mirror of
https://github.com/sunnypilot/sunnypilot.git
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Revert "Longitudinal: Dynamic Experimental Control" (#571)
Revert "Longitudinal: Dynamic Experimental Control (#564)"
This reverts commit bba3c39e2f.
This commit is contained in:
Kumar
+1
-8
@@ -82,14 +82,7 @@ struct ModelManagerSP @0xaedffd8f31e7b55d {
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}
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}
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struct LongitudinalPlanSP @0xf35cc4560bbf6ec2 {
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mpcSource @0 :MpcSource;
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dynamicExperimentalControl @1 :Bool;
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enum MpcSource {
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acc @0;
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blended @1;
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}
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struct CustomReserved2 @0xf35cc4560bbf6ec2 {
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}
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struct CustomReserved3 @0xda96579883444c35 {
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+1
-1
@@ -2633,7 +2633,7 @@ struct Event {
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# *********** Custom: reserved for forks ***********
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selfdriveStateSP @107 :Custom.SelfdriveStateSP;
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modelManagerSP @108 :Custom.ModelManagerSP;
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longitudinalPlanSP @109 :Custom.LongitudinalPlanSP;
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customReserved2 @109 :Custom.CustomReserved2;
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customReserved3 @110 :Custom.CustomReserved3;
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customReserved4 @111 :Custom.CustomReserved4;
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customReserved5 @112 :Custom.CustomReserved5;
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@@ -77,7 +77,6 @@ _services: dict[str, tuple] = {
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# sunnypilot
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"modelManagerSP": (False, 1., 1),
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"selfdriveStateSP": (True, 100., 10),
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"longitudinalPlanSP": (True, 20., 10),
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# debug
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"uiDebug": (True, 0., 1),
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@@ -223,8 +223,6 @@ std::unordered_map<std::string, uint32_t> keys = {
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{"SunnylinkDongleId", PERSISTENT},
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{"SunnylinkdPid", PERSISTENT},
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{"SunnylinkEnabled", PERSISTENT},
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{"DynamicExperimentalControl", PERSISTENT},
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};
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} // namespace
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@@ -16,8 +16,6 @@ from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N, get_speed_
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from openpilot.selfdrive.car.cruise import V_CRUISE_MAX, V_CRUISE_UNSET
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from openpilot.common.swaglog import cloudlog
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from openpilot.sunnypilot.selfdrive.controls.lib.dec.helpers import DecPlanner
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LON_MPC_STEP = 0.2 # first step is 0.2s
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A_CRUISE_MIN = -1.2
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A_CRUISE_MAX_VALS = [1.6, 1.2, 0.8, 0.6]
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@@ -69,11 +67,10 @@ def get_accel_from_plan(speeds, accels, action_t=DT_MDL, vEgoStopping=0.05):
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return a_target, should_stop
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class LongitudinalPlanner(DecPlanner):
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class LongitudinalPlanner:
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def __init__(self, CP, init_v=0.0, init_a=0.0, dt=DT_MDL):
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self.CP = CP
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self.mpc = LongitudinalMpc(dt=dt)
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DecPlanner.__init__(self, self.CP, self.mpc)
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self.fcw = False
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self.dt = dt
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self.allow_throttle = True
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@@ -108,10 +105,7 @@ class LongitudinalPlanner(DecPlanner):
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return x, v, a, j, throttle_prob
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def update(self, sm):
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DecPlanner.update(self, sm)
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self.mpc.mode = 'blended' if sm['selfdriveState'].experimentalMode else 'acc'
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if dec_mpc_mode := self.get_mpc_mode(sm):
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self.mpc.mode = dec_mpc_mode
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if len(sm['carControl'].orientationNED) == 3:
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accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
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@@ -212,4 +206,3 @@ class LongitudinalPlanner(DecPlanner):
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longitudinalPlan.allowThrottle = self.allow_throttle
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pm.send('longitudinalPlan', plan_send)
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self.publish_longitudinal_plan_sp(sm, pm)
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@@ -18,7 +18,7 @@ def main():
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ldw = LaneDepartureWarning()
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longitudinal_planner = LongitudinalPlanner(CP)
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pm = messaging.PubMaster(['longitudinalPlan', 'driverAssistance', 'longitudinalPlanSP'])
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pm = messaging.PubMaster(['longitudinalPlan', 'driverAssistance'])
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sm = messaging.SubMaster(['carControl', 'carState', 'controlsState', 'liveParameters', 'radarState', 'modelV2', 'selfdriveState'],
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poll='modelV2', ignore_avg_freq=['radarState'])
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@@ -40,12 +40,6 @@ TogglesPanel::TogglesPanel(SettingsWindow *parent) : ListWidget(parent) {
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"",
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"../assets/img_experimental_white.svg",
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},
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{
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"DynamicExperimentalControl",
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tr("Enable Dynamic Experimental Control"),
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tr("Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal."),
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"../assets/offroad/icon_blank.png",
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},
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{
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"DisengageOnAccelerator",
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tr("Disengage on Accelerator Pedal"),
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@@ -1411,14 +1411,6 @@ This may take up to a minute.</source>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation>تمكين مراقبة السائق حتى عندما لا يكون نظام OpenPilot مُفعّلاً.</translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1395,14 +1395,6 @@ This may take up to a minute.</source>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1395,14 +1395,6 @@ Esto puede tardar un minuto.</translation>
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<source>Enable the openpilot longitudinal control (alpha) toggle to allow Experimental mode.</source>
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<translation>Activar el control longitudinal (fase experimental) para permitir el modo Experimental.</translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1395,14 +1395,6 @@ Cela peut prendre jusqu'à une minute.</translation>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1389,14 +1389,6 @@ This may take up to a minute.</source>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1391,14 +1391,6 @@ This may take up to a minute.</source>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation>Openpilot이 활성화되지 않은 경우에도 드라이버 모니터링을 활성화합니다.</translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1395,14 +1395,6 @@ Isso pode levar até um minuto.</translation>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation>Habilite o monitoramento do motorista mesmo quando o openpilot não estiver acionado.</translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1391,14 +1391,6 @@ This may take up to a minute.</source>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1389,14 +1389,6 @@ This may take up to a minute.</source>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1391,14 +1391,6 @@ This may take up to a minute.</source>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation>即使在openpilot未激活时也启用驾驶员监控。</translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1391,14 +1391,6 @@ This may take up to a minute.</source>
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<source>Enable driver monitoring even when openpilot is not engaged.</source>
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<translation>即使在openpilot未激活時也啟用駕駛監控。</translation>
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</message>
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<message>
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<source>Enable Dynamic Experimental Control</source>
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<translation type="unfinished"></translation>
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</message>
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<message>
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<source>Enable toggle to allow the model to determine when to use sunnypilot ACC or sunnypilot End to End Longitudinal.</source>
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<translation type="unfinished"></translation>
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</message>
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</context>
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<context>
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<name>Updater</name>
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@@ -1,399 +0,0 @@
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# The MIT License
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#
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# Copyright (c) 2019-, Rick Lan, dragonpilot community, and a number of other of contributors.
|
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#
|
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# Permission is hereby granted, free of charge, to any person obtaining a copy
|
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# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to the following conditions:
|
||||
#
|
||||
# The above copyright notice and this permission notice shall be included in
|
||||
# all copies or substantial portions of the Software.
|
||||
#
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
|
||||
# THE SOFTWARE.
|
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#
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# Version = 2024-7-11
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import numpy as np
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from cereal import messaging
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from openpilot.common.numpy_fast import interp
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from openpilot.common.params import Params
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from openpilot.common.realtime import DT_MDL
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# d-e2e, from modeldata.h
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TRAJECTORY_SIZE = 33
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LEAD_WINDOW_SIZE = 4
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LEAD_PROB = 0.6
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SLOW_DOWN_WINDOW_SIZE = 4
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SLOW_DOWN_PROB = 0.6
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SLOW_DOWN_BP = [0., 10., 20., 30., 40., 50., 55., 60.]
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SLOW_DOWN_DIST = [25., 38., 55., 75., 95., 115., 130., 150.]
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SLOWNESS_WINDOW_SIZE = 12
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SLOWNESS_PROB = 0.5
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SLOWNESS_CRUISE_OFFSET = 1.05
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DANGEROUS_TTC_WINDOW_SIZE = 3
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DANGEROUS_TTC = 2.3
|
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HIGHWAY_CRUISE_KPH = 70
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|
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STOP_AND_GO_FRAME = 60
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|
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SET_MODE_TIMEOUT = 10
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|
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MPC_FCW_WINDOW_SIZE = 10
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MPC_FCW_PROB = 0.5
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V_ACC_MIN = 9.72
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class SNG_State:
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off = 0
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stopped = 1
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going = 2
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|
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|
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class GenericMovingAverageCalculator:
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def __init__(self, window_size):
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self.window_size = window_size
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self.data = []
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self.total = 0
|
||||
|
||||
def add_data(self, value: float) -> None:
|
||||
if len(self.data) == self.window_size:
|
||||
self.total -= self.data.pop(0)
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self.data.append(value)
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self.total += value
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||||
|
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def get_moving_average(self) -> float | None:
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return None if len(self.data) == 0 else self.total / len(self.data)
|
||||
|
||||
def reset_data(self) -> None:
|
||||
self.data = []
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||||
self.total = 0
|
||||
|
||||
|
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class WeightedMovingAverageCalculator:
|
||||
def __init__(self, window_size):
|
||||
self.window_size = window_size
|
||||
self.data = []
|
||||
self.weights = np.linspace(1, 3, window_size) # Linear weights, adjust as needed
|
||||
|
||||
def add_data(self, value: float) -> None:
|
||||
if len(self.data) == self.window_size:
|
||||
self.data.pop(0)
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||||
self.data.append(value)
|
||||
|
||||
def get_weighted_average(self) -> float | None:
|
||||
if len(self.data) == 0:
|
||||
return None
|
||||
weighted_sum: float = float(np.dot(self.data, self.weights[-len(self.data):]))
|
||||
weight_total: float = float(np.sum(self.weights[-len(self.data):]))
|
||||
return weighted_sum / weight_total
|
||||
|
||||
def reset_data(self) -> None:
|
||||
self.data = []
|
||||
|
||||
|
||||
class DynamicExperimentalController:
|
||||
def __init__(self, params=None):
|
||||
self._params = params or Params()
|
||||
self._is_enabled: bool = self._params.get_bool("DynamicExperimentalControl")
|
||||
self._mode: str = 'acc'
|
||||
self._mode_prev: str = 'acc'
|
||||
self._mode_changed: bool = False
|
||||
self._frame: int = 0
|
||||
|
||||
# Use weighted moving average for filtering leads
|
||||
self._lead_gmac = WeightedMovingAverageCalculator(window_size=LEAD_WINDOW_SIZE)
|
||||
self._has_lead_filtered = False
|
||||
self._has_lead_filtered_prev = False
|
||||
|
||||
self._slow_down_gmac = WeightedMovingAverageCalculator(window_size=SLOW_DOWN_WINDOW_SIZE)
|
||||
self._has_slow_down = False
|
||||
|
||||
self._has_blinkers = False
|
||||
|
||||
self._slowness_gmac = WeightedMovingAverageCalculator(window_size=SLOWNESS_WINDOW_SIZE)
|
||||
self._has_slowness = False
|
||||
|
||||
self._has_nav_instruction = False
|
||||
|
||||
self._dangerous_ttc_gmac = WeightedMovingAverageCalculator(window_size=DANGEROUS_TTC_WINDOW_SIZE)
|
||||
self._has_dangerous_ttc = False
|
||||
|
||||
self._v_ego_kph = 0.
|
||||
self._v_cruise_kph = 0.
|
||||
|
||||
self._has_lead = False
|
||||
|
||||
self._has_standstill = False
|
||||
self._has_standstill_prev = False
|
||||
|
||||
self._sng_transit_frame = 0
|
||||
self._sng_state = SNG_State.off
|
||||
|
||||
self._mpc_fcw_gmac = WeightedMovingAverageCalculator(window_size=MPC_FCW_WINDOW_SIZE)
|
||||
self._has_mpc_fcw = False
|
||||
self._mpc_fcw_crash_cnt = 0
|
||||
|
||||
self._set_mode_timeout = 0
|
||||
|
||||
@staticmethod
|
||||
def _anomaly_detection(recent_data: list[float], threshold: float = 2.0, context_check: bool = True) -> bool:
|
||||
"""
|
||||
Basic anomaly detection using standard deviation.
|
||||
"""
|
||||
if len(recent_data) < 5:
|
||||
return False
|
||||
mean: float = float(np.mean(recent_data))
|
||||
std_dev: float = float(np.std(recent_data))
|
||||
anomaly: bool = bool(recent_data[-1] > mean + threshold * std_dev)
|
||||
|
||||
# Context check to ensure repeated anomaly
|
||||
if context_check:
|
||||
return bool(np.count_nonzero(np.array(recent_data) > mean + threshold * std_dev) > 1)
|
||||
return anomaly
|
||||
|
||||
def _adaptive_slowdown_threshold(self) -> float:
|
||||
"""
|
||||
Adapts the slow-down threshold based on vehicle speed and recent behavior.
|
||||
"""
|
||||
adaptive_threshold: float = float(
|
||||
interp(self._v_ego_kph, SLOW_DOWN_BP, SLOW_DOWN_DIST) * (1.0 + 0.05 * np.log(1 + len(self._slow_down_gmac.data)))
|
||||
)
|
||||
return adaptive_threshold
|
||||
|
||||
def _smoothed_lead_detection(self, lead_prob: float, smoothing_factor: float = 0.2) -> bool:
|
||||
"""
|
||||
Smoothing the lead detection to avoid erratic behavior.
|
||||
"""
|
||||
self._has_lead_filtered = (1 - smoothing_factor) * self._has_lead_filtered + smoothing_factor * lead_prob
|
||||
return bool(self._has_lead_filtered > LEAD_PROB)
|
||||
|
||||
def _adaptive_lead_prob_threshold(self) -> float:
|
||||
"""
|
||||
Adapts lead probability threshold based on driving conditions.
|
||||
"""
|
||||
if self._v_ego_kph > HIGHWAY_CRUISE_KPH:
|
||||
return float(LEAD_PROB + 0.1) # Increase the threshold on highways
|
||||
return float(LEAD_PROB)
|
||||
|
||||
def _update(self, sm: messaging.SubMaster) -> None:
|
||||
car_state = sm['carState']
|
||||
lead_one = sm['radarState'].leadOne
|
||||
md = sm['modelV2']
|
||||
|
||||
self._v_ego_kph = car_state.vEgo * 3.6
|
||||
self._v_cruise_kph = car_state.vCruise
|
||||
self._has_lead = lead_one.status
|
||||
self._has_standstill = car_state.standstill
|
||||
|
||||
# fcw detection
|
||||
self._mpc_fcw_gmac.add_data(self._mpc_fcw_crash_cnt > 0)
|
||||
self._has_mpc_fcw = self._mpc_fcw_gmac.get_weighted_average() > MPC_FCW_PROB
|
||||
|
||||
# nav enable detection
|
||||
# self._has_nav_instruction = md.navEnabledDEPRECATED and maneuver_distance / max(car_state.vEgo, 1) < 13
|
||||
|
||||
# lead detection with smoothing
|
||||
self._lead_gmac.add_data(lead_one.status)
|
||||
#self._has_lead_filtered = self._lead_gmac.get_weighted_average() > LEAD_PROB
|
||||
lead_prob = self._lead_gmac.get_weighted_average() or 0
|
||||
self._has_lead_filtered = self._smoothed_lead_detection(lead_prob)
|
||||
|
||||
# adaptive slow down detection
|
||||
adaptive_threshold = self._adaptive_slowdown_threshold()
|
||||
slow_down_trigger = len(md.orientation.x) == len(md.position.x) == TRAJECTORY_SIZE and md.position.x[TRAJECTORY_SIZE - 1] < adaptive_threshold
|
||||
self._slow_down_gmac.add_data(slow_down_trigger)
|
||||
self._has_slow_down = self._slow_down_gmac.get_weighted_average() > SLOW_DOWN_PROB
|
||||
|
||||
# anomaly detection for slow down events
|
||||
if self._anomaly_detection(self._slow_down_gmac.data):
|
||||
# Handle anomaly: potentially log it, adjust behavior, or issue a warning
|
||||
self._has_slow_down = False # Reset slow down if anomaly detected
|
||||
|
||||
# blinker detection
|
||||
self._has_blinkers = car_state.leftBlinker or car_state.rightBlinker
|
||||
|
||||
# sng detection
|
||||
if self._has_standstill:
|
||||
self._sng_state = SNG_State.stopped
|
||||
self._sng_transit_frame = 0
|
||||
else:
|
||||
if self._sng_transit_frame == 0:
|
||||
if self._sng_state == SNG_State.stopped:
|
||||
self._sng_state = SNG_State.going
|
||||
self._sng_transit_frame = STOP_AND_GO_FRAME
|
||||
elif self._sng_state == SNG_State.going:
|
||||
self._sng_state = SNG_State.off
|
||||
elif self._sng_transit_frame > 0:
|
||||
self._sng_transit_frame -= 1
|
||||
|
||||
# slowness detection
|
||||
if not self._has_standstill:
|
||||
self._slowness_gmac.add_data(self._v_ego_kph <= (self._v_cruise_kph * SLOWNESS_CRUISE_OFFSET))
|
||||
self._has_slowness = self._slowness_gmac.get_weighted_average() > SLOWNESS_PROB
|
||||
|
||||
# dangerous TTC detection
|
||||
if not self._has_lead_filtered and self._has_lead_filtered_prev:
|
||||
self._dangerous_ttc_gmac.reset_data()
|
||||
self._has_dangerous_ttc = False
|
||||
|
||||
if self._has_lead and car_state.vEgo >= 0.01:
|
||||
self._dangerous_ttc_gmac.add_data(lead_one.dRel / car_state.vEgo)
|
||||
|
||||
self._has_dangerous_ttc = self._dangerous_ttc_gmac.get_weighted_average() is not None and self._dangerous_ttc_gmac.get_weighted_average() <= DANGEROUS_TTC
|
||||
|
||||
# keep prev values
|
||||
self._has_standstill_prev = self._has_standstill
|
||||
self._has_lead_filtered_prev = self._has_lead_filtered
|
||||
|
||||
def _radarless_mode(self) -> None:
|
||||
# when mpc fcw crash prob is high
|
||||
# use blended to slow down quickly
|
||||
if self._has_mpc_fcw:
|
||||
self._set_mode('blended')
|
||||
return
|
||||
|
||||
# Nav enabled and distance to upcoming turning is 300 or below
|
||||
# if self._has_nav_instruction:
|
||||
# self._set_mode('blended')
|
||||
# return
|
||||
|
||||
# when blinker is on and speed is driving below V_ACC_MIN: blended
|
||||
# we don't want it to switch mode at higher speed, blended may trigger hard brake
|
||||
# if self._has_blinkers and self._v_ego_kph < V_ACC_MIN:
|
||||
# self._set_mode('blended')
|
||||
# return
|
||||
|
||||
# when at highway cruise and SNG: blended
|
||||
# ensuring blended mode is used because acc is bad at catching SNG lead car
|
||||
# especially those who accel very fast and then brake very hard.
|
||||
# if self._sng_state == SNG_State.going and self._v_cruise_kph >= V_ACC_MIN:
|
||||
# self._set_mode('blended')
|
||||
# return
|
||||
|
||||
# when standstill: blended
|
||||
# in case of lead car suddenly move away under traffic light, acc mode won't brake at traffic light.
|
||||
if self._has_standstill:
|
||||
self._set_mode('blended')
|
||||
return
|
||||
|
||||
# when detecting slow down scenario: blended
|
||||
# e.g. traffic light, curve, stop sign etc.
|
||||
if self._has_slow_down:
|
||||
self._set_mode('blended')
|
||||
return
|
||||
|
||||
# when detecting lead slow down: blended
|
||||
# use blended for higher braking capability
|
||||
if self._has_dangerous_ttc:
|
||||
self._set_mode('blended')
|
||||
return
|
||||
|
||||
# car driving at speed lower than set speed: acc
|
||||
if self._has_slowness:
|
||||
self._set_mode('acc')
|
||||
return
|
||||
|
||||
self._set_mode('acc')
|
||||
|
||||
def _radar_mode(self) -> None:
|
||||
# when mpc fcw crash prob is high
|
||||
# use blended to slow down quickly
|
||||
if self._has_mpc_fcw:
|
||||
self._set_mode('blended')
|
||||
return
|
||||
|
||||
# If there is a filtered lead, the vehicle is not in standstill, and the lead vehicle's yRel meets the condition,
|
||||
if self._has_lead_filtered and not self._has_standstill:
|
||||
self._set_mode('acc')
|
||||
return
|
||||
|
||||
# when blinker is on and speed is driving below V_ACC_MIN: blended
|
||||
# we don't want it to switch mode at higher speed, blended may trigger hard brake
|
||||
# if self._has_blinkers and self._v_ego_kph < V_ACC_MIN:
|
||||
# self._set_mode('blended')
|
||||
# return
|
||||
|
||||
# when standstill: blended
|
||||
# in case of lead car suddenly move away under traffic light, acc mode won't brake at traffic light.
|
||||
if self._has_standstill:
|
||||
self._set_mode('blended')
|
||||
return
|
||||
|
||||
# when detecting slow down scenario: blended
|
||||
# e.g. traffic light, curve, stop sign etc.
|
||||
if self._has_slow_down:
|
||||
self._set_mode('blended')
|
||||
return
|
||||
|
||||
# car driving at speed lower than set speed: acc
|
||||
if self._has_slowness:
|
||||
self._set_mode('acc')
|
||||
return
|
||||
|
||||
# Nav enabled and distance to upcoming turning is 300 or below
|
||||
# if self._has_nav_instruction:
|
||||
# self._set_mode('blended')
|
||||
# return
|
||||
|
||||
self._set_mode('acc')
|
||||
|
||||
def get_mpc_mode(self) -> str:
|
||||
return str(self._mode)
|
||||
|
||||
def has_changed(self) -> bool:
|
||||
return bool(self._mode_changed)
|
||||
|
||||
def set_enabled(self, enabled: bool) -> None:
|
||||
self._is_enabled = enabled
|
||||
|
||||
def is_enabled(self) -> bool:
|
||||
return self._is_enabled
|
||||
|
||||
def set_mpc_fcw_crash_cnt(self, crash_cnt: float) -> None:
|
||||
self._mpc_fcw_crash_cnt = crash_cnt
|
||||
|
||||
def _set_mode(self, mode: str) -> None:
|
||||
if self._set_mode_timeout == 0:
|
||||
self._mode = mode
|
||||
if mode == 'blended':
|
||||
self._set_mode_timeout = SET_MODE_TIMEOUT
|
||||
|
||||
if self._set_mode_timeout > 0:
|
||||
self._set_mode_timeout -= 1
|
||||
|
||||
def _read_params(self) -> None:
|
||||
if self._frame % int(1. / DT_MDL) == 0:
|
||||
self._is_enabled = self._params.get_bool("DynamicExperimentalControl")
|
||||
|
||||
def update(self, radar_unavailable: bool, sm: messaging.SubMaster) -> None:
|
||||
self._read_params()
|
||||
|
||||
if self._is_enabled:
|
||||
self._update(sm)
|
||||
|
||||
if radar_unavailable:
|
||||
self._radarless_mode()
|
||||
else:
|
||||
self._radar_mode()
|
||||
|
||||
self._mode_changed = self._mode != self._mode_prev
|
||||
self._mode_prev = self._mode
|
||||
|
||||
self._frame += 1
|
||||
@@ -1,46 +0,0 @@
|
||||
"""
|
||||
Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
|
||||
|
||||
This file is part of sunnypilot and is licensed under the MIT License.
|
||||
See the LICENSE.md file in the root directory for more details.
|
||||
"""
|
||||
|
||||
from cereal import messaging, custom
|
||||
from opendbc.car import structs
|
||||
from openpilot.sunnypilot.selfdrive.controls.lib.dec.dec import DynamicExperimentalController
|
||||
|
||||
MpcSource = custom.LongitudinalPlanSP.MpcSource
|
||||
|
||||
|
||||
class DecPlanner:
|
||||
def __init__(self, CP: structs.CarParams, mpc):
|
||||
self.CP = CP
|
||||
self.mpc = mpc
|
||||
|
||||
self.is_enabled = False
|
||||
|
||||
self.dynamic_experimental_controller = DynamicExperimentalController()
|
||||
|
||||
def get_mpc_mode(self, sm: messaging.SubMaster):
|
||||
if not self.is_enabled or not sm['selfdriveState'].experimentalMode:
|
||||
return None
|
||||
|
||||
return self.dynamic_experimental_controller.get_mpc_mode()
|
||||
|
||||
def update(self, sm: messaging.SubMaster) -> None:
|
||||
self.dynamic_experimental_controller.set_mpc_fcw_crash_cnt(self.mpc.crash_cnt)
|
||||
self.dynamic_experimental_controller.update(self.CP.radarUnavailable, sm)
|
||||
|
||||
def publish_longitudinal_plan_sp(self, sm: messaging.SubMaster, pm: messaging.PubMaster) -> None:
|
||||
plan_sp_send = messaging.new_message('longitudinalPlanSP')
|
||||
|
||||
plan_sp_send.valid = sm.all_checks(service_list=['carState', 'controlsState'])
|
||||
|
||||
longitudinalPlanSP = plan_sp_send.longitudinalPlanSP
|
||||
|
||||
# DEC
|
||||
longitudinalPlanSP.mpcSource = MpcSource.blended if self.mpc.mode == 'blended' else MpcSource.acc
|
||||
|
||||
longitudinalPlanSP.dynamicExperimentalControl = self.dynamic_experimental_controller.is_enabled()
|
||||
|
||||
pm.send('longitudinalPlanSP', plan_sp_send)
|
||||
@@ -1,257 +0,0 @@
|
||||
from openpilot.sunnypilot.selfdrive.controls.lib.dec.dec import (
|
||||
DynamicExperimentalController,
|
||||
TRAJECTORY_SIZE,
|
||||
LEAD_WINDOW_SIZE,
|
||||
SLOW_DOWN_WINDOW_SIZE,
|
||||
DANGEROUS_TTC_WINDOW_SIZE,
|
||||
MPC_FCW_WINDOW_SIZE,
|
||||
SNG_State,
|
||||
STOP_AND_GO_FRAME
|
||||
)
|
||||
|
||||
import pytest
|
||||
import numpy as np
|
||||
from openpilot.common.params import Params
|
||||
|
||||
class MockInterp:
|
||||
def __call__(self, x, xp, fp):
|
||||
return np.interp(x, xp, fp)
|
||||
|
||||
class MockCarState:
|
||||
def __init__(self, v_ego=0., standstill=False, left_blinker=False, right_blinker=False):
|
||||
self.vEgo = v_ego
|
||||
self.standstill = standstill
|
||||
self.leftBlinker = left_blinker
|
||||
self.rightBlinker = right_blinker
|
||||
|
||||
class MockLeadOne:
|
||||
def __init__(self, status=False, d_rel=0):
|
||||
self.status = status
|
||||
self.dRel = d_rel
|
||||
|
||||
class MockModelData:
|
||||
def __init__(self, x_vals=None, positions=None):
|
||||
self.orientation = type('Orientation', (), {'x': x_vals})()
|
||||
self.position = type('Position', (), {'x': positions})()
|
||||
|
||||
class MockControlState:
|
||||
def __init__(self, v_cruise=0):
|
||||
self.vCruise = v_cruise
|
||||
|
||||
@pytest.fixture
|
||||
def interp(monkeypatch):
|
||||
mock_interp = MockInterp()
|
||||
monkeypatch.setattr('openpilot.common.numpy_fast.interp', mock_interp)
|
||||
return mock_interp
|
||||
|
||||
@pytest.fixture
|
||||
def controller(interp):
|
||||
params = Params()
|
||||
params.put_bool("DynamicExperimentalControl", True)
|
||||
controller = DynamicExperimentalController()
|
||||
return controller
|
||||
|
||||
def test_initial_state(controller):
|
||||
"""Test initial state of the controller"""
|
||||
assert controller._mode == 'acc'
|
||||
assert not controller._has_lead
|
||||
assert not controller._has_standstill
|
||||
assert controller._sng_state == SNG_State.off
|
||||
assert not controller._has_lead_filtered
|
||||
assert not controller._has_slow_down
|
||||
assert not controller._has_dangerous_ttc
|
||||
assert not controller._has_mpc_fcw
|
||||
|
||||
@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
|
||||
def test_standstill_detection(controller, has_radar):
|
||||
"""Test standstill detection and state transitions"""
|
||||
car_state = MockCarState(standstill=True)
|
||||
lead_one = MockLeadOne()
|
||||
md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
|
||||
controls_state = MockControlState()
|
||||
|
||||
# Test transition to standstill
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
assert controller._sng_state == SNG_State.stopped
|
||||
assert controller.get_mpc_mode() == 'blended'
|
||||
|
||||
# Test transition from standstill to moving
|
||||
car_state.standstill = False
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
assert controller._sng_state == SNG_State.going
|
||||
|
||||
# Test complete transition to normal driving
|
||||
for _ in range(STOP_AND_GO_FRAME + 1):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
assert controller._sng_state == SNG_State.off
|
||||
|
||||
@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
|
||||
def test_lead_detection(controller, has_radar):
|
||||
"""Test lead vehicle detection and filtering"""
|
||||
car_state = MockCarState(v_ego=20) # 72 kph
|
||||
lead_one = MockLeadOne(status=True, d_rel=50) # Safe distance
|
||||
md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
|
||||
controls_state = MockControlState(v_cruise=72)
|
||||
|
||||
# Let moving average stabilize
|
||||
for _ in range(LEAD_WINDOW_SIZE + 1):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert controller._has_lead_filtered
|
||||
expected_mode = 'acc' if has_radar else 'blended'
|
||||
assert controller.get_mpc_mode() == expected_mode
|
||||
|
||||
# Test lead loss detection
|
||||
lead_one.status = False
|
||||
for _ in range(LEAD_WINDOW_SIZE + 1):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert not controller._has_lead_filtered
|
||||
|
||||
@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
|
||||
def test_slow_down_detection(controller, has_radar):
|
||||
"""Test slow down detection based on trajectory"""
|
||||
car_state = MockCarState(v_ego=10/3.6) # 10 kph
|
||||
lead_one = MockLeadOne()
|
||||
x_vals = [0] * TRAJECTORY_SIZE
|
||||
positions = [20] * TRAJECTORY_SIZE # Position within slow down threshold
|
||||
md = MockModelData(x_vals=x_vals, positions=positions)
|
||||
controls_state = MockControlState(v_cruise=30)
|
||||
|
||||
# Test slow down detection
|
||||
for _ in range(SLOW_DOWN_WINDOW_SIZE + 1):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert controller._has_slow_down
|
||||
assert controller.get_mpc_mode() == 'blended'
|
||||
|
||||
# Test slow down recovery
|
||||
positions = [200] * TRAJECTORY_SIZE # Position outside slow down threshold
|
||||
md = MockModelData(x_vals=x_vals, positions=positions)
|
||||
for _ in range(SLOW_DOWN_WINDOW_SIZE + 1):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert not controller._has_slow_down
|
||||
|
||||
@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
|
||||
def test_dangerous_ttc_detection(controller, has_radar):
|
||||
"""Test Time-To-Collision detection and handling"""
|
||||
car_state = MockCarState(v_ego=10) # 36 kph
|
||||
lead_one = MockLeadOne(status=True)
|
||||
md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
|
||||
controls_state = MockControlState(v_cruise=36)
|
||||
|
||||
# First establish normal conditions with lead
|
||||
lead_one.dRel = 100 # Safe distance
|
||||
for _ in range(LEAD_WINDOW_SIZE + 1): # First establish lead detection
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert controller._has_lead_filtered # Verify lead is detected
|
||||
|
||||
# Now test dangerous TTC detection
|
||||
lead_one.dRel = 10 # 10m distance - should trigger dangerous TTC
|
||||
# TTC = dRel/vEgo = 10/10 = 1s (which is less than DANGEROUS_TTC = 2.3s)
|
||||
|
||||
# Need to update multiple times to allow the weighted average to stabilize
|
||||
for _ in range(DANGEROUS_TTC_WINDOW_SIZE * 2):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert controller._has_dangerous_ttc, "TTC of 1s should be considered dangerous"
|
||||
expected_mode = 'acc' if has_radar else 'blended'
|
||||
assert controller.get_mpc_mode() == expected_mode, f"Should be in [{expected_mode}] mode with dangerous TTC"
|
||||
|
||||
@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
|
||||
def test_mode_transitions(controller, has_radar):
|
||||
"""Test comprehensive mode transitions under different conditions"""
|
||||
# Initialize with normal driving conditions
|
||||
car_state = MockCarState(v_ego=25) # 90 kph
|
||||
lead_one = MockLeadOne(status=False)
|
||||
md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[200] * TRAJECTORY_SIZE)
|
||||
controls_state = MockControlState(v_cruise=100)
|
||||
|
||||
def stabilize_filters():
|
||||
"""Helper to let all moving averages stabilize"""
|
||||
for _ in range(max(LEAD_WINDOW_SIZE, SLOW_DOWN_WINDOW_SIZE,
|
||||
DANGEROUS_TTC_WINDOW_SIZE, MPC_FCW_WINDOW_SIZE) + 1):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
# Test 1: Normal driving -> ACC mode
|
||||
stabilize_filters()
|
||||
assert controller.get_mpc_mode() == 'acc', "Should be in ACC mode under normal driving conditions"
|
||||
|
||||
# Test 2: Standstill -> Blended mode
|
||||
car_state.standstill = True
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
assert controller.get_mpc_mode() == 'blended', "Should be in blended mode during standstill"
|
||||
|
||||
# Test 3: Lead car appears -> ACC mode
|
||||
car_state = MockCarState(v_ego=20) # Reset car state
|
||||
lead_one.status = True
|
||||
lead_one.dRel = 50 # Safe distance
|
||||
stabilize_filters()
|
||||
assert not controller._has_dangerous_ttc, "Should not have dangerous TTC"
|
||||
assert controller.get_mpc_mode() == 'acc', "Should be in ACC mode with safe lead distance"
|
||||
|
||||
# Test 4: Dangerous TTC -> Blended mode
|
||||
car_state = MockCarState(v_ego=20) # 72 kph
|
||||
lead_one.status = True
|
||||
lead_one.dRel = 50 # First establish normal lead detection
|
||||
|
||||
# First establish lead detection
|
||||
for _ in range(LEAD_WINDOW_SIZE + 1):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert controller._has_lead_filtered # Verify lead is detected
|
||||
|
||||
# Now create dangerous TTC condition
|
||||
lead_one.dRel = 20 # This creates a TTC of 1s, well below DANGEROUS_TTC
|
||||
|
||||
for _ in range(DANGEROUS_TTC_WINDOW_SIZE * 2):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert controller._has_dangerous_ttc, "Should detect dangerous TTC condition"
|
||||
expected_mode = 'acc' if has_radar else 'blended'
|
||||
assert controller.get_mpc_mode() == expected_mode, f"Should be in [{expected_mode}] mode with dangerous TTC"
|
||||
|
||||
@pytest.mark.parametrize("has_radar", [True, False], ids=["with_radar", "without_radar"])
|
||||
def test_mpc_fcw_handling(controller, has_radar):
|
||||
"""Test MPC FCW crash count handling and mode transitions"""
|
||||
car_state = MockCarState(v_ego=20)
|
||||
lead_one = MockLeadOne()
|
||||
md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
|
||||
controls_state = MockControlState(v_cruise=72)
|
||||
|
||||
# Test FCW activation
|
||||
controller.set_mpc_fcw_crash_cnt(5)
|
||||
for _ in range(MPC_FCW_WINDOW_SIZE + 1):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert controller._has_mpc_fcw
|
||||
assert controller.get_mpc_mode() == 'blended'
|
||||
|
||||
# Test FCW recovery
|
||||
controller.set_mpc_fcw_crash_cnt(0)
|
||||
for _ in range(MPC_FCW_WINDOW_SIZE + 1):
|
||||
controller.update(not has_radar, car_state, lead_one, md, controls_state)
|
||||
|
||||
assert not controller._has_mpc_fcw
|
||||
|
||||
def test_radar_unavailable_handling(controller):
|
||||
"""Test behavior transitions between radar available and unavailable states"""
|
||||
car_state = MockCarState(v_ego=27.78) # 100 kph
|
||||
lead_one = MockLeadOne(status=True, d_rel=50)
|
||||
md = MockModelData(x_vals=[0] * TRAJECTORY_SIZE, positions=[150] * TRAJECTORY_SIZE)
|
||||
controls_state = MockControlState(v_cruise=100)
|
||||
|
||||
# Test with radar available
|
||||
for _ in range(LEAD_WINDOW_SIZE + 1):
|
||||
controller.update(False, car_state, lead_one, md, controls_state)
|
||||
radar_mode = controller.get_mpc_mode()
|
||||
|
||||
# Test with radar unavailable
|
||||
for _ in range(LEAD_WINDOW_SIZE + 1):
|
||||
controller.update(True, car_state, lead_one, md, controls_state)
|
||||
radarless_mode = controller.get_mpc_mode()
|
||||
|
||||
assert radar_mode is not None
|
||||
assert radarless_mode is not None
|
||||
@@ -43,7 +43,6 @@ def manager_init() -> None:
|
||||
]
|
||||
|
||||
sunnypilot_default_params: list[tuple[str, str | bytes]] = [
|
||||
("DynamicExperimentalControl", "0"),
|
||||
("Mads", "1"),
|
||||
("MadsMainCruiseAllowed", "1"),
|
||||
("MadsPauseLateralOnBrake", "0"),
|
||||
|
||||
Reference in New Issue
Block a user