Custom driving personality profiles

Added toggles to customize the driving personality profiles.
This commit is contained in:
FrogAi
2024-01-12 22:39:30 -07:00
parent ccf674762e
commit fb8103b646
7 changed files with 109 additions and 22 deletions

View File

@@ -56,26 +56,46 @@ T_DIFFS = np.diff(T_IDXS, prepend=[0.])
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
def get_jerk_factor(custom_personalities, aggressive_jerk, standard_jerk, relaxed_jerk, personality=log.LongitudinalPersonality.standard):
if custom_personalities:
if personality==log.LongitudinalPersonality.relaxed:
return relaxed_jerk
elif personality==log.LongitudinalPersonality.standard:
return standard_jerk
elif personality==log.LongitudinalPersonality.aggressive:
return aggressive_jerk
else:
raise NotImplementedError("Longitudinal personality not supported")
else:
raise NotImplementedError("Longitudinal personality not supported")
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
def get_T_FOLLOW(custom_personalities=False, aggressive_follow=1.25, standard_follow=1.45, relaxed_follow=1.75, personality=log.LongitudinalPersonality.standard):
if custom_personalities:
if personality==log.LongitudinalPersonality.relaxed:
return relaxed_follow
elif personality==log.LongitudinalPersonality.standard:
return standard_follow
elif personality==log.LongitudinalPersonality.aggressive:
return aggressive_follow
else:
raise NotImplementedError("Longitudinal personality not supported")
else:
raise NotImplementedError("Longitudinal personality not supported")
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)
@@ -272,8 +292,8 @@ class LongitudinalMpc:
for i in range(N):
self.solver.cost_set(i, 'Zl', Zl)
def set_weights(self, prev_accel_constraint=True, personality=log.LongitudinalPersonality.standard):
jerk_factor = get_jerk_factor(personality)
def set_weights(self, custom_personalities, aggressive_jerk, standard_jerk, relaxed_jerk, prev_accel_constraint=True, personality=log.LongitudinalPersonality.standard):
jerk_factor = get_jerk_factor(custom_personalities, aggressive_jerk, standard_jerk, relaxed_jerk, 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, jerk_factor * a_change_cost, jerk_factor * J_EGO_COST]
@@ -331,8 +351,8 @@ class LongitudinalMpc:
self.cruise_min_a = min_a
self.max_a = max_a
def update(self, radarstate, v_cruise, x, v, a, j, aggressive_acceleration, personality=log.LongitudinalPersonality.standard):
t_follow = get_T_FOLLOW(personality)
def update(self, radarstate, v_cruise, x, v, a, j, aggressive_acceleration, custom_personalities, aggressive_follow, standard_follow, relaxed_follow, personality=log.LongitudinalPersonality.standard):
t_follow = get_T_FOLLOW(custom_personalities, aggressive_follow, standard_follow, relaxed_follow, personality)
self.t_follow = t_follow
v_ego = self.x0[1]
self.status = radarstate.leadOne.status or radarstate.leadTwo.status