From 3c9c61abcb2eba73781313db033f4c6471e6675c Mon Sep 17 00:00:00 2001 From: HaraldSchafer Date: Tue, 16 Nov 2021 17:57:55 -0800 Subject: [PATCH] Add cost to long plan change for smoother lag comp (#22923) * add plan changing cost * fix compile * set weights * try this cost * horizon problem * looks pretty good * update refs * update refs * smoother plan changes --- .../lib/longitudinal_mpc_lib/long_mpc.py | 34 +++++++++++++------ .../test_longitudinal.py | 2 +- selfdrive/test/process_replay/ref_commit | 2 +- 3 files changed, 25 insertions(+), 13 deletions(-) diff --git a/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py b/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py index 24586941dbf3ed8..58f5365c99c5686 100644 --- a/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py +++ b/selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py @@ -26,15 +26,17 @@ X_DIM = 3 U_DIM = 1 -COST_E_DIM = 4 +PARAM_DIM= 5 +COST_E_DIM = 5 COST_DIM = COST_E_DIM + 1 CONSTR_DIM = 4 X_EGO_OBSTACLE_COST = 3. -V_EGO_COST = 0. X_EGO_COST = 0. +V_EGO_COST = 0. A_EGO_COST = 0. -J_EGO_COST = 10. +J_EGO_COST = 5.0 +A_CHANGE_COST = .5 DANGER_ZONE_COST = 100. CRASH_DISTANCE = .5 LIMIT_COST = 1e6 @@ -88,7 +90,8 @@ def gen_long_model(): desired_TR = SX.sym('desired_TR') a_min = SX.sym('a_min') a_max = SX.sym('a_max') - model.p = vertcat(a_min, a_max, x_obstacle, desired_TR) + prev_a = SX.sym('prev_a') + model.p = vertcat(a_min, a_max, x_obstacle, prev_a, desired_TR) # dynamics model f_expl = vertcat(v_ego, a_ego, j_ego) @@ -121,7 +124,8 @@ def gen_long_mpc_solver(): a_min, a_max = ocp.model.p[0], ocp.model.p[1] x_obstacle = ocp.model.p[2] - desired_TR = ocp.model.p[3] + prev_a = ocp.model.p[3] + desired_TR = ocp.model.p[4] ocp.cost.yref = np.zeros((COST_DIM, )) ocp.cost.yref_e = np.zeros((COST_E_DIM, )) @@ -136,6 +140,7 @@ def gen_long_mpc_solver(): x_ego, v_ego, a_ego, + 20*(a_ego - prev_a), j_ego] ocp.model.cost_y_expr = vertcat(*costs) ocp.model.cost_y_expr_e = vertcat(*costs[:-1]) @@ -152,7 +157,7 @@ def gen_long_mpc_solver(): x0 = np.zeros(X_DIM) ocp.constraints.x0 = x0 - ocp.parameter_values = np.array([-1.2, 1.2, 0.0, T_REACT]) # defaults + ocp.parameter_values = np.array([-1.2, 1.2, 0.0, 0.0, T_REACT]) # defaults # We put all constraint cost weights to 0 and only set them at runtime cost_weights = np.zeros(CONSTR_DIM) @@ -205,6 +210,7 @@ def reset(self): self.solver = AcadosOcpSolverFast('long', N, EXPORT_DIR) self.v_solution = [0.0 for i in range(N+1)] self.a_solution = [0.0 for i in range(N+1)] + self.prev_a = self.a_solution self.j_solution = [0.0 for i in range(N)] self.yref = np.zeros((N+1, COST_DIM)) for i in range(N): @@ -212,7 +218,7 @@ def reset(self): self.solver.cost_set(N, "yref", self.yref[N][:COST_E_DIM]) self.x_sol = np.zeros((N+1, X_DIM)) self.u_sol = np.zeros((N,1)) - self.params = np.zeros((N+1,4)) + self.params = np.zeros((N+1, PARAM_DIM)) for i in range(N+1): self.solver.set(i, 'x', np.zeros(X_DIM)) self.last_cloudlog_t = 0 @@ -239,8 +245,9 @@ def set_weights_for_lead_policy(self): j_ego_cost_multiplier = interp(self.desired_TR, TRs, [0.5, 1.0, 1.0]) d_zone_cost_multiplier = interp(self.desired_TR, TRs, [4., 1.0, 1.0]) - W = np.asfortranarray(np.diag([X_EGO_OBSTACLE_COST * x_ego_obstacle_cost_multiplier, X_EGO_COST, V_EGO_COST, A_EGO_COST, J_EGO_COST * j_ego_cost_multiplier])) + W = np.asfortranarray(np.diag([X_EGO_OBSTACLE_COST * x_ego_obstacle_cost_multiplier, X_EGO_COST, V_EGO_COST, A_EGO_COST, A_CHANGE_COST, J_EGO_COST * j_ego_cost_multiplier])) for i in range(N): + W[4,4] = A_CHANGE_COST * np.interp(T_IDXS[i], [0.0, 1.0, 2.0], [1.0, 1.0, 0.0]) self.solver.cost_set(i, 'W', W) # Setting the slice without the copy make the array not contiguous, # causing issues with the C interface. @@ -252,7 +259,7 @@ def set_weights_for_lead_policy(self): self.solver.cost_set(i, 'Zl', Zl) def set_weights_for_xva_policy(self): - W = np.asfortranarray(np.diag([0., 10., 1., 10., 1.])) + W = np.asfortranarray(np.diag([0., 10., 1., 10., 0.0, 1.])) for i in range(N): self.solver.cost_set(i, 'W', W) # Setting the slice without the copy make the array not contiguous, @@ -348,7 +355,8 @@ def update(self, carstate, radarstate, v_cruise): x_obstacles = np.column_stack([lead_0_obstacle, lead_1_obstacle, cruise_obstacle]) self.source = SOURCES[np.argmin(x_obstacles[0])] self.params[:,2] = np.min(x_obstacles, axis=1) - self.params[:,3] = self.desired_TR + self.params[:,3] = np.copy(self.prev_a) + self.params[:,4] = self.desired_TR self.run() if (np.any(lead_xv_0[:,0] - self.x_sol[:,0] < CRASH_DISTANCE) and @@ -369,7 +377,9 @@ def update_with_xva(self, x, v, a): x_obstacle = 1e5*np.ones((N+1)) desired_TR = T_REACT*np.ones((N+1)) self.params = np.concatenate([self.accel_limit_arr, - x_obstacle[:,None], desired_TR[:,None]], axis=1) + x_obstacle[:,None], + self.prev_a, + desired_TR[:,None]], axis=1) self.run() @@ -388,6 +398,8 @@ def run(self): self.a_solution = self.x_sol[:,2] self.j_solution = self.u_sol[:,0] + self.prev_a = interp(T_IDXS + 0.05, T_IDXS, self.a_solution) + t = sec_since_boot() if self.solution_status != 0: if t > self.last_cloudlog_t + 5.0: diff --git a/selfdrive/test/longitudinal_maneuvers/test_longitudinal.py b/selfdrive/test/longitudinal_maneuvers/test_longitudinal.py index 4267f3c9e134d7b..35b9f32e68e86a2 100755 --- a/selfdrive/test/longitudinal_maneuvers/test_longitudinal.py +++ b/selfdrive/test/longitudinal_maneuvers/test_longitudinal.py @@ -62,7 +62,7 @@ speed_lead_values=[20., 20., 0.], prob_lead_values=[0., 1., 1.], cruise_values=[20., 20., 20.], - breakpoints=[2., 2.01, 8.51], + breakpoints=[2., 2.01, 8.8], ), Maneuver( "approach stopped car at 20m/s", diff --git a/selfdrive/test/process_replay/ref_commit b/selfdrive/test/process_replay/ref_commit index d58be73f571c5ba..3ccea748430f20b 100644 --- a/selfdrive/test/process_replay/ref_commit +++ b/selfdrive/test/process_replay/ref_commit @@ -1 +1 @@ -b3c61dc5f6777497fdd82fb7421a469a43efcbf1 \ No newline at end of file +6540e8c5a765975fd292b1efdef97b2d6391fa9c