Longitudinal MPC set qp tolerance to 1e-3 (#23899)

* initialize solverExecutionTime longitudinal_planner

* test_onroad: improve timing evaluation

* longitudinal MPC: set qp_tol to 1e-3

* update ref

Co-authored-by: Harald Schafer <harald.the.engineer@gmail.com>
old-commit-hash: ab371f888a8ee0d309130bd56aba04ab84820921

selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py

@@ -178,6 +178,7 @@ def gen_long_mpc_solver():
   # More iterations take too much time and less lead to inaccurate convergence in
   # some situations. Ideally we would run just 1 iteration to ensure fixed runtime.
   ocp.solver_options.qp_solver_iter_max = 10
+  ocp.solver_options.qp_tol = 1e-3
 
   # set prediction horizon
   ocp.solver_options.tf = Tf

selfdrive/controls/lib/longitudinal_planner.py

@@ -55,6 +55,7 @@ class Planner:
     self.v_desired_trajectory = np.zeros(CONTROL_N)
     self.a_desired_trajectory = np.zeros(CONTROL_N)
     self.j_desired_trajectory = np.zeros(CONTROL_N)
+    self.solverExecutionTime = 0.0
 
   def update(self, sm):
     v_ego = sm['carState'].vEgo

selfdrive/test/process_replay/ref_commit

@@ -1 +1 @@
-5cd22804273530aaf69ebb9f5039e8d08ca44ef8
+10555bbd908c7eead5eeaa36ab07ec2189845a95

selfdrive/test/test_onroad.py

@@ -216,8 +216,9 @@ class TestOnroad(unittest.TestCase):
       ts = [getattr(getattr(m, s), "solverExecutionTime") for m in self.lr if m.which() == s]
       self.assertLess(min(ts), instant_max, f"high '{s}' execution time: {min(ts)}")
       self.assertLess(np.mean(ts), avg_max, f"high avg '{s}' execution time: {np.mean(ts)}")
-      result += f"'{s}' execution time: {min(ts)}\n"
-      result += f"'{s}' avg execution time: {np.mean(ts)}\n"
+      result += f"'{s}' execution time: min  {min(ts):.5f}s\n"
+      result += f"'{s}' execution time: max  {max(ts):.5f}s\n"
+      result += f"'{s}' execution time: mean {np.mean(ts):.5f}s\n"
     result += "------------------------------------------------\n"
     print(result)
 
@@ -237,8 +238,8 @@ class TestOnroad(unittest.TestCase):
       ts = [getattr(getattr(m, s), "modelExecutionTime") for m in self.lr if m.which() == s]
       self.assertLess(min(ts), instant_max, f"high '{s}' execution time: {min(ts)}")
       self.assertLess(np.mean(ts), avg_max, f"high avg '{s}' execution time: {np.mean(ts)}")
-      result += f"'{s}' execution time: {min(ts)}\n"
-      result += f"'{s}' avg execution time: {np.mean(ts)}\n"
+      result += f"'{s}' execution time: min  {min(ts):.5f}s\n"
+      result += f"'{s}' execution time: mean {np.mean(ts):.5f}s\n"
     result += "------------------------------------------------\n"
     print(result)