def reset(self):
self.solver = AcadosOcpSolverCython(MODEL_NAME, ACADOS_SOLVER_TYPE, N)
self.v_solution = np.zeros(N+1)
self.a_solution = np.zeros(N+1)
self.prev_a = np.array(self.a_solution)
self.j_solution = np.zeros(N)
self.yref = np.zeros((N+1, COST_DIM))
for i in range(N):
self.solver.cost_set(i, "yref", self.yref[i])
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, PARAM_DIM))
for i in range(N+1):
self.solver.set(i, 'x', np.zeros(X_DIM))
self.last_cloudlog_t = 0
self.status = False
self.crash_cnt = 0.0
self.solution_status = 0
# timers
self.solve_time = 0.0
self.time_qp_solution = 0.0
self.time_linearization = 0.0
self.time_integrator = 0.0
self.x0 = np.zeros(X_DIM)
self.set_weights()
def __init__(self, e2e=False):
self.e2e = e2e
self.solver = AcadosOcpSolverCython(MODEL_NAME, ACADOS_SOLVER_TYPE, N)
self.reset()
self.source = SOURCES[2]
class LongitudinalMpc:
def __init__(self, e2e=False):
self.e2e = e2e
self.solver = AcadosOcpSolverCython(MODEL_NAME, ACADOS_SOLVER_TYPE, N)
self.reset()
self.source = SOURCES[2]
def reset(self):
# self.solver = AcadosOcpSolverCython(MODEL_NAME, ACADOS_SOLVER_TYPE, N)
self.solver.reset()
# self.solver.options_set('print_level', 2)
self.v_solution = np.zeros(N + 1)
self.a_solution = np.zeros(N + 1)
self.prev_a = np.array(self.a_solution)
self.j_solution = np.zeros(N)
self.yref = np.zeros((N + 1, COST_DIM))
for i in range(N):
self.solver.cost_set(i, "yref", self.yref[i])
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, PARAM_DIM))
for i in range(N + 1):
self.solver.set(i, 'x', np.zeros(X_DIM))
self.last_cloudlog_t = 0
self.status = False
self.crash_cnt = 0.0
self.solution_status = 0
# timers
self.solve_time = 0.0
self.time_qp_solution = 0.0
self.time_linearization = 0.0
self.time_integrator = 0.0
self.x0 = np.zeros(X_DIM)
self.set_weights()
def set_weights(self, prev_accel_constraint=True):
if self.e2e:
self.set_weights_for_xva_policy()
self.params[:, 0] = -10.
self.params[:, 1] = 10.
self.params[:, 2] = 1e5
else:
self.set_weights_for_lead_policy(prev_accel_constraint)
def set_weights_for_lead_policy(self, prev_accel_constraint=True):
a_change_cost = A_CHANGE_COST if prev_accel_constraint else 0
W = np.asfortranarray(
np.diag([
X_EGO_OBSTACLE_COST, X_EGO_COST, V_EGO_COST, A_EGO_COST,
a_change_cost, J_EGO_COST
]))
for i in range(N):
# reduce the cost on (a-a_prev) later in the horizon.
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.
self.solver.cost_set(N, 'W', np.copy(W[:COST_E_DIM, :COST_E_DIM]))
# Set L2 slack cost on lower bound constraints
Zl = np.array([LIMIT_COST, LIMIT_COST, LIMIT_COST, DANGER_ZONE_COST])
for i in range(N):
self.solver.cost_set(i, 'Zl', Zl)
def set_weights_for_xva_policy(self):
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,
# causing issues with the C interface.
self.solver.cost_set(N, 'W', np.copy(W[:COST_E_DIM, :COST_E_DIM]))
# Set L2 slack cost on lower bound constraints
Zl = np.array([LIMIT_COST, LIMIT_COST, LIMIT_COST, 0.0])
for i in range(N):
self.solver.cost_set(i, 'Zl', Zl)
def set_cur_state(self, v, a):
v_prev = self.x0[1]
self.x0[1] = v
self.x0[2] = a
if abs(v_prev - v) > 2.: # probably only helps if v < v_prev
for i in range(0, N + 1):
self.solver.set(i, 'x', self.x0)
@staticmethod
def extrapolate_lead(x_lead, v_lead, a_lead, a_lead_tau):
a_lead_traj = a_lead * np.exp(-a_lead_tau * (T_IDXS**2) / 2.)
v_lead_traj = np.clip(v_lead + np.cumsum(T_DIFFS * a_lead_traj), 0.0,
1e8)
x_lead_traj = x_lead + np.cumsum(T_DIFFS * v_lead_traj)
lead_xv = np.column_stack((x_lead_traj, v_lead_traj))
return lead_xv
def process_lead(self, lead):
v_ego = self.x0[1]
if lead is not None and lead.status:
x_lead = lead.dRel
v_lead = lead.vLead
a_lead = lead.aLeadK
a_lead_tau = lead.aLeadTau
else:
# Fake a fast lead car, so mpc can keep running in the same mode
x_lead = 50.0
v_lead = v_ego + 10.0
a_lead = 0.0
a_lead_tau = _LEAD_ACCEL_TAU
# MPC will not converge if immediate crash is expected
# Clip lead distance to what is still possible to brake for
min_x_lead = (
(v_ego + v_lead) / 2) * (v_ego - v_lead) / (-MIN_ACCEL * 2)
x_lead = clip(x_lead, min_x_lead, 1e8)
v_lead = clip(v_lead, 0.0, 1e8)
a_lead = clip(a_lead, -10., 5.)
lead_xv = self.extrapolate_lead(x_lead, v_lead, a_lead, a_lead_tau)
return lead_xv
def set_accel_limits(self, min_a, max_a):
self.cruise_min_a = min_a
self.cruise_max_a = max_a
def update(self, carstate, radarstate, v_cruise):
v_ego = self.x0[1]
self.status = radarstate.leadOne.status or radarstate.leadTwo.status
lead_xv_0 = self.process_lead(radarstate.leadOne)
lead_xv_1 = self.process_lead(radarstate.leadTwo)
# set accel limits in params
self.params[:, 0] = interp(float(self.status), [0.0, 1.0],
[self.cruise_min_a, MIN_ACCEL])
self.params[:, 1] = self.cruise_max_a
# To estimate a safe distance from a moving lead, we calculate how much stopping
# distance that lead needs as a minimum. We can add that to the current distance
# and then treat that as a stopped car/obstacle at this new distance.
lead_0_obstacle = lead_xv_0[:, 0] + get_stopped_equivalence_factor(
lead_xv_0[:, 1])
lead_1_obstacle = lead_xv_1[:, 0] + get_stopped_equivalence_factor(
lead_xv_1[:, 1])
# Fake an obstacle for cruise, this ensures smooth acceleration to set speed
# when the leads are no factor.
v_lower = v_ego + (T_IDXS * self.cruise_min_a * 1.05)
v_upper = v_ego + (T_IDXS * self.cruise_max_a * 1.05)
v_cruise_clipped = np.clip(v_cruise * np.ones(N + 1), v_lower, v_upper)
cruise_obstacle = np.cumsum(
T_DIFFS *
v_cruise_clipped) + get_safe_obstacle_distance(v_cruise_clipped)
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] = np.copy(self.prev_a)
self.run()
if (np.any(lead_xv_0[:, 0] - self.x_sol[:, 0] < CRASH_DISTANCE)
and radarstate.leadOne.modelProb > 0.9):
self.crash_cnt += 1
else:
self.crash_cnt = 0
def update_with_xva(self, x, v, a):
# v, and a are in local frame, but x is wrt the x[0] position
# In >90degree turns, x goes to 0 (and may even be -ve)
# So, we use integral(v) + x[0] to obtain the forward-distance
xforward = ((v[1:] + v[:-1]) / 2) * (T_IDXS[1:] - T_IDXS[:-1])
x = np.cumsum(np.insert(xforward, 0, x[0]))
self.yref[:, 1] = x
self.yref[:, 2] = v
self.yref[:, 3] = a
for i in range(N):
self.solver.cost_set(i, "yref", self.yref[i])
self.solver.cost_set(N, "yref", self.yref[N][:COST_E_DIM])
self.params[:, 3] = np.copy(self.prev_a)
self.run()
def run(self):
# t0 = sec_since_boot()
# reset = 0
for i in range(N + 1):
self.solver.set(i, 'p', self.params[i])
self.solver.constraints_set(0, "lbx", self.x0)
self.solver.constraints_set(0, "ubx", self.x0)
self.solution_status = self.solver.solve()
self.solve_time = float(self.solver.get_stats('time_tot')[0])
self.time_qp_solution = float(self.solver.get_stats('time_qp')[0])
self.time_linearization = float(self.solver.get_stats('time_lin')[0])
self.time_integrator = float(self.solver.get_stats('time_sim')[0])
# qp_iter = self.solver.get_stats('statistics')[-1][-1] # SQP_RTI specific
# print(f"long_mpc timings: tot {self.solve_time:.2e}, qp {self.time_qp_solution:.2e}, lin {self.time_linearization:.2e}, integrator {self.time_integrator:.2e}, qp_iter {qp_iter}")
# res = self.solver.get_residuals()
# print(f"long_mpc residuals: {res[0]:.2e}, {res[1]:.2e}, {res[2]:.2e}, {res[3]:.2e}")
# self.solver.print_statistics()
for i in range(N + 1):
self.x_sol[i] = self.solver.get(i, 'x')
for i in range(N):
self.u_sol[i] = self.solver.get(i, 'u')
self.v_solution = self.x_sol[:, 1]
self.a_solution = self.x_sol[:, 2]
self.j_solution = self.u_sol[:, 0]
self.prev_a = np.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:
self.last_cloudlog_t = t
cloudlog.warning(
f"Long mpc reset, solution_status: {self.solution_status}")
self.reset()