| CPC B62D 15/029 (2013.01) [B60W 40/105 (2013.01); B60W 2510/20 (2013.01); B60W 2554/4041 (2020.02)] | 7 Claims |
|
1. A real-time path planning method taking into account dynamic properties of vehicles, comprising the steps of:
step 1: taking into account the dynamic properties of a vehicle, and calculating a reachable set of the vehicle based on a vehicle state and a wheel lateral force without connecting to internet;
step 2: constructing an artificial potential field, and obtaining a path planning taking into account non-linear properties of the vehicle based on the artificial potential field and the reachable set of the vehicle;
wherein step 1 further comprises the steps of:
step 1.1: obtaining an initial state of the vehicle, and estimating the wheel lateral force of the vehicle based on a two-degree-of-freedom vehicle model and a non-linear wheel model without connecting to internet; wherein the initial state of the vehicle comprises an initial value of transverse vehicle speed, a longitudinal vehicle speed and a yaw velocity;
step 1.2: determining an input amount according to the wheel lateral force of the vehicle, and predicting a vehicle state at a next moment based on a discretized three-degree-of-freedom vehicle model without connecting to internet;
step 1.3: calculating another vehicle state at another moment, subsequent to the next moment, based on the initial state of the vehicle and the predicted vehicle state at the next moment;
step 1.4: determining a range of feasible input variables according to the wheel lateral force estimated in step 1.1 and determining a maximum value of the wheel lateral force of the vehicle based on the range of feasible input variables evaluating values in the range of the feasible input variables and selecting a specific value, in the range of the feasible input variables, as the input amount in accordance with the wheel lateral force of the vehicle estimated in step 1.1, and repeating steps 1.2 and 1.3 to obtain a specific vehicle reachable set in the initial state of the vehicle, wherein the reachable set of the vehicle includes discrete data of the initial state of the vehicle, vehicle wheel lateral force data, vehicle next moment state data and vehicle another moment state data;
step 1.5: determining a range of safe vehicle states according to a maximum value of the transverse vehicle speed, a maximum value of the longitudinal vehicle speed, a maximum value of the yaw velocity, and the initial state of the vehicle; evaluating values in the range of the safe vehicle states and selecting a specific value, in the range of the safe vehicle states, as the initial state of the vehicle, and repeating steps 1.1-1.4 to obtain vehicle reachable sets that includes the specific vehicle reachable set, and storing the vehicle reachable sets in a database without connecting to internet;
wherein step 2 comprises the steps of:
step 2.1: obtaining the initial state of the vehicle with connecting to internet, querying the reachable set of the vehicle based on the initial state of the vehicle, and taking it as a range of an end point of a path planned in a current step;
step 2.2: constructing an artificial potential field distribution of a scene based on a driving environment;
step 2.3: selecting a point with a minimum barrier coefficient obtained based on an artificial potential field method in the reachable set of the vehicle queried in step 2.1, and using the point as an end point of the path planned in the current step at the next moment;
step 2.4: querying a vehicle state at the next moment corresponding to the reachable set of the vehicle according to the end point of the path planned in the current step at the next moment, and taking the vehicle state at the next moment as the initial state of the vehicle of a planned path in a next predicted sample time;
step 2.5: repeating steps 2.1-2.4 until a valid duration of a required future path is equal to a sum of predicted sample times, and completing the path planning.
|