dragonpilot 0.7.5.4

========================
* Removed Steering monitor timer adjustment in code (timer in settings will no longer work.)
* Updated Dynamic Follow model. (Special Thanks to @ShaneSmiskol for the feature and @cgw1968 for testing)

CHANGELOGS-DEV.md

@@ -1,3 +1,10 @@
+dragonpilot 0.7.5.4
+========================
+* 移除方向盤監控設定。(無法取消/關閉方向盤監控)
+* Removed Steering monitor timer adjustment in code (timer in settings will no longer work.)
+* Dynamic Follow 更新模型。(感謝 @ShaneSmiskol 提供代碼、 @cgw1968 測試)
+* Updated Dynamic Follow model. (Special Thanks to @ShaneSmiskol for the feature and @cgw1968 for testing)
+
 dragonpilot 0.7.5.3
 ========================
 * Dynamic Follow 更新至 ShaneSmiskol:stock_additions 0.7.5 版。(感謝 @ShaneSmiskol 提供代碼、 @Wei 測試)

CHANGELOGS-REL.md

@@ -1,3 +1,54 @@
+dragonpilot 0.7.5
+========================
+* 移除方向盤監控設定。(無法取消/關閉方向盤監控)
+* Removed Steering monitor timer adjustment in code (timer in settings will no longer work.)
+* 優化 Lexus GSH 轉向。(感謝 @簡銘佑 測試)
+* Optimize Lexus GSH steering. (Thanks to @簡銘佑)
+* C2 支援自動關機「DragonAutoShutdownAt」參數。(感謝 @cgw1968 建議)
+* C2 to support auto shutdown "DragonAutoShutDownAt" param. (Thanks to @cgw1968)
+* 將剎車狀熊顯示於 dp 資訊欄。
+* Added brake indicator to dp infobar.
+* 加入「方向燈取消控制」延遲控制設。(感謝 @wabes 建議)
+* Added delay config to "Disable Lat Control on Blinker". (Thanks to @wabes)
+* 加入巴西版 2020 Corolla Hybrid 指紋v2。(感謝 @berno22 提供)
+* Added Brazil 2020 Corolla Hybrid FPv2. (Thanks to @berno22) 
+* 加入對 VW MQB/PQ 的支援。(感謝 @dingliangxue 移植)
+* Added support to VW MQB/PQ platform. (Thanks to @dingliangxue)
+* 加入「允許多次自動換道」功能。(感謝 @阿濤 建議)
+* Added "Allow Continuous Auto Lane Change" Toggle. (Thanks to @阿濤)
+* 新增 DragonMaxSpeedLimit 設定值 (mph)，當如果車速高於此值 op 將會停止操控。(感謝 @Anthony 建議)
+* Added DragonMaxSpeedLimit parameter (mph), op will stop controlling when car speed is high than the value. (Thanks to @Anthony)
+* 新增加拿大版 2018 Toyota Sienna LTD 指紋(v2)。(感謝 明峰 提供)
+* Added Canada 2018 Toyota Sienna LTD fingerprint (v2). (Thanks to 明峰)
+* 新增「通過移動網路上傳」開關
+* Added Upload Over Mobile Network toggle.
+* 新增「通過熱點上傳」開關
+* Added Upload Over Hotspot toggle.
+* 新增加拿大版 2018 Toyota Sienna LTD 指紋(v1)。(感謝 明峰 提供)
+* Added Canada 2018 Toyota Sienna LTD fingerprint (v1). (Thanks to 明峰)
+* 新增大陸版 Volkswagen Golf GTI 指紋 (v1)。(感謝 easyeiji 提供)
+* Added China Volkswagen Golf GTI fingerprint (v1). (Thanks to easyeiji)
+* 更新 dp 圖示 (特別感謝 @wabes 的設計與提供)。
+* Updated dp logo, special thanks to @wabes for the design.
+* 簡/繁中文版和 i18n 整合成為單一版本。  
+* Merged zhs/zht/i18n versions into one.
+* 新增大陸版 CAMRY HYBRID 指紋v2。(感謝 @杜子腾)
+* Added China Camery Hybrid FPv2. (Thanks to @杜子腾) 
+* 新增台灣版 Altis HYBRID 指紋v1。(感謝 @Fish)
+* Added Taiwan Altis Hybrid FPv1. (Thanks to @Fish)
+* 新增行駛時關閉畫面功能。
+* Added Screen off while driving feature.
+* 新增倒車時關閉畫面功能。
+* Added Screen off while reversing feature.
+* 新增駕駛介面加入「加速模式」切換鈕。 
+* Added acceleration profile toggle onto driving UI.
+* 新增自定車型功能，取代指紋暫存功能。
+* Replaced fingerprint cache with custom car model selector.
+* 新增可調亮度。
+* Added Brightness changer.
+* 新增部分德語支持。(特別感謝 @arne182 提供)
+* Added partial de_DE language support (Thanks to @arne182)
+
 dragonpilot 0.7.4
 ========================
 * [2020-04-10] 移除所有的第三方應用改為自動下載。

CHANGELOGS.md

@@ -1,3 +1,13 @@
+2020-06-22 (0.7.5)
+========================
+* 移除方向盤監控設定。(無法取消/關閉方向盤監控)
+* Removed Steering monitor timer adjustment in code (timer in settings will no longer work.)
+
+2020-06-18 (0.7.5)
+========================
+* Dynamic Follow 更新模型。(感謝 @ShaneSmiskol 提供代碼、 @cgw1968 測試)
+* Updated Dynamic Follow model. (Special Thanks to @ShaneSmiskol for the feature and @cgw1968 for testing)
+
 2020-05-30 (0.7.5)
 ========================
 * Dynamic Follow 更新至 ShaneSmiskol:stock_additions 0.7.5 版。(感謝 @ShaneSmiskol 提供代碼、 @Wei 測試)

selfdrive/controls/lib/driver_monitor.py

@@ -117,9 +117,6 @@ class DriverStatus():
     self.is_rhd_region = False
     self.is_rhd_region_checked = False
 
-    # dp
-    self.awareness_time = _AWARENESS_TIME
-
     self._set_timers(active_monitoring=True)
 
   def _set_timers(self, active_monitoring):
@@ -147,9 +144,9 @@ class DriverStatus():
         self.awareness_active = self.awareness
         self.awareness = self.awareness_passive
 
-      self.threshold_pre = _AWARENESS_PRE_TIME_TILL_TERMINAL / self.awareness_time
-      self.threshold_prompt = _AWARENESS_PROMPT_TIME_TILL_TERMINAL / self.awareness_time
-      self.step_change = DT_DMON / self.awareness_time
+      self.threshold_pre = _AWARENESS_PRE_TIME_TILL_TERMINAL / _AWARENESS_TIME
+      self.threshold_prompt = _AWARENESS_PROMPT_TIME_TILL_TERMINAL / _AWARENESS_TIME
+      self.step_change = DT_DMON / _AWARENESS_TIME
       self.active_monitoring_mode = False
 
   def _is_driver_distracted(self, pose, blink):

selfdrive/controls/lib/dynamic_follow/__init__.py

@@ -28,10 +28,10 @@ class DynamicFollow:
 
     # Model variables
     mpc_rate = 1 / 20.
-    self.model_scales = {'v_ego': [-0.06112159043550491, 37.96522521972656], 'v_lead': [0.0, 35.27671432495117], 'x_lead': [2.4600000381469727, 139.52000427246094]}
+    self.model_scales = {'v_ego': [-0.06112159043550491, 37.96522521972656], 'a_lead': [-3.109330892562866, 3.3612186908721924], 'v_lead': [0.0, 35.27671432495117], 'x_lead': [2.4600000381469727, 141.44000244140625]}
     self.predict_rate = 1 / 4.
-    self.skip_every = round(0.2 / mpc_rate)
-    self.model_input_len = round(35 / mpc_rate)  # int: model input time
+    self.skip_every = round(0.25 / mpc_rate)
+    self.model_input_len = round(45 / mpc_rate)
 
     # Dynamic follow variables
     self.default_TR = 1.8
@@ -40,6 +40,9 @@ class DynamicFollow:
     self.v_ego_retention = 2.5
     self.v_rel_retention = 1.5
 
+    self.sng_TR = 1.8  # reacceleration stop and go TR
+    self.sng_speed = 18.0 * CV.MPH_TO_MS
+
     # dp params
     self.last_ts = 0.
     self.dp_df_profile = PROFILE_OFF
@@ -64,6 +67,7 @@ class DynamicFollow:
     self.last_cost = 0.0
     self.last_predict_time = 0.0
     self.auto_df_model_data = []
+    self._get_live_params()  # so they're defined just in case
 
   def update(self, CS, libmpc):
     self._get_live_params()
@@ -135,6 +139,7 @@ class DynamicFollow:
     # Store data for auto-df model
     self.auto_df_model_data.append([self._norm(self.car_data.v_ego, 'v_ego'),
                                     self._norm(self.lead_data.v_lead, 'v_lead'),
+                                    self._norm(self.lead_data.a_lead, 'a_lead'),
                                     self._norm(self.lead_data.x_lead, 'x_lead')])
     while len(self.auto_df_model_data) > self.model_input_len:
       del self.auto_df_model_data[0]
@@ -220,12 +225,27 @@ class DynamicFollow:
         return calc_mod
     return None
 
-  def global_profile_mod(self, TR, profile_mod_pos, profile_mod_neg):
-    if self.global_df_mod is not None:  # only apply when not in sng
-      TR *= self.global_df_mod
-      profile_mod_pos *= (1 - self.global_df_mod) + 1
-      profile_mod_neg *= self.global_df_mod
-    return TR, profile_mod_pos, profile_mod_neg
+  def global_profile_mod(self, profile_mod_x, profile_mod_pos, profile_mod_neg, x_vel, y_dist):
+    """
+    This function modifies the y_dist list used by dynamic follow in accordance with global_df_mod
+    It also intelligently adjusts the profile mods at each breakpoint based on the change in TR
+    """
+    if self.global_df_mod is None:
+      return profile_mod_pos, profile_mod_neg, y_dist
+    global_df_mod = 1 - self.global_df_mod
+
+    # Calculate new TRs
+    speeds = [0, self.sng_speed, 18, x_vel[-1]]  # [0, 18 mph, ~40 mph, highest profile mod speed (~78 mph)]
+    mods = [0, 0.1, 0.7, 1]  # how much to limit global_df_mod at each speed, 1 is full effect
+    y_dist_new = [y - (y * global_df_mod * np.interp(x, speeds, mods)) for x, y in zip(x_vel, y_dist)]
+
+    # Calculate how to change profile mods based on change in TR
+    # eg. if df mod is 0.7, then increase positive mod and decrease negative mod
+    calc_profile_mods = [(np.interp(mod_x, x_vel, y_dist) - np.interp(mod_x, x_vel, y_dist_new) + 1) for mod_x in profile_mod_x]
+    profile_mod_pos = [mod_pos * mod for mod_pos, mod in zip(profile_mod_pos, calc_profile_mods)]
+    profile_mod_neg = [mod_neg * ((1 - mod) + 1) for mod_neg, mod in zip(profile_mod_neg, calc_profile_mods)]
+
+    return profile_mod_pos, profile_mod_neg, y_dist_new
 
   def _get_TR(self):
     x_vel = [0.0, 1.8627, 3.7253, 5.588, 7.4507, 9.3133, 11.5598, 13.645, 22.352, 31.2928, 33.528, 35.7632, 40.2336]  # velocities
@@ -261,24 +281,23 @@ class DynamicFollow:
     else:
       raise Exception('Unknown profile type: {}'.format(df_profile))
 
+    # Global df mod
+    profile_mod_pos, profile_mod_neg, y_dist = self.global_profile_mod(profile_mod_x, profile_mod_pos, profile_mod_neg, x_vel, y_dist)
+
     # Profile modifications - Designed so that each profile reacts similarly to changing lead dynamics
     profile_mod_pos = interp(self.car_data.v_ego, profile_mod_x, profile_mod_pos)
     profile_mod_neg = interp(self.car_data.v_ego, profile_mod_x, profile_mod_neg)
 
-    sng_TR = 1.8  # reacceleration stop and go TR
-    sng_speed = 18.0 * CV.MPH_TO_MS
-
-    if self.car_data.v_ego > sng_speed:  # keep sng distance until we're above sng speed again
+    if self.car_data.v_ego > self.sng_speed:  # keep sng distance until we're above sng speed again
       self.sng = False
 
-    if (self.car_data.v_ego >= sng_speed or self.df_data.v_egos[0]['v_ego'] >= self.car_data.v_ego) and not self.sng:
+    if (self.car_data.v_ego >= self.sng_speed or self.df_data.v_egos[0]['v_ego'] >= self.car_data.v_ego) and not self.sng:
       # if above 15 mph OR we're decelerating to a stop, keep shorter TR. when we reaccelerate, use sng_TR and slowly decrease
       TR = interp(self.car_data.v_ego, x_vel, y_dist)
-      TR, profile_mod_pos, profile_mod_neg = self.global_profile_mod(TR, profile_mod_pos, profile_mod_neg)  # only within normal driving conditions
     else:  # this allows us to get closer to the lead car when stopping, while being able to have smooth stop and go when reaccelerating
       self.sng = True
-      x = [sng_speed * 0.7, sng_speed]  # decrease TR between 12.6 and 18 mph from 1.8s to defined TR above at 18mph while accelerating
-      y = [sng_TR, interp(sng_speed, x_vel, y_dist)]
+      x = [self.sng_speed * 0.7, self.sng_speed]  # decrease TR between 12.6 and 18 mph from 1.8s to defined TR above at 18mph while accelerating
+      y = [self.sng_TR, interp(self.sng_speed, x_vel, y_dist)]
       TR = interp(self.car_data.v_ego, x, y)
 
     TR_mods = []
@@ -297,7 +316,7 @@ class DynamicFollow:
       if self.lead_data.v_lead - deadzone > self.car_data.v_ego:
        TR_mods.append(rel_accel_mod)
 
-    x = [sng_speed / 5.0, sng_speed]  # as we approach 0, apply x% more distance
+    x = [self.sng_speed / 5.0, self.sng_speed]  # as we approach 0, apply x% more distance
     y = [1.05, 1.0]
     profile_mod_pos *= interp(self.car_data.v_ego, x, y)  # but only for currently positive mods
 
@@ -306,9 +325,10 @@ class DynamicFollow:
 
     if self.car_data.left_blinker or self.car_data.right_blinker and df_profile != self.df_profiles.traffic:
       x = [8.9408, 22.352, 31.2928]  # 20, 50, 70 mph
-      y = [1.0, .75, .65]  # reduce TR when changing lanes
-      TR *= interp(self.car_data.v_ego, x, y)
-    return clip(TR, 0.9, 2.7)
+      y = [1.0, .75, .65]
+      TR *= interp(self.car_data.v_ego, x, y)  # reduce TR when changing lanes
+
+    return float(clip(TR, self.min_TR, 2.7))
 
   def update_lead(self, v_lead=None, a_lead=None, x_lead=None, status=False, new_lead=False):
     self.lead_data.v_lead = v_lead
@@ -329,4 +349,5 @@ class DynamicFollow:
   def _get_live_params(self):
     self.global_df_mod = None #self.op_params.get('global_df_mod', None)
     if self.global_df_mod is not None:
-      self.global_df_mod = np.clip(self.global_df_mod, 0.7, 1.1)
+      self.global_df_mod = clip(self.global_df_mod, 0.85, 1.2)
+    self.min_TR = 0.9  # default

selfdrive/controls/lib/dynamic_follow/auto_df.py

@@ -16,7 +16,5 @@ def predict(x):
   l1 = np.dot(l0, w[1]) + b[1]
   l1 = np.maximum(0, l1)
   l2 = np.dot(l1, w[2]) + b[2]
-  l2 = np.maximum(0, l2)
-  l3 = np.dot(l2, w[3]) + b[3]
-  l3 = softmax(l3)
-  return l3
+  l2 = softmax(l2)
+  return l2