is associated with at least one of the following attributes: a vehicle kinematics attribute, a vehicle user operation attribute, an object ahead kinematics attribute, and a road ahead attribute; a hidden layer, and an output layer, wherein the output layer comprises an output node about the calibrated value of TTAB and an output node about the calibrated value of TTAS.