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353 lines
12 KiB
353 lines
12 KiB
#!/usr/bin/env python3
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import os
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import numpy as np
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from common.realtime import sec_since_boot
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from common.numpy_fast import clip, interp
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from selfdrive.swaglog import cloudlog
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from selfdrive.modeld.constants import T_IDXS as T_IDXS_LST
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from selfdrive.controls.lib.drive_helpers import LON_MPC_N as N
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from selfdrive.controls.lib.radar_helpers import _LEAD_ACCEL_TAU
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from pyextra.acados_template import AcadosModel, AcadosOcp, AcadosOcpSolver
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from casadi import SX, vertcat
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LONG_MPC_DIR = os.path.dirname(os.path.abspath(__file__))
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EXPORT_DIR = os.path.join(LONG_MPC_DIR, "c_generated_code")
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JSON_FILE = "acados_ocp_long.json"
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SOURCES = ['lead0', 'lead1', 'cruise']
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X_DIM = 3
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U_DIM = 1
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COST_E_DIM = 3
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COST_DIM = COST_E_DIM + 1
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CONSTR_DIM = 4
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X_EGO_COST = 3.
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X_EGO_E2E_COST = 10.
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A_EGO_COST = 1.
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J_EGO_COST = 10.
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DANGER_ZONE_COST = 100.
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CRASH_DISTANCE = .5
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LIMIT_COST = 1e6
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T_IDXS = np.array(T_IDXS_LST)
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T_DIFFS = np.diff(T_IDXS, prepend=[0.])
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MIN_ACCEL = -3.5
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T_REACT = 1.8
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MAX_BRAKE = 9.81
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def get_stopped_equivalence_factor(v_lead):
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return T_REACT * v_lead + (v_lead*v_lead) / (2 * MAX_BRAKE)
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def get_safe_obstacle_distance(v_ego):
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return 2 * T_REACT * v_ego + (v_ego*v_ego) / (2 * MAX_BRAKE) + 4.0
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def desired_follow_distance(v_ego, v_lead):
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return get_safe_obstacle_distance(v_ego) - get_stopped_equivalence_factor(v_lead)
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def gen_long_model():
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model = AcadosModel()
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model.name = 'long'
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# set up states & controls
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x_ego = SX.sym('x_ego')
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v_ego = SX.sym('v_ego')
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a_ego = SX.sym('a_ego')
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model.x = vertcat(x_ego, v_ego, a_ego)
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# controls
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j_ego = SX.sym('j_ego')
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model.u = vertcat(j_ego)
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# xdot
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x_ego_dot = SX.sym('x_ego_dot')
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v_ego_dot = SX.sym('v_ego_dot')
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a_ego_dot = SX.sym('a_ego_dot')
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model.xdot = vertcat(x_ego_dot, v_ego_dot, a_ego_dot)
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# live parameters
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x_obstacle = SX.sym('x_obstacle')
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a_min = SX.sym('a_min')
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a_max = SX.sym('a_max')
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model.p = vertcat(a_min, a_max, x_obstacle)
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# dynamics model
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f_expl = vertcat(v_ego, a_ego, j_ego)
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model.f_impl_expr = model.xdot - f_expl
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model.f_expl_expr = f_expl
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return model
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def gen_long_mpc_solver():
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ocp = AcadosOcp()
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ocp.model = gen_long_model()
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Tf = T_IDXS[-1]
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# set dimensions
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ocp.dims.N = N
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# set cost module
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ocp.cost.cost_type = 'NONLINEAR_LS'
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ocp.cost.cost_type_e = 'NONLINEAR_LS'
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QR = np.zeros((COST_DIM, COST_DIM))
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Q = np.zeros((COST_E_DIM, COST_E_DIM))
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ocp.cost.W = QR
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ocp.cost.W_e = Q
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x_ego, v_ego, a_ego = ocp.model.x[0], ocp.model.x[1], ocp.model.x[2]
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j_ego = ocp.model.u[0]
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a_min, a_max = ocp.model.p[0], ocp.model.p[1]
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x_obstacle = ocp.model.p[2]
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ocp.cost.yref = np.zeros((COST_DIM, ))
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ocp.cost.yref_e = np.zeros((COST_E_DIM, ))
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desired_dist_comfort = get_safe_obstacle_distance(v_ego)
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# The main cost in normal operation is how close you are to the "desired" distance
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# from an obstacle at every timestep. This obstacle can be a lead car
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# or other object. In e2e mode we can use x_position targets as a cost
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# instead.
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costs = [((x_obstacle - x_ego) - (desired_dist_comfort)) / (v_ego + 10.),
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x_ego,
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a_ego,
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j_ego]
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ocp.model.cost_y_expr = vertcat(*costs)
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ocp.model.cost_y_expr_e = vertcat(*costs[:-1])
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# Constraints on speed, acceleration and desired distance to
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# the obstacle, which is treated as a slack constraint so it
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# behaves like an assymetrical cost.
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constraints = vertcat((v_ego),
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(a_ego - a_min),
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(a_max - a_ego),
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((x_obstacle - x_ego) - (3/4) * (desired_dist_comfort)) / (v_ego + 10.))
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ocp.model.con_h_expr = constraints
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ocp.model.con_h_expr_e = vertcat(np.zeros(CONSTR_DIM))
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x0 = np.zeros(X_DIM)
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ocp.constraints.x0 = x0
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ocp.parameter_values = np.array([-1.2, 1.2, 0.0])
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# We put all constraint cost weights to 0 and only set them at runtime
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cost_weights = np.zeros(CONSTR_DIM)
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ocp.cost.zl = cost_weights
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ocp.cost.Zl = cost_weights
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ocp.cost.Zu = cost_weights
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ocp.cost.zu = cost_weights
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ocp.constraints.lh = np.zeros(CONSTR_DIM)
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ocp.constraints.lh_e = np.zeros(CONSTR_DIM)
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ocp.constraints.uh = 1e4*np.ones(CONSTR_DIM)
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ocp.constraints.uh_e = 1e4*np.ones(CONSTR_DIM)
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ocp.constraints.idxsh = np.arange(CONSTR_DIM)
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# The HPIPM solver can give decent solutions even when it is stopped early
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# Which is critical for our purpose where the compute time is strictly bounded
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# We use HPIPM in the SPEED_ABS mode, which ensures fastest runtime. This
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# does not cause issues since the problem is well bounded.
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ocp.solver_options.qp_solver = 'PARTIAL_CONDENSING_HPIPM'
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ocp.solver_options.hessian_approx = 'GAUSS_NEWTON'
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ocp.solver_options.integrator_type = 'ERK'
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ocp.solver_options.nlp_solver_type = 'SQP_RTI'
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# More iterations take too much time and less lead to inaccurate convergence in
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# some situations. Ideally we would run just 1 iteration to ensure fixed runtime.
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ocp.solver_options.qp_solver_iter_max = 4
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# set prediction horizon
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ocp.solver_options.tf = Tf
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ocp.solver_options.shooting_nodes = T_IDXS
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ocp.code_export_directory = EXPORT_DIR
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return ocp
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class LongitudinalMpc():
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def __init__(self, e2e=False):
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self.e2e = e2e
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self.reset()
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self.accel_limit_arr = np.zeros((N+1, 2))
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self.accel_limit_arr[:,0] = -1.2
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self.accel_limit_arr[:,1] = 1.2
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self.source = SOURCES[2]
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def reset(self):
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self.solver = AcadosOcpSolver('long', N, EXPORT_DIR)
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self.v_solution = [0.0 for i in range(N+1)]
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self.a_solution = [0.0 for i in range(N+1)]
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self.j_solution = [0.0 for i in range(N)]
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self.yref = np.zeros((N+1, COST_DIM))
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self.solver.cost_set_slice(0, N, "yref", self.yref[:N])
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self.solver.set(N, "yref", self.yref[N][:COST_E_DIM])
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self.x_sol = np.zeros((N+1, X_DIM))
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self.u_sol = np.zeros((N,1))
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self.params = np.zeros((N+1,3))
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for i in range(N+1):
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self.solver.set(i, 'x', np.zeros(X_DIM))
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self.last_cloudlog_t = 0
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self.status = False
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self.crash_cnt = 0.0
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self.solution_status = 0
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self.x0 = np.zeros(X_DIM)
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self.set_weights()
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def set_weights(self):
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if self.e2e:
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self.set_weights_for_xva_policy()
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else:
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self.set_weights_for_lead_policy()
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def set_weights_for_lead_policy(self):
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W = np.diag([X_EGO_COST, 0.0, A_EGO_COST, J_EGO_COST])
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Ws = np.tile(W[None], reps=(N,1,1))
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self.solver.cost_set_slice(0, N, 'W', Ws, api='old')
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# Setting the slice without the copy make the array not contiguous,
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# causing issues with the C interface.
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self.solver.cost_set(N, 'W', np.copy(W[:COST_E_DIM, :COST_E_DIM]))
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# Set L2 slack cost on lower bound constraints
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Zl = np.array([LIMIT_COST, LIMIT_COST, LIMIT_COST, DANGER_ZONE_COST])
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Zls = np.tile(Zl[None], reps=(N+1,1,1))
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self.solver.cost_set_slice(0, N+1, 'Zl', Zls, api='old')
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def set_weights_for_xva_policy(self):
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W = np.diag([0.0, X_EGO_E2E_COST, 0., J_EGO_COST])
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Ws = np.tile(W[None], reps=(N,1,1))
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self.solver.cost_set_slice(0, N, 'W', Ws, api='old')
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# Setting the slice without the copy make the array not contiguous,
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# causing issues with the C interface.
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self.solver.cost_set(N, 'W', np.copy(W[:COST_E_DIM, :COST_E_DIM]))
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# Set L2 slack cost on lower bound constraints
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Zl = np.array([LIMIT_COST, LIMIT_COST, LIMIT_COST, 0.0])
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Zls = np.tile(Zl[None], reps=(N+1,1,1))
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self.solver.cost_set_slice(0, N+1, 'Zl', Zls, api='old')
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def set_cur_state(self, v, a):
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if abs(self.x0[1] - v) > 1.:
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self.x0[1] = v
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self.x0[2] = a
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for i in range(0, N+1):
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self.solver.set(i, 'x', self.x0)
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else:
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self.x0[1] = v
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self.x0[2] = a
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def extrapolate_lead(self, x_lead, v_lead, a_lead, a_lead_tau):
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a_lead_traj = a_lead * np.exp(-a_lead_tau * (T_IDXS**2)/2.)
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v_lead_traj = np.clip(v_lead + np.cumsum(T_DIFFS * a_lead_traj), 0.0, 1e8)
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x_lead_traj = x_lead + np.cumsum(T_DIFFS * v_lead_traj)
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lead_xv = np.column_stack((x_lead_traj, v_lead_traj))
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return lead_xv
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def process_lead(self, lead):
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v_ego = self.x0[1]
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if lead is not None and lead.status:
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x_lead = lead.dRel
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v_lead = lead.vLead
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a_lead = lead.aLeadK
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a_lead_tau = lead.aLeadTau
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else:
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# Fake a fast lead car, so mpc can keep running in the same mode
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x_lead = 50.0
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v_lead = v_ego + 10.0
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a_lead = 0.0
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a_lead_tau = _LEAD_ACCEL_TAU
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# MPC will not converge if immediate crash is expected
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# Clip lead distance to what is still possible to brake for
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min_x_lead = ((v_ego + v_lead)/2) * (v_ego - v_lead) / (-MIN_ACCEL * 2)
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x_lead = clip(x_lead, min_x_lead, 1e8)
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v_lead = clip(v_lead, 0.0, 1e8)
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a_lead = clip(a_lead, -10., 5.)
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lead_xv = self.extrapolate_lead(x_lead, v_lead, a_lead, a_lead_tau)
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return lead_xv
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def set_accel_limits(self, min_a, max_a):
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self.cruise_min_a = min_a
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self.cruise_max_a = max_a
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def update(self, carstate, radarstate, v_cruise):
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v_ego = self.x0[1]
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self.status = radarstate.leadOne.status or radarstate.leadTwo.status
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lead_xv_0 = self.process_lead(radarstate.leadOne)
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lead_xv_1 = self.process_lead(radarstate.leadTwo)
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# set accel limits in params
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self.params[:,0] = interp(float(self.status), [0.0, 1.0], [self.cruise_min_a, MIN_ACCEL])
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self.params[:,1] = self.cruise_max_a
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# To estimate a safe distance from a moving lead, we calculate how much stopping
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# distance that lead needs as a minimum. We can add that to the current distance
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# and then treat that as a stopped car/obstacle at this new distance.
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lead_0_obstacle = lead_xv_0[:,0] + get_stopped_equivalence_factor(lead_xv_0[:,1])
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lead_1_obstacle = lead_xv_1[:,0] + get_stopped_equivalence_factor(lead_xv_1[:,1])
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# Fake an obstacle for cruise, this ensures smooth acceleration to set speed
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# when the leads are no factor.
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cruise_lower_bound = v_ego + (3/4) * self.cruise_min_a * T_IDXS
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cruise_upper_bound = v_ego + (3/4) * self.cruise_max_a * T_IDXS
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v_cruise_clipped = np.clip(v_cruise * np.ones(N+1),
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cruise_lower_bound,
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cruise_upper_bound)
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cruise_obstacle = T_IDXS*v_cruise_clipped + get_safe_obstacle_distance(v_cruise_clipped)
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x_obstacles = np.column_stack([lead_0_obstacle, lead_1_obstacle, cruise_obstacle])
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self.source = SOURCES[np.argmin(x_obstacles[0])]
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self.params[:,2] = np.min(x_obstacles, axis=1)
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self.run()
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if (np.any(lead_xv_0[:,0] - self.x_sol[:,0] < CRASH_DISTANCE) and
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radarstate.leadOne.modelProb > 0.9):
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self.crash_cnt += 1
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else:
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self.crash_cnt = 0
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def update_with_xva(self, x, v, a):
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self.yref[:,1] = x
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self.solver.cost_set_slice(0, N, "yref", self.yref[:N], api='old')
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self.solver.set(N, "yref", self.yref[N][:COST_E_DIM])
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self.accel_limit_arr[:,0] = -10.
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self.accel_limit_arr[:,1] = 10.
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x_obstacle = 1e5*np.ones((N+1))
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self.params = np.concatenate([self.accel_limit_arr,
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x_obstacle[:,None]], axis=1)
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self.run()
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def run(self):
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for i in range(N+1):
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self.solver.set_param(i, self.params[i])
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self.solver.constraints_set(0, "lbx", self.x0)
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self.solver.constraints_set(0, "ubx", self.x0)
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self.solution_status = self.solver.solve()
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self.solver.fill_in_slice(0, N+1, 'x', self.x_sol)
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self.solver.fill_in_slice(0, N, 'u', self.u_sol)
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self.v_solution = self.x_sol[:,1]
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self.a_solution = self.x_sol[:,2]
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self.j_solution = self.u_sol[:,0]
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t = sec_since_boot()
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if self.solution_status != 0:
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if t > self.last_cloudlog_t + 5.0:
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self.last_cloudlog_t = t
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cloudlog.warning("Long mpc reset, solution_status: %s" % (
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self.solution_status))
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self.reset()
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if __name__ == "__main__":
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ocp = gen_long_mpc_solver()
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AcadosOcpSolver.generate(ocp, json_file=JSON_FILE, build=False)
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