Merge remote-tracking branch 'upstream/master' into globally-fix-buttons-transitioning-without-going-unpressed

RELEASES.md

@@ -6,8 +6,9 @@ Version 0.8.17 (2022-11-21)
   * Trained in new reprojective simulator
   * Trained in 36hrs from scratch, compared to one week for previous releases
   * Training now simulates both lateral and longitudinal behavior, which allows openpilot to slow down for turns, stop at traffic lights, and more in experimental mode
-* New driver monitoring model
-  * New end-to-end distracted trigger
+* Driver monitoring updates
+  * New bigger model with added end-to-end distracted trigger
+  * Reduced false positives during driver calibration
 * Experimental driving mode
   * End-to-end longitudinal control
   * Stops for traffic lights and stop signs

panda

@@ -1 +1 @@
-Subproject commit 281eb7731b4338fef049977593fdf3315adf09e9
+Subproject commit d57311126860c9a87edf5b7b9f81a2a038866a26

selfdrive/boardd/panda_comms.h

@@ -34,7 +34,7 @@ public:
   virtual int bulk_read(unsigned char endpoint, unsigned char* data, int length, unsigned int timeout=TIMEOUT) = 0;
 
 protected:
-  std::mutex hw_lock;
+  std::recursive_mutex hw_lock;
 };
 
 class PandaUsbHandle : public PandaCommsHandle {
@@ -74,6 +74,7 @@ private:
   uint8_t rx_buf[SPI_BUF_SIZE];
 
   int wait_for_ack(spi_ioc_transfer &transfer, uint8_t ack);
+  int bulk_transfer(uint8_t endpoint, uint8_t *tx_data, uint16_t tx_len, uint8_t *rx_data, uint16_t rx_len);
   int spi_transfer(uint8_t endpoint, uint8_t *tx_data, uint16_t tx_len, uint8_t *rx_data, uint16_t max_rx_len);
   int spi_transfer_retry(uint8_t endpoint, uint8_t *tx_data, uint16_t tx_len, uint8_t *rx_data, uint16_t max_rx_len);
 };

selfdrive/boardd/spi.cc

@@ -2,6 +2,7 @@
 #include <linux/spi/spidev.h>
 
 #include <cassert>
+#include <cmath>
 #include <cstring>
 
 #include "common/util.h"
@@ -99,13 +100,45 @@ int PandaSpiHandle::control_read(uint8_t request, uint16_t param1, uint16_t para
 }
 
 int PandaSpiHandle::bulk_write(unsigned char endpoint, unsigned char* data, int length, unsigned int timeout) {
-  return 0;
+  return bulk_transfer(endpoint, data, length, NULL, 0);
 }
-
 int PandaSpiHandle::bulk_read(unsigned char endpoint, unsigned char* data, int length, unsigned int timeout) {
-  return 0;
+  return bulk_transfer(endpoint, NULL, 0, data, length);
+}
+
+int PandaSpiHandle::bulk_transfer(uint8_t endpoint, uint8_t *tx_data, uint16_t tx_len, uint8_t *rx_data, uint16_t rx_len) {
+  std::lock_guard lk(hw_lock);
+
+  const int xfer_size = 0x40;
+
+  int ret = 0;
+  uint16_t length = (tx_data != NULL) ? tx_len : rx_len;
+  for (int i = 0; i < (int)std::ceil((float)length / xfer_size); i++) {
+    int d;
+    if (tx_data != NULL) {
+      int len = std::min(xfer_size, tx_len - (xfer_size * i));
+      d = spi_transfer_retry(endpoint, tx_data + (xfer_size * i), len, NULL, 0);
+    } else {
+      d = spi_transfer_retry(endpoint, NULL, 0, rx_data + (xfer_size * i), xfer_size);
+    }
+
+    if (d < 0) {
+      LOGE("SPI: bulk transfer failed with %d", d);
+      comms_healthy = false;
+      return -1;
+    }
+
+    ret += d;
+    if ((rx_data != NULL) && d < xfer_size) {
+      break;
+    }
+  }
+
+  return ret;
 }
 
+
+
 std::vector<std::string> PandaSpiHandle::list() {
   // TODO: list all pandas available over SPI
   return {};
@@ -130,15 +163,15 @@ bool check_checksum(uint8_t *data, int data_len) {
 
 
 int PandaSpiHandle::spi_transfer_retry(uint8_t endpoint, uint8_t *tx_data, uint16_t tx_len, uint8_t *rx_data, uint16_t max_rx_len) {
-  int err;
+  int ret;
 
   std::lock_guard lk(hw_lock);
   do {
     // TODO: handle error
-    err = spi_transfer(endpoint, tx_data, tx_len, rx_data, max_rx_len);
-  } while (err < 0 && connected && !PANDA_NO_RETRY);
+    ret = spi_transfer(endpoint, tx_data, tx_len, rx_data, max_rx_len);
+  } while (ret < 0 && connected && !PANDA_NO_RETRY);
 
-  return err;
+  return ret;
 }
 
 int PandaSpiHandle::wait_for_ack(spi_ioc_transfer &transfer, uint8_t ack) {
@@ -153,7 +186,7 @@ int PandaSpiHandle::wait_for_ack(spi_ioc_transfer &transfer, uint8_t ack) {
     if (rx_buf[0] == ack) {
       break;
     } else if (rx_buf[0] == SPI_NACK) {
-      LOGW("SPI: got header NACK");
+      LOGW("SPI: got NACK");
       return -1;
     }
   }

selfdrive/controls/lib/events.py

@@ -597,6 +597,7 @@ EVENTS: Dict[int, Dict[str, Union[Alert, AlertCallbackType]]] = {
 
   EventName.buttonCancel: {
     ET.USER_DISABLE: EngagementAlert(AudibleAlert.disengage),
+    ET.NO_ENTRY: NoEntryAlert("Cancel Pressed"),
   },
 
   EventName.brakeHold: {

selfdrive/controls/lib/longitudinal_planner.py

@@ -69,7 +69,8 @@ class LongitudinalPlanner:
     e2e = self.params.get_bool('ExperimentalMode') and self.CP.openpilotLongitudinalControl
     self.mpc.mode = 'blended' if e2e else 'acc'
 
-  def parse_model(self, model_msg, model_error):
+  @staticmethod
+  def parse_model(model_msg, model_error):
     if (len(model_msg.position.x) == 33 and
        len(model_msg.velocity.x) == 33 and
        len(model_msg.acceleration.x) == 33):
@@ -107,6 +108,7 @@ class LongitudinalPlanner:
       accel_limits = [A_CRUISE_MIN, get_max_accel(v_ego)]
       accel_limits_turns = limit_accel_in_turns(v_ego, sm['carState'].steeringAngleDeg, accel_limits, self.CP)
     else:
+      accel_limits = [MIN_ACCEL, MAX_ACCEL]
       accel_limits_turns = [MIN_ACCEL, MAX_ACCEL]
 
     if reset_state:

selfdrive/test/longitudinal_maneuvers/maneuver.py

@@ -17,6 +17,7 @@ class Maneuver():
     self.only_lead2 = kwargs.get("only_lead2", False)
     self.only_radar = kwargs.get("only_radar", False)
     self.ensure_start = kwargs.get("ensure_start", False)
+    self.enabled = kwargs.get("enabled", True)
 
     self.duration = duration
     self.title = title
@@ -26,23 +27,24 @@ class Maneuver():
       lead_relevancy=self.lead_relevancy,
       speed=self.speed,
       distance_lead=self.distance_lead,
+      enabled=self.enabled,
       only_lead2=self.only_lead2,
       only_radar=self.only_radar,
     )
 
     valid = True
     logs = []
-    while plant.current_time() < self.duration:
-      speed_lead = np.interp(plant.current_time(), self.breakpoints, self.speed_lead_values)
-      prob = np.interp(plant.current_time(), self.breakpoints, self.prob_lead_values)
-      cruise = np.interp(plant.current_time(), self.breakpoints, self.cruise_values)
+    while plant.current_time < self.duration:
+      speed_lead = np.interp(plant.current_time, self.breakpoints, self.speed_lead_values)
+      prob = np.interp(plant.current_time, self.breakpoints, self.prob_lead_values)
+      cruise = np.interp(plant.current_time, self.breakpoints, self.cruise_values)
       log = plant.step(speed_lead, prob, cruise)
 
       d_rel = log['distance_lead'] - log['distance'] if self.lead_relevancy else 200.
       v_rel = speed_lead - log['speed'] if self.lead_relevancy else 0.
       log['d_rel'] = d_rel
       log['v_rel'] = v_rel
-      logs.append(np.array([plant.current_time(),
+      logs.append(np.array([plant.current_time,
                             log['distance'],
                             log['distance_lead'],
                             log['speed'],

selfdrive/test/longitudinal_maneuvers/plant.py

@@ -10,11 +10,12 @@ from selfdrive.modeld.constants import T_IDXS
 from selfdrive.controls.lib.longitudinal_planner import LongitudinalPlanner
 from selfdrive.controls.lib.radar_helpers import _LEAD_ACCEL_TAU
 
-class Plant():
+
+class Plant:
   messaging_initialized = False
 
   def __init__(self, lead_relevancy=False, speed=0.0, distance_lead=2.0,
-               only_lead2=False, only_radar=False):
+               enabled=True, only_lead2=False, only_radar=False):
     self.rate = 1. / DT_MDL
 
     if not Plant.messaging_initialized:
@@ -32,10 +33,11 @@ class Plant():
     self.speeds = []
 
     # lead car
-    self.distance_lead = distance_lead
     self.lead_relevancy = lead_relevancy
-    self.only_lead2=only_lead2
-    self.only_radar=only_radar
+    self.distance_lead = distance_lead
+    self.enabled = enabled
+    self.only_lead2 = only_lead2
+    self.only_radar = only_radar
 
     self.rk = Ratekeeper(self.rate, print_delay_threshold=100.0)
     self.ts = 1. / self.rate
@@ -47,6 +49,7 @@ class Plant():
 
     self.planner = LongitudinalPlanner(CarInterface.get_params(CAR.CIVIC), init_v=self.speed)
 
+  @property
   def current_time(self):
     return float(self.rk.frame) / self.rate
 
@@ -104,9 +107,7 @@ class Plant():
     acceleration.x = [float(x) for x in np.zeros_like(T_IDXS)]
     model.modelV2.acceleration = acceleration
 
-
-
-    control.controlsState.longControlState = LongCtrlState.pid
+    control.controlsState.longControlState = LongCtrlState.pid if self.enabled else LongCtrlState.off
     control.controlsState.vCruise = float(v_cruise * 3.6)
     car_state.carState.vEgo = float(self.speed)
     car_state.carState.standstill = self.speed < 0.01
@@ -141,7 +142,7 @@ class Plant():
     # print at 5hz
     if (self.rk.frame % (self.rate // 5)) == 0:
       print("%2.2f sec   %6.2f m  %6.2f m/s  %6.2f m/s2   lead_rel: %6.2f m  %6.2f m/s"
-            % (self.current_time(), self.distance, self.speed, self.acceleration, d_rel, v_rel))
+            % (self.current_time, self.distance, self.speed, self.acceleration, d_rel, v_rel))
 
 
     # ******** update prevs ********

selfdrive/test/longitudinal_maneuvers/test_longitudinal.py

@@ -10,7 +10,7 @@ from selfdrive.test.longitudinal_maneuvers.maneuver import Maneuver
 # TODO: make new FCW tests
 maneuvers = [
   Maneuver(
-    'approach stopped car at 20m/s, initial distance: 120m',
+    'approach stopped car at 25m/s, initial distance: 120m',
     duration=20.,
     initial_speed=25.,
     lead_relevancy=True,
@@ -118,6 +118,13 @@ maneuvers = [
     breakpoints=[1., 10., 15.],
     ensure_start=True,
   ),
+  Maneuver(
+    'cruising at 25 m/s while disabled',
+    duration=20.,
+    initial_speed=25.,
+    lead_relevancy=False,
+    enabled=False,
+  ),
 ]
 
 

selfdrive/test/process_replay/ref_commit

@@ -1 +1 @@
-9e37e8ee8013515cfd75cf352a1a2b9aa447c441
+b5c833a8f5b3e6202a52746fc16809c7b649d591