def steer_reactive(P, tp, a, t, sx, infleA, str8ness):
if abs(a) > .6:
return steer_centeralign(P, tp, a)
maxsen = max(t)
if maxsen > 0 and abs(tp) < .99:
MaxSensorPos = t.index(maxsen)
MaxSensorAng = sangsrad[MaxSensorPos]
sensangF = -.9
aadj = MaxSensorAng * sensangF
if maxsen < 40:
ttp = MaxSensorAng * - P['s2sen'] / maxsen
else:
if str8ness < P['str8thresh'] and abs(infleA) > P['ignoreinfleA']:
ttp = -abs(infleA) / infleA
aadj = 0
else:
ttp = 0
senslimF = .031
ttp = snakeoil.clip(ttp, tp - senslimF, tp + senslimF)
else:
aadj = a
if tp:
ttp = .94 * abs(tp) / tp
else:
ttp = 0
sto = steer_centeralign(P, tp, aadj, ttp)
return speed_appropriate_steer(P, sto, sx)
def speed_appropriate_steer(P, sto, sx):
if sx > 0:
stmax = max(P['sxappropriatest1'] / math.sqrt(sx) - P['sxappropriatest2'],
P['safeatanyspeed'])
else:
stmax = 1
return snakeoil.clip(sto, -stmax, stmax)
def traffic_speed_adjustment(os, sx, ts, tsen):
global opHistory
if not opHistory:
opHistory = os
return 0
tsa = 0
mpn = 0
sn = min(os[17], os[18])
if sn > tsen[9] and tsen[9] > 0:
return 0
if sn < 15:
sn = min(sn, os[16], os[19])
if sn < 8:
sn = min(sn, os[15], os[20])
sn -= 5
if sn < 3:
opHistory = os
return -ts
opn = mpn + sn
mpp = mpn - sx / 180
sp = min(opHistory[17], opHistory[18])
if sp < 15:
sp = min(sp, os[16], os[19])
if sp < 8:
sp = min(sn, os[15], os[20])
sp -= 5
opHistory = os
opp = mpp + sp
osx = (opn - opp) * 180
osx = snakeoil.clip(osx, 0, 300)
if osx - sx > 0: return 0
max_tsa = osx - ts
max_worry = 80
full_serious = 20
if sn > max_worry:
seriousness = 0
elif sn < full_serious:
seriousness = 1
else:
seriousness = (max_worry - sn) / (max_worry - full_serious)
tsa = max_tsa * seriousness
tsa = snakeoil.clip(tsa, -ts, 0)
return tsa
def throttle_control(P, ts, sx, sl, sy, ang, steer):
if ts < 0:
tooslow = sx - ts
else:
okmaxspeed4steer = P['stst'] * (steer ** 2) - P['st'] * steer + P['stC']
if steer > P['fullstis']:
ts = P['fullstmaxsx']
else:
ts = min(ts, okmaxspeed4steer)
tooslow = ts - sx
ao = 2 / (1 + math.exp(-tooslow)) - 1
ao -= abs(sl) * P['slipdec']
spincut = P['spincutint'] - P['spincutslp'] * abs(sy)
spincut = snakeoil.clip(spincut, P['spincutclip'], 1)
ao *= spincut
ww = abs(ang) / P['wwlim']
wwcut = min(ww, .1)
if ts > 0 and sx > 5:
ao -= wwcut
if ao > .8: ao = 1
return ao
def drive(c, tick):
S, R, P = c.S.d, c.R.d, c.P
global target_speed
global lap
global prev_distance_from_start
global learn_final
global badness
badness = S['damage'] - badness
skid = skid_severity(P, S['wheelSpinVel'], S['speedX'])
if skid > 1:
badness += 15
if car_might_be_stuck(S['speedX'], S['angle'], S['trackPos']):
S['stucktimer'] = (S['stucktimer'] % 400) + 1
if car_is_stuck(S['speedX'], S['stucktimer'], S['angle'],
S['trackPos'], S['track'][9], target_speed):
badness += 100
R['brake'] = 0
if target_speed > 0:
target_speed = -40
else:
target_speed = 40
else:
S['stucktimer'] = 0
if S['z'] > 4:
badness += 20
infleX, infleA, straightness = track_sensor_analysis(S['track'], S['angle'])
if target_speed > 0:
if c.stage:
if not S['stucktimer']:
target_speed = speed_planning(P, S['track'],
S['speedX'], S['speedY'], R['steer'], S['angle'],
infleX, infleA)
target_speed += jump_speed_adjustment(S['z'])
if c.stage > 1:
target_speed += traffic_speed_adjustment(
S['opponents'], S['speedX'], target_speed, S['track'])
target_speed *= damage_speed_adjustment(S['damage'])
else:
if lap > 1 and T.usable_model:
target_speed = speed_planning(P, S['track'],
S['speedX'], S['speedY'], R['steer'], S['angle'],
infleX, infleA)
target_speed *= damage_speed_adjustment(S['damage'])
else:
target_speed = 50
target_speed = min(target_speed, 333)
caution = 1
if T.usable_model:
snow = T.section_in_now(S['distFromStart'])
snext = T.section_ahead(S['distFromStart'])
if snow:
if snow.badness > 100: caution = .80
if snow.badness > 1000: caution = .65
if snow.badness > 10000: caution = .4
if snext:
if snow.end - S['distFromStart'] < 200:
if snext.badness > 100: caution = .90
if snext.badness > 1000: caution = .75
if snext.badness > 10000: caution = .5
target_speed *= caution
if T.usable_model or c.stage > 1:
if abs(S['trackPos']) > 1:
s = steer_centeralign(P, S['trackPos'], S['angle'])
badness += 1
else:
s = steer_reactive(P, S['trackPos'], S['angle'], S['track'],
S['speedX'], infleA, straightness)
else:
s = steer_centeralign(P, S['trackPos'], S['angle'])
R['steer'] = s
if S['stucktimer'] and S['distRaced'] > 20:
if target_speed < 0:
R['steer'] = -S['angle']
if c.stage > 1:
if target_speed < 0:
target_speed *= snakeoil.clip(S['opponents'][0] / 20, .1, 1)
target_speed *= snakeoil.clip(S['opponents'][35] / 20, .1, 1)
else:
R['steer'] = speed_appropriate_steer(P,
traffic_navigation(S['opponents'], R['steer']),
S['speedX'] + 50)
if not S['stucktimer']:
target_speed = abs(target_speed)
slip = find_slip(S['wheelSpinVel'])
R['accel'] = throttle_control(P, target_speed, S['speedX'], slip,
S['speedY'], S['angle'], R['steer'])
if R['accel'] < .01:
R['brake'] = brake_control(P, R['brake'], S['speedX'], S['speedY'], target_speed, skid)
else:
R['brake'] = 0
R['gear'], R['clutch'] = automatic_transimission(P,
S['rpm'], S['gear'], R['clutch'], S['rpm'],
S['speedX'],
target_speed, tick)
R['clutch'] = clutch_control(P, R['clutch'], slip, S['speedX'], S['speedY'])
if S['distRaced'] < S['distFromStart']:
lap = 0
if prev_distance_from_start > S['distFromStart'] and abs(S['angle']) < .1:
lap += 1
prev_distance_from_start = S['distFromStart']
if not lap:
T.laplength = max(S['distFromStart'], T.laplength)
elif lap == 1 and not T.usable_model:
learn_track(R['steer'], S['angle'], S['track'], S['distFromStart'])
elif c.stage == 3:
pass
else:
if not learn_final:
learn_track_final(T.laplength)
T.post_process_track()
learn_final = True
if T.laplength:
properlap = S['distRaced'] / T.laplength
else:
properlap = 0
if c.stage == 0 and lap < 4:
T.record_badness(badness, S['distFromStart'])
S['targetSpeed'] = target_speed
target_speed = 70
badness = S['damage']
return
def jump_speed_adjustment(z):
offtheground = snakeoil.clip(z - .350, 0, 1000)
jsa = offtheground * -800
return jsa