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Driving Personality setting (also changes follow distance) (#24742)

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* Skeleton

* Adjustable follow parameter

* fix rebase

* long planner

* typo

* Add ui toggle

* Fix icon

* Improve text

* Better toggle position

* Im a UX engineer now

* add param reader

* CHange jerk to have same crash test performance

* Try reading param

* Unused comment

* translate ui text

* std stoi

* Parametrized buttons

* Empty strings are ints

* Move to generic

* Update translations

* Fix translation diffs

* Release notes

* update refs

* tweaks

* Misc fixes

* No param is standard
old-commit-hash: 205c900742
beeps
Harald Schäfer 2 years ago committed by GitHub
parent 6d32cb8dc4
commit 36aa81cdc3
14 changed files with 270 additions and 16 deletions
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  1. 5
      RELEASES.md
  2. 1
      common/params.cc
  3. 49
      selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
  4. 21
      selfdrive/controls/lib/longitudinal_planner.py
  5. 2
      selfdrive/manager/manager.py
  6. 2
      selfdrive/test/process_replay/ref_commit
  7. 12
      selfdrive/ui/qt/offroad/settings.cc
  8. 74
      selfdrive/ui/qt/widgets/controls.h
  9. 20
      selfdrive/ui/translations/main_de.ts
  10. 20
      selfdrive/ui/translations/main_ja.ts
  11. 20
      selfdrive/ui/translations/main_ko.ts
  12. 20
      selfdrive/ui/translations/main_pt-BR.ts
  13. 20
      selfdrive/ui/translations/main_zh-CHS.ts
  14. 20
      selfdrive/ui/translations/main_zh-CHT.ts

5
RELEASES.md
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@ -1,6 +1,11 @@
Version 0.9.3 (2023-06-XX)
========================
* New driving model
* New driving personality setting
* Three settings: aggressive, standard, and relaxed
* Standard is recommended and the default
* In aggressive mode lead follow distance is shorter and quicker gas/brake response
* In relaxed mode lead follow distance is longer
Version 0.9.2 (2023-05-22)
========================

1
common/params.cc
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@ -104,6 +104,7 @@ std::unordered_map<std::string, uint32_t> keys = {
{"DisablePowerDown", PERSISTENT},
{"ExperimentalMode", PERSISTENT},
{"ExperimentalModeConfirmed", PERSISTENT},
{"LongitudinalPersonality", PERSISTENT},
{"ExperimentalLongitudinalEnabled", PERSISTENT},
{"DisableUpdates", PERSISTENT},
{"DisengageOnAccelerator", PERSISTENT},

49
selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py
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@ -1,7 +1,7 @@
#!/usr/bin/env python3
import os
import numpy as np
from cereal import log
from common.realtime import sec_since_boot
from common.numpy_fast import clip
from system.swaglog import cloudlog
@ -54,18 +54,38 @@ FCW_IDXS = T_IDXS < 5.0
T_DIFFS = np.diff(T_IDXS, prepend=[0.])
MIN_ACCEL = -3.5
MAX_ACCEL = 2.0
T_FOLLOW = 1.45
COMFORT_BRAKE = 2.5
STOP_DISTANCE = 6.0
def get_jerk_factor(personality=log.LongitudinalPersonality.standard):
if personality==log.LongitudinalPersonality.relaxed:
return 1.0
elif personality==log.LongitudinalPersonality.standard:
return 1.0
elif personality==log.LongitudinalPersonality.aggressive:
return 0.5
else:
raise NotImplementedError("Longitudinal personality not supported")
def get_T_FOLLOW(personality=log.LongitudinalPersonality.standard):
if personality==log.LongitudinalPersonality.relaxed:
return 1.75
elif personality==log.LongitudinalPersonality.standard:
return 1.45
elif personality==log.LongitudinalPersonality.aggressive:
return 1.25
else:
raise NotImplementedError("Longitudinal personality not supported")
def get_stopped_equivalence_factor(v_lead):
return (v_lead**2) / (2 * COMFORT_BRAKE)
def get_safe_obstacle_distance(v_ego, t_follow=T_FOLLOW):
def get_safe_obstacle_distance(v_ego, t_follow):
return (v_ego**2) / (2 * COMFORT_BRAKE) + t_follow * v_ego + STOP_DISTANCE
def desired_follow_distance(v_ego, v_lead):
return get_safe_obstacle_distance(v_ego) - get_stopped_equivalence_factor(v_lead)
def desired_follow_distance(v_ego, v_lead, t_follow=get_T_FOLLOW()):
return get_safe_obstacle_distance(v_ego, t_follow) - get_stopped_equivalence_factor(v_lead)
def gen_long_model():
@ -161,7 +181,8 @@ def gen_long_ocp():
x0 = np.zeros(X_DIM)
ocp.constraints.x0 = x0
ocp.parameter_values = np.array([-1.2, 1.2, 0.0, 0.0, T_FOLLOW, LEAD_DANGER_FACTOR])
ocp.parameter_values = np.array([-1.2, 1.2, 0.0, 0.0, get_T_FOLLOW(), LEAD_DANGER_FACTOR])
# We put all constraint cost weights to 0 and only set them at runtime
cost_weights = np.zeros(CONSTR_DIM)
@ -249,10 +270,11 @@ class LongitudinalMpc:
for i in range(N):
self.solver.cost_set(i, 'Zl', Zl)
def set_weights(self, prev_accel_constraint=True):
def set_weights(self, prev_accel_constraint=True, personality=log.LongitudinalPersonality.standard):
jerk_factor = get_jerk_factor(personality)
if self.mode == 'acc':
a_change_cost = A_CHANGE_COST if prev_accel_constraint else 0
cost_weights = [X_EGO_OBSTACLE_COST, X_EGO_COST, V_EGO_COST, A_EGO_COST, a_change_cost, J_EGO_COST]
cost_weights = [X_EGO_OBSTACLE_COST, X_EGO_COST, V_EGO_COST, A_EGO_COST, jerk_factor * a_change_cost, jerk_factor * J_EGO_COST]
constraint_cost_weights = [LIMIT_COST, LIMIT_COST, LIMIT_COST, DANGER_ZONE_COST]
elif self.mode == 'blended':
a_change_cost = 40.0 if prev_accel_constraint else 0
@ -307,7 +329,8 @@ class LongitudinalMpc:
self.cruise_min_a = min_a
self.max_a = max_a
def update(self, radarstate, v_cruise, x, v, a, j):
def update(self, radarstate, v_cruise, x, v, a, j, personality=log.LongitudinalPersonality.standard):
t_follow = get_T_FOLLOW(personality)
v_ego = self.x0[1]
self.status = radarstate.leadOne.status or radarstate.leadTwo.status
@ -334,7 +357,7 @@ class LongitudinalMpc:
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)
cruise_obstacle = np.cumsum(T_DIFFS * v_cruise_clipped) + get_safe_obstacle_distance(v_cruise_clipped, get_T_FOLLOW())
x_obstacles = np.column_stack([lead_0_obstacle, lead_1_obstacle, cruise_obstacle])
self.source = SOURCES[np.argmin(x_obstacles[0])]
@ -368,7 +391,7 @@ class LongitudinalMpc:
self.params[:,2] = np.min(x_obstacles, axis=1)
self.params[:,3] = np.copy(self.prev_a)
self.params[:,4] = T_FOLLOW
self.params[:,4] = t_follow
self.run()
if (np.any(lead_xv_0[FCW_IDXS,0] - self.x_sol[FCW_IDXS,0] < CRASH_DISTANCE) and
@ -380,9 +403,9 @@ class LongitudinalMpc:
# Check if it got within lead comfort range
# TODO This should be done cleaner
if self.mode == 'blended':
if any((lead_0_obstacle - get_safe_obstacle_distance(self.x_sol[:,1], T_FOLLOW))- self.x_sol[:,0] < 0.0):
if any((lead_0_obstacle - get_safe_obstacle_distance(self.x_sol[:,1], t_follow))- self.x_sol[:,0] < 0.0):
self.source = 'lead0'
if any((lead_1_obstacle - get_safe_obstacle_distance(self.x_sol[:,1], T_FOLLOW))- self.x_sol[:,0] < 0.0) and \
if any((lead_1_obstacle - get_safe_obstacle_distance(self.x_sol[:,1], t_follow))- self.x_sol[:,0] < 0.0) and \
(lead_1_obstacle[0] - lead_0_obstacle[0]):
self.source = 'lead1'

21
selfdrive/controls/lib/longitudinal_planner.py
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@ -2,6 +2,8 @@
import math
import numpy as np
from common.numpy_fast import clip, interp
from common.params import Params
from cereal import log
import cereal.messaging as messaging
from common.conversions import Conversions as CV
@ -57,6 +59,17 @@ class LongitudinalPlanner:
self.a_desired_trajectory = np.zeros(CONTROL_N)
self.j_desired_trajectory = np.zeros(CONTROL_N)
self.solverExecutionTime = 0.0
self.params = Params()
self.param_read_counter = 0
self.read_param()
self.personality = log.LongitudinalPersonality.standard
def read_param(self):
param_value = self.params.get('LongitudinalPersonality')
if param_value is not None:
self.personality = int(param_value)
else:
self.personality = log.LongitudinalPersonality.standard
@staticmethod
def parse_model(model_msg, model_error):
@ -75,6 +88,9 @@ class LongitudinalPlanner:
return x, v, a, j
def update(self, sm):
if self.param_read_counter % 50 == 0:
self.read_param()
self.param_read_counter += 1
self.mpc.mode = 'blended' if sm['controlsState'].experimentalMode else 'acc'
v_ego = sm['carState'].vEgo
@ -114,11 +130,11 @@ class LongitudinalPlanner:
accel_limits_turns[0] = min(accel_limits_turns[0], self.a_desired + 0.05)
accel_limits_turns[1] = max(accel_limits_turns[1], self.a_desired - 0.05)
self.mpc.set_weights(prev_accel_constraint)
self.mpc.set_weights(prev_accel_constraint, personality=self.personality)
self.mpc.set_accel_limits(accel_limits_turns[0], accel_limits_turns[1])
self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
x, v, a, j = self.parse_model(sm['modelV2'], self.v_model_error)
self.mpc.update(sm['radarState'], v_cruise, x, v, a, j)
self.mpc.update(sm['radarState'], v_cruise, x, v, a, j, personality=self.personality)
self.v_desired_trajectory_full = np.interp(T_IDXS, T_IDXS_MPC, self.mpc.v_solution)
self.a_desired_trajectory_full = np.interp(T_IDXS, T_IDXS_MPC, self.mpc.a_solution)
@ -154,5 +170,6 @@ class LongitudinalPlanner:
longitudinalPlan.fcw = self.fcw
longitudinalPlan.solverExecutionTime = self.mpc.solve_time
longitudinalPlan.personality = self.personality
pm.send('longitudinalPlan', plan_send)

2
selfdrive/manager/manager.py
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@ -7,6 +7,7 @@ import sys
import traceback
from typing import List, Tuple, Union
from cereal import log
import cereal.messaging as messaging
import selfdrive.sentry as sentry
from common.basedir import BASEDIR
@ -44,6 +45,7 @@ def manager_init() -> None:
("HasAcceptedTerms", "0"),
("LanguageSetting", "main_en"),
("OpenpilotEnabledToggle", "1"),
("LongitudinalPersonality", str(log.LongitudinalPersonality.standard)),
]
if not PC:
default_params.append(("LastUpdateTime", datetime.datetime.utcnow().isoformat().encode('utf8')))

2
selfdrive/test/process_replay/ref_commit
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@ -1 +1 @@
dfe1e4a5fd7a464c91c0d2e70592b4b1470fda8d
3e684aef4483b8d311d71bab3bb543d7bad26563

12
selfdrive/ui/qt/offroad/settings.cc
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@ -89,6 +89,13 @@ TogglesPanel::TogglesPanel(SettingsWindow *parent) : ListWidget(parent) {
#endif
};
std::vector<QString> longi_button_texts{tr("Aggressive"), tr("Standard"), tr("Relaxed")};
std::vector<int> longi_button_widths{300, 250, 225};
ButtonParamControl* long_personality_setting = new ButtonParamControl("LongitudinalPersonality", tr("Driving Personality"),
tr("Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake."),
"../assets/offroad/icon_speed_limit.png",
longi_button_texts, longi_button_widths);
for (auto &[param, title, desc, icon] : toggle_defs) {
auto toggle = new ParamControl(param, title, desc, icon, this);
@ -97,6 +104,11 @@ TogglesPanel::TogglesPanel(SettingsWindow *parent) : ListWidget(parent) {
addItem(toggle);
toggles[param.toStdString()] = toggle;
// insert longitudinal personality after NDOG toggle
if (param == "DisengageOnAccelerator") {
addItem(long_personality_setting);
}
}
// Toggles with confirmation dialogs

74
selfdrive/ui/qt/widgets/controls.h
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@ -197,6 +197,80 @@ private:
bool store_confirm = false;
};
class ButtonParamControl : public AbstractControl {
Q_OBJECT
public:
ButtonParamControl(const QString &param, const QString &title, const QString &desc, const QString &icon,
std::vector<QString> button_texts, std::vector<int> button_widths) : AbstractControl(title, desc, icon) {
select_style = (R"(
padding: 0;
border-radius: 50px;
font-size: 45px;
font-weight: 500;
color: #E4E4E4;
background-color: #33Ab4C;
)");
unselect_style = (R"(
padding: 0;
border-radius: 50px;
font-size: 45px;
font-weight: 350;
color: #E4E4E4;
background-color: #393939;
)");
key = param.toStdString();
for (int i = 0; i < button_texts.size(); i++) {
QPushButton *button = new QPushButton();
button->setText(button_texts[i]);
hlayout->addWidget(button);
button->setFixedSize(button_widths[i], 100);
button->setStyleSheet(unselect_style);
buttons.push_back(button);
QObject::connect(button, &QPushButton::clicked, [=]() {
params.put(key, (QString::number(i)).toStdString());
refresh();
});
}
refresh();
}
void read_param() {
auto value = params.get(key);
if (!value.empty()) {
param_value = std::stoi(value);
}
};
void set_param(int new_value) {
QString values = QString::number(new_value);
params.put(key, values.toStdString());
refresh();
};
void refresh() {
read_param();
for (int i = 0; i < buttons.size(); i++) {
buttons[i]->setStyleSheet(unselect_style);
if (param_value == i) {
buttons[i]->setStyleSheet(select_style);
}
}
};
void showEvent(QShowEvent *event) override {
refresh();
};
private:
std::string key;
std::vector<QPushButton*> buttons;
QString unselect_style;
QString select_style;
Params params;
int param_value = 0;
};
class ListWidget : public QWidget {
Q_OBJECT
public:

20
selfdrive/ui/translations/main_de.ts
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@ -1056,6 +1056,26 @@ This may take up to a minute.</source>
<source>On this car, openpilot defaults to the car&apos;s built-in ACC instead of openpilot&apos;s longitudinal control. Enable this to switch to openpilot longitudinal control. Enabling Experimental mode is recommended when enabling openpilot longitudinal control alpha.</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Aggressive</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Relaxed</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Driving Personality</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake.</source>
<translation type="unfinished"></translation>
</message>
</context>
<context>
<name>Updater</name>

20
selfdrive/ui/translations/main_ja.ts
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@ -1050,6 +1050,26 @@ This may take up to a minute.</source>
<source>On this car, openpilot defaults to the car&apos;s built-in ACC instead of openpilot&apos;s longitudinal control. Enable this to switch to openpilot longitudinal control. Enabling Experimental mode is recommended when enabling openpilot longitudinal control alpha.</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Aggressive</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Relaxed</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Driving Personality</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake.</source>
<translation type="unfinished"></translation>
</message>
</context>
<context>
<name>Updater</name>

20
selfdrive/ui/translations/main_ko.ts
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@ -1051,6 +1051,26 @@ This may take up to a minute.</source>
<source>On this car, openpilot defaults to the car&apos;s built-in ACC instead of openpilot&apos;s longitudinal control. Enable this to switch to openpilot longitudinal control. Enabling Experimental mode is recommended when enabling openpilot longitudinal control alpha.</source>
<translation>이 차량은 openpilot 롱컨트롤 대신 차량의 내장 ACC로 기본 설정됩니다. openpilot 롱컨트롤으로 전환하려면 이 기능을 활성화하세요. openpilot 롱컨트롤 알파를 활성화하는경우 실험적 모드 활성화를 권장합니다.</translation>
</message>
<message>
<source>Aggressive</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Relaxed</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Driving Personality</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake.</source>
<translation type="unfinished"></translation>
</message>
</context>
<context>
<name>Updater</name>

20
selfdrive/ui/translations/main_pt-BR.ts
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@ -1055,6 +1055,26 @@ Isso pode levar até um minuto.</translation>
<source>On this car, openpilot defaults to the car&apos;s built-in ACC instead of openpilot&apos;s longitudinal control. Enable this to switch to openpilot longitudinal control. Enabling Experimental mode is recommended when enabling openpilot longitudinal control alpha.</source>
<translation>Neste carro, o openpilot tem como padrão o ACC embutido do carro em vez do controle longitudinal do openpilot. Habilite isso para alternar para o controle longitudinal openpilot. Recomenda-se ativar o modo Experimental ao ativar o alfa de controle longitudinal openpilot.</translation>
</message>
<message>
<source>Aggressive</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Relaxed</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Driving Personality</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake.</source>
<translation type="unfinished"></translation>
</message>
</context>
<context>
<name>Updater</name>

20
selfdrive/ui/translations/main_zh-CHS.ts
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@ -1048,6 +1048,26 @@ This may take up to a minute.</source>
<source>On this car, openpilot defaults to the car&apos;s built-in ACC instead of openpilot&apos;s longitudinal control. Enable this to switch to openpilot longitudinal control. Enabling Experimental mode is recommended when enabling openpilot longitudinal control alpha.</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Aggressive</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Relaxed</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Driving Personality</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake.</source>
<translation type="unfinished"></translation>
</message>
</context>
<context>
<name>Updater</name>

20
selfdrive/ui/translations/main_zh-CHT.ts
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@ -1050,6 +1050,26 @@ This may take up to a minute.</source>
<source>On this car, openpilot defaults to the car&apos;s built-in ACC instead of openpilot&apos;s longitudinal control. Enable this to switch to openpilot longitudinal control. Enabling Experimental mode is recommended when enabling openpilot longitudinal control alpha.</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Aggressive</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Relaxed</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Driving Personality</source>
<translation type="unfinished"></translation>
</message>
<message>
<source>Standard is recommended. In aggressive mode, openpilot will follow lead cars closer and be more aggressive with the gas and brake.</source>
<translation type="unfinished"></translation>
</message>
</context>
<context>
<name>Updater</name>

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