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 (B1) about the calibrated value of TTAB and an output node (B2) 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 (B1) about the calibrated value of TTAB and an output node (B2) 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 (B1) about the calibrated value of TTAB and an output node (B2) about the calibrated value of TTAS. <EOS>