pipeline works from pi simulation to control output and strategy generation.
This commit is contained in:
Aditya Pulipaka
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -4,147 +4,105 @@ from typing import Dict, Any
|
||||
|
||||
|
||||
def normalize_telemetry(payload: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Normalize Pi/FastF1-like telemetry payload to Enricher expected schema.
|
||||
"""Normalize lap-level telemetry payload from Pi stream to Enricher schema.
|
||||
|
||||
Accepted aliases:
|
||||
- speed: Speed
|
||||
- throttle: Throttle
|
||||
- brake: Brake, Brakes
|
||||
- tire_compound: Compound, TyreCompound, Tire
|
||||
- fuel_level: Fuel, FuelRel, FuelLevel
|
||||
- ers: ERS, ERSCharge
|
||||
- track_temp: TrackTemp, track_temperature
|
||||
- rain_probability: RainProb, PrecipProb
|
||||
- lap: Lap, LapNumber, lap_number
|
||||
Accepted aliases for lap-level data:
|
||||
- lap_number: lap, Lap, LapNumber, lap_number
|
||||
- total_laps: TotalLaps, total_laps
|
||||
- track_name: TrackName, track_name, Circuit
|
||||
- driver_name: DriverName, driver_name, Driver
|
||||
- current_position: Position, current_position
|
||||
- tire_life_laps: TireAge, tire_age, tire_life_laps
|
||||
- rainfall: Rainfall, rainfall, Rain
|
||||
- lap_time: lap_time, LapTime, Time
|
||||
- average_speed: average_speed, avg_speed, AvgSpeed
|
||||
- max_speed: max_speed, MaxSpeed, max
|
||||
- tire_compound: tire_compound, Compound, TyreCompound, Tire
|
||||
- tire_life_laps: tire_life_laps, TireAge, tire_age
|
||||
- track_temperature: track_temperature, TrackTemp, track_temp
|
||||
- rainfall: rainfall, Rainfall, Rain
|
||||
|
||||
Values are clamped and defaulted if missing.
|
||||
Returns normalized dict ready for enrichment layer.
|
||||
"""
|
||||
aliases = {
|
||||
"lap": ["lap", "Lap", "LapNumber", "lap_number"],
|
||||
"speed": ["speed", "Speed"],
|
||||
"throttle": ["throttle", "Throttle"],
|
||||
"brake": ["brake", "Brake", "Brakes"],
|
||||
"tire_compound": ["tire_compound", "Compound", "TyreCompound", "Tire"],
|
||||
"fuel_level": ["fuel_level", "Fuel", "FuelRel", "FuelLevel"],
|
||||
"ers": ["ers", "ERS", "ERSCharge"],
|
||||
"track_temp": ["track_temp", "TrackTemp", "track_temperature"],
|
||||
"rain_probability": ["rain_probability", "RainProb", "PrecipProb"],
|
||||
"lap_number": ["lap_number", "lap", "Lap", "LapNumber"],
|
||||
"total_laps": ["total_laps", "TotalLaps"],
|
||||
"track_name": ["track_name", "TrackName", "Circuit"],
|
||||
"driver_name": ["driver_name", "DriverName", "Driver"],
|
||||
"current_position": ["current_position", "Position"],
|
||||
"lap_time": ["lap_time", "LapTime", "Time"],
|
||||
"average_speed": ["average_speed", "avg_speed", "AvgSpeed"],
|
||||
"max_speed": ["max_speed", "MaxSpeed", "max"],
|
||||
"tire_compound": ["tire_compound", "Compound", "TyreCompound", "Tire"],
|
||||
"tire_life_laps": ["tire_life_laps", "TireAge", "tire_age"],
|
||||
"track_temperature": ["track_temperature", "TrackTemp", "track_temp"],
|
||||
"rainfall": ["rainfall", "Rainfall", "Rain"],
|
||||
}
|
||||
|
||||
out: Dict[str, Any] = {}
|
||||
|
||||
def pick(key: str, default=None):
|
||||
"""Pick first matching alias from payload."""
|
||||
for k in aliases.get(key, [key]):
|
||||
if k in payload and payload[k] is not None:
|
||||
return payload[k]
|
||||
return default
|
||||
|
||||
def clamp01(x, default=0.0):
|
||||
try:
|
||||
v = float(x)
|
||||
except (TypeError, ValueError):
|
||||
return default
|
||||
return max(0.0, min(1.0, v))
|
||||
|
||||
# Map values with sensible defaults
|
||||
lap = pick("lap", 0)
|
||||
# Extract and validate lap-level fields
|
||||
lap_number = pick("lap_number", 0)
|
||||
try:
|
||||
lap = int(lap)
|
||||
lap_number = int(lap_number)
|
||||
except (TypeError, ValueError):
|
||||
lap = 0
|
||||
lap_number = 0
|
||||
|
||||
speed = pick("speed", 0.0)
|
||||
total_laps = pick("total_laps", 51)
|
||||
try:
|
||||
speed = float(speed)
|
||||
total_laps = int(total_laps)
|
||||
except (TypeError, ValueError):
|
||||
speed = 0.0
|
||||
total_laps = 51
|
||||
|
||||
throttle = clamp01(pick("throttle", 0.0), 0.0)
|
||||
brake = clamp01(pick("brake", 0.0), 0.0)
|
||||
lap_time = pick("lap_time", None)
|
||||
if lap_time:
|
||||
out["lap_time"] = str(lap_time)
|
||||
|
||||
average_speed = pick("average_speed", 0.0)
|
||||
try:
|
||||
average_speed = float(average_speed)
|
||||
except (TypeError, ValueError):
|
||||
average_speed = 0.0
|
||||
|
||||
max_speed = pick("max_speed", 0.0)
|
||||
try:
|
||||
max_speed = float(max_speed)
|
||||
except (TypeError, ValueError):
|
||||
max_speed = 0.0
|
||||
|
||||
tire_compound = pick("tire_compound", "medium")
|
||||
if isinstance(tire_compound, str):
|
||||
tire_compound = tire_compound.lower()
|
||||
tire_compound = tire_compound.upper() # Keep uppercase for consistency
|
||||
else:
|
||||
tire_compound = "medium"
|
||||
tire_compound = "MEDIUM"
|
||||
|
||||
fuel_level = clamp01(pick("fuel_level", 0.5), 0.5)
|
||||
|
||||
ers = pick("ers", None)
|
||||
if ers is not None:
|
||||
ers = clamp01(ers, None)
|
||||
|
||||
track_temp = pick("track_temp", None)
|
||||
tire_life_laps = pick("tire_life_laps", 0)
|
||||
try:
|
||||
track_temp = float(track_temp) if track_temp is not None else None
|
||||
tire_life_laps = int(tire_life_laps)
|
||||
except (TypeError, ValueError):
|
||||
track_temp = None
|
||||
tire_life_laps = 0
|
||||
|
||||
rain_prob = pick("rain_probability", None)
|
||||
track_temperature = pick("track_temperature", 25.0)
|
||||
try:
|
||||
rain_prob = clamp01(rain_prob, None) if rain_prob is not None else None
|
||||
except Exception:
|
||||
rain_prob = None
|
||||
track_temperature = float(track_temperature)
|
||||
except (TypeError, ValueError):
|
||||
track_temperature = 25.0
|
||||
|
||||
rainfall = pick("rainfall", False)
|
||||
try:
|
||||
rainfall = bool(rainfall)
|
||||
except (TypeError, ValueError):
|
||||
rainfall = False
|
||||
|
||||
# Build normalized output
|
||||
out.update({
|
||||
"lap": lap,
|
||||
"speed": speed,
|
||||
"throttle": throttle,
|
||||
"brake": brake,
|
||||
"lap_number": lap_number,
|
||||
"total_laps": total_laps,
|
||||
"average_speed": average_speed,
|
||||
"max_speed": max_speed,
|
||||
"tire_compound": tire_compound,
|
||||
"fuel_level": fuel_level,
|
||||
"tire_life_laps": tire_life_laps,
|
||||
"track_temperature": track_temperature,
|
||||
"rainfall": rainfall,
|
||||
})
|
||||
if ers is not None:
|
||||
out["ers"] = ers
|
||||
if track_temp is not None:
|
||||
out["track_temp"] = track_temp
|
||||
if rain_prob is not None:
|
||||
out["rain_probability"] = rain_prob
|
||||
|
||||
# Add race context fields if present
|
||||
total_laps = pick("total_laps", None)
|
||||
if total_laps is not None:
|
||||
try:
|
||||
out["total_laps"] = int(total_laps)
|
||||
except (TypeError, ValueError):
|
||||
pass
|
||||
|
||||
track_name = pick("track_name", None)
|
||||
if track_name:
|
||||
out["track_name"] = str(track_name)
|
||||
|
||||
driver_name = pick("driver_name", None)
|
||||
if driver_name:
|
||||
out["driver_name"] = str(driver_name)
|
||||
|
||||
current_position = pick("current_position", None)
|
||||
if current_position is not None:
|
||||
try:
|
||||
out["current_position"] = int(current_position)
|
||||
except (TypeError, ValueError):
|
||||
pass
|
||||
|
||||
tire_life_laps = pick("tire_life_laps", None)
|
||||
if tire_life_laps is not None:
|
||||
try:
|
||||
out["tire_life_laps"] = int(tire_life_laps)
|
||||
except (TypeError, ValueError):
|
||||
pass
|
||||
|
||||
rainfall = pick("rainfall", None)
|
||||
if rainfall is not None:
|
||||
out["rainfall"] = bool(rainfall)
|
||||
|
||||
return out
|
||||
|
||||
@@ -25,24 +25,24 @@ _CALLBACK_URL = os.getenv("NEXT_STAGE_CALLBACK_URL")
|
||||
|
||||
|
||||
class EnrichedRecord(BaseModel):
|
||||
"""Lap-level enriched telemetry model."""
|
||||
lap: int
|
||||
aero_efficiency: float
|
||||
tire_degradation_index: float
|
||||
ers_charge: float
|
||||
fuel_optimization_score: float
|
||||
driver_consistency: float
|
||||
weather_impact: str
|
||||
tire_degradation_rate: float
|
||||
pace_trend: str
|
||||
tire_cliff_risk: float
|
||||
optimal_pit_window: List[int]
|
||||
performance_delta: float
|
||||
|
||||
|
||||
@app.post("/ingest/telemetry")
|
||||
async def ingest_telemetry(payload: Dict[str, Any] = Body(...)):
|
||||
"""Receive raw telemetry (from Pi), normalize, enrich, return enriched with race context.
|
||||
"""Receive raw lap-level telemetry (from Pi), normalize, enrich, return enriched with race context.
|
||||
|
||||
Optionally forwards to NEXT_STAGE_CALLBACK_URL if set.
|
||||
"""
|
||||
try:
|
||||
normalized = normalize_telemetry(payload)
|
||||
result = _enricher.enrich_with_context(normalized)
|
||||
result = _enricher.enrich_lap_data(normalized)
|
||||
enriched = result["enriched_telemetry"]
|
||||
race_context = result["race_context"]
|
||||
except Exception as e:
|
||||
@@ -85,3 +85,12 @@ async def list_enriched(limit: int = 50):
|
||||
@app.get("/healthz")
|
||||
async def healthz():
|
||||
return {"status": "ok", "stored": len(_recent)}
|
||||
|
||||
|
||||
@app.post("/reset")
|
||||
async def reset_enricher():
|
||||
"""Reset enricher state for a new session/race."""
|
||||
global _enricher
|
||||
_enricher = Enricher()
|
||||
_recent.clear()
|
||||
return {"status": "reset", "message": "Enricher state and buffer cleared"}
|
||||
|
||||
@@ -2,370 +2,254 @@ from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, Any, Optional, List
|
||||
import math
|
||||
import pandas as pd
|
||||
|
||||
|
||||
# --- Contracts ---
|
||||
# Input telemetry (example, extensible):
|
||||
# --- LAP-LEVEL TELEMETRY CONTRACT ---
|
||||
# Input from Raspberry Pi (lap-level data):
|
||||
# {
|
||||
# "lap": 27,
|
||||
# "speed": 282, # km/h
|
||||
# "throttle": 0.91, # 0..1
|
||||
# "brake": 0.05, # 0..1
|
||||
# "tire_compound": "medium",# soft|medium|hard|inter|wet
|
||||
# "fuel_level": 0.47, # 0..1 (fraction of race fuel)
|
||||
# "ers": 0.72, # optional 0..1
|
||||
# "track_temp": 38, # optional Celsius
|
||||
# "rain_probability": 0.2 # optional 0..1
|
||||
#
|
||||
# # Additional fields for race context:
|
||||
# "track_name": "Monza", # optional
|
||||
# "total_laps": 51, # optional
|
||||
# "driver_name": "Alonso", # optional
|
||||
# "current_position": 5, # optional
|
||||
# "tire_life_laps": 12, # optional (tire age)
|
||||
# "rainfall": False # optional (boolean)
|
||||
# }
|
||||
#
|
||||
# Output enrichment + race context:
|
||||
# {
|
||||
# "enriched_telemetry": {
|
||||
# "lap": 27,
|
||||
# "aero_efficiency": 0.83,
|
||||
# "tire_degradation_index": 0.65,
|
||||
# "ers_charge": 0.72,
|
||||
# "fuel_optimization_score": 0.91,
|
||||
# "driver_consistency": 0.89,
|
||||
# "weather_impact": "low|medium|high"
|
||||
# },
|
||||
# "race_context": {
|
||||
# "race_info": {...},
|
||||
# "driver_state": {...},
|
||||
# "competitors": [...]
|
||||
# }
|
||||
# "lap_number": 27,
|
||||
# "total_laps": 51,
|
||||
# "lap_time": "0 days 00:01:27.318000",
|
||||
# "average_speed": 234.62,
|
||||
# "max_speed": 333.0,
|
||||
# "tire_compound": "MEDIUM",
|
||||
# "tire_life_laps": 19,
|
||||
# "track_temperature": 43.6,
|
||||
# "rainfall": false
|
||||
# }
|
||||
|
||||
|
||||
_TIRES_BASE_WEAR = {
|
||||
"soft": 0.012,
|
||||
"medium": 0.008,
|
||||
"hard": 0.006,
|
||||
"inter": 0.015,
|
||||
"wet": 0.02,
|
||||
_TIRE_DEGRADATION_RATES = {
|
||||
"soft": 0.030, # Fast degradation
|
||||
"medium": 0.020, # Moderate degradation
|
||||
"hard": 0.015, # Slow degradation
|
||||
"inter": 0.025,
|
||||
"wet": 0.022,
|
||||
}
|
||||
|
||||
_TIRE_CLIFF_THRESHOLD = 25 # Laps before cliff risk increases significantly
|
||||
|
||||
|
||||
@dataclass
|
||||
class EnricherState:
|
||||
last_lap: Optional[int] = None
|
||||
lap_speeds: Dict[int, float] = field(default_factory=dict)
|
||||
lap_throttle_avg: Dict[int, float] = field(default_factory=dict)
|
||||
cumulative_wear: float = 0.0 # 0..1 approx
|
||||
|
||||
# Race context state
|
||||
track_name: str = "Unknown Circuit"
|
||||
total_laps: int = 50
|
||||
driver_name: str = "Driver"
|
||||
current_position: int = 10
|
||||
tire_compound_history: List[str] = field(default_factory=list)
|
||||
"""Maintains race state across laps for trend analysis."""
|
||||
lap_times: List[float] = field(default_factory=list) # Recent lap times in seconds
|
||||
lap_speeds: List[float] = field(default_factory=list) # Recent average speeds
|
||||
current_tire_age: int = 0
|
||||
current_tire_compound: str = "medium"
|
||||
tire_stint_start_lap: int = 1
|
||||
total_laps: int = 51
|
||||
track_name: str = "Monza"
|
||||
|
||||
|
||||
class Enricher:
|
||||
"""Heuristic enrichment engine to simulate HPC analytics on telemetry.
|
||||
|
||||
Stateless inputs are enriched with stateful estimates (wear, consistency, etc.).
|
||||
Designed for predictable, dependency-free behavior.
|
||||
"""
|
||||
HPC-simulated enrichment for lap-level F1 telemetry.
|
||||
|
||||
Accepts lap-level data from Raspberry Pi and generates performance insights
|
||||
that simulate HPC computational analysis.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.state = EnricherState()
|
||||
self._baseline_lap_time: Optional[float] = None # Best lap time as baseline
|
||||
|
||||
# --- Public API ---
|
||||
def enrich(self, telemetry: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Legacy method - returns only enriched telemetry metrics."""
|
||||
lap = int(telemetry.get("lap", 0))
|
||||
speed = float(telemetry.get("speed", 0.0))
|
||||
throttle = float(telemetry.get("throttle", 0.0))
|
||||
brake = float(telemetry.get("brake", 0.0))
|
||||
tire_compound = str(telemetry.get("tire_compound", "medium")).lower()
|
||||
fuel_level = float(telemetry.get("fuel_level", 0.5))
|
||||
ers = telemetry.get("ers")
|
||||
track_temp = telemetry.get("track_temp")
|
||||
rain_prob = telemetry.get("rain_probability")
|
||||
|
||||
# Update per-lap aggregates
|
||||
self._update_lap_stats(lap, speed, throttle)
|
||||
|
||||
# Metrics
|
||||
aero_eff = self._compute_aero_efficiency(speed, throttle, brake)
|
||||
tire_deg = self._compute_tire_degradation(lap, speed, throttle, tire_compound, track_temp)
|
||||
ers_charge = self._compute_ers_charge(ers, throttle, brake)
|
||||
fuel_opt = self._compute_fuel_optimization(fuel_level, throttle)
|
||||
consistency = self._compute_driver_consistency()
|
||||
weather_impact = self._compute_weather_impact(rain_prob, track_temp)
|
||||
|
||||
return {
|
||||
"lap": lap,
|
||||
"aero_efficiency": round(aero_eff, 3),
|
||||
"tire_degradation_index": round(tire_deg, 3),
|
||||
"ers_charge": round(ers_charge, 3),
|
||||
"fuel_optimization_score": round(fuel_opt, 3),
|
||||
"driver_consistency": round(consistency, 3),
|
||||
"weather_impact": weather_impact,
|
||||
}
|
||||
|
||||
def enrich_with_context(self, telemetry: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Enrich telemetry and build complete race context for AI layer."""
|
||||
# Extract all fields
|
||||
lap = int(telemetry.get("lap", telemetry.get("lap_number", 0)))
|
||||
speed = float(telemetry.get("speed", 0.0))
|
||||
throttle = float(telemetry.get("throttle", 0.0))
|
||||
brake = float(telemetry.get("brake", 0.0))
|
||||
tire_compound = str(telemetry.get("tire_compound", "medium")).lower()
|
||||
fuel_level = float(telemetry.get("fuel_level", 0.5))
|
||||
ers = telemetry.get("ers")
|
||||
track_temp = telemetry.get("track_temp", telemetry.get("track_temperature"))
|
||||
rain_prob = telemetry.get("rain_probability")
|
||||
rainfall = telemetry.get("rainfall", False)
|
||||
def enrich_lap_data(self, lap_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Main enrichment method for lap-level data.
|
||||
Returns enriched telemetry + race context for AI layer.
|
||||
"""
|
||||
# Extract lap data
|
||||
lap_number = int(lap_data.get("lap_number", 0))
|
||||
total_laps = int(lap_data.get("total_laps", 51))
|
||||
lap_time_str = lap_data.get("lap_time")
|
||||
average_speed = float(lap_data.get("average_speed", 0.0))
|
||||
max_speed = float(lap_data.get("max_speed", 0.0))
|
||||
tire_compound = str(lap_data.get("tire_compound", "MEDIUM")).lower()
|
||||
tire_life_laps = int(lap_data.get("tire_life_laps", 0))
|
||||
track_temperature = float(lap_data.get("track_temperature", 25.0))
|
||||
rainfall = bool(lap_data.get("rainfall", False))
|
||||
|
||||
# Race context fields
|
||||
track_name = telemetry.get("track_name", self.state.track_name)
|
||||
total_laps = int(telemetry.get("total_laps", self.state.total_laps))
|
||||
driver_name = telemetry.get("driver_name", self.state.driver_name)
|
||||
current_position = int(telemetry.get("current_position", self.state.current_position))
|
||||
tire_life_laps = int(telemetry.get("tire_life_laps", 0))
|
||||
# Convert lap time to seconds
|
||||
lap_time_seconds = self._parse_lap_time(lap_time_str)
|
||||
|
||||
# Update state
|
||||
self.state.lap_times.append(lap_time_seconds)
|
||||
self.state.lap_speeds.append(average_speed)
|
||||
self.state.current_tire_age = tire_life_laps
|
||||
self.state.current_tire_compound = tire_compound
|
||||
self.state.total_laps = total_laps
|
||||
|
||||
# Keep only last 10 laps for analysis
|
||||
if len(self.state.lap_times) > 10:
|
||||
self.state.lap_times = self.state.lap_times[-10:]
|
||||
self.state.lap_speeds = self.state.lap_speeds[-10:]
|
||||
|
||||
# Set baseline (best lap time)
|
||||
if self._baseline_lap_time is None or lap_time_seconds < self._baseline_lap_time:
|
||||
self._baseline_lap_time = lap_time_seconds
|
||||
|
||||
# Compute HPC-simulated insights
|
||||
tire_deg_rate = self._compute_tire_degradation_rate(tire_compound, tire_life_laps, track_temperature)
|
||||
pace_trend = self._compute_pace_trend()
|
||||
tire_cliff_risk = self._compute_tire_cliff_risk(tire_compound, tire_life_laps)
|
||||
pit_window = self._compute_optimal_pit_window(lap_number, total_laps, tire_life_laps, tire_compound)
|
||||
performance_delta = self._compute_performance_delta(lap_time_seconds)
|
||||
|
||||
# Update state with race context
|
||||
if track_name:
|
||||
self.state.track_name = track_name
|
||||
if total_laps:
|
||||
self.state.total_laps = total_laps
|
||||
if driver_name:
|
||||
self.state.driver_name = driver_name
|
||||
if current_position:
|
||||
self.state.current_position = current_position
|
||||
|
||||
# Track tire compound changes
|
||||
if tire_compound and (not self.state.tire_compound_history or
|
||||
self.state.tire_compound_history[-1] != tire_compound):
|
||||
self.state.tire_compound_history.append(tire_compound)
|
||||
|
||||
# Update per-lap aggregates
|
||||
self._update_lap_stats(lap, speed, throttle)
|
||||
|
||||
# Compute enriched metrics
|
||||
aero_eff = self._compute_aero_efficiency(speed, throttle, brake)
|
||||
tire_deg = self._compute_tire_degradation(lap, speed, throttle, tire_compound, track_temp)
|
||||
ers_charge = self._compute_ers_charge(ers, throttle, brake)
|
||||
fuel_opt = self._compute_fuel_optimization(fuel_level, throttle)
|
||||
consistency = self._compute_driver_consistency()
|
||||
weather_impact = self._compute_weather_impact(rain_prob, track_temp)
|
||||
|
||||
# Build enriched telemetry
|
||||
enriched_telemetry = {
|
||||
"lap": lap,
|
||||
"aero_efficiency": round(aero_eff, 3),
|
||||
"tire_degradation_index": round(tire_deg, 3),
|
||||
"ers_charge": round(ers_charge, 3),
|
||||
"fuel_optimization_score": round(fuel_opt, 3),
|
||||
"driver_consistency": round(consistency, 3),
|
||||
"weather_impact": weather_impact,
|
||||
"lap": lap_number,
|
||||
"tire_degradation_rate": round(tire_deg_rate, 3),
|
||||
"pace_trend": pace_trend,
|
||||
"tire_cliff_risk": round(tire_cliff_risk, 3),
|
||||
"optimal_pit_window": pit_window,
|
||||
"performance_delta": round(performance_delta, 2)
|
||||
}
|
||||
|
||||
# Build race context
|
||||
race_context = self._build_race_context(
|
||||
lap=lap,
|
||||
total_laps=total_laps,
|
||||
track_name=track_name,
|
||||
track_temp=track_temp,
|
||||
rainfall=rainfall,
|
||||
driver_name=driver_name,
|
||||
current_position=current_position,
|
||||
tire_compound=tire_compound,
|
||||
tire_life_laps=tire_life_laps,
|
||||
fuel_level=fuel_level
|
||||
)
|
||||
race_context = {
|
||||
"race_info": {
|
||||
"track_name": self.state.track_name,
|
||||
"total_laps": total_laps,
|
||||
"current_lap": lap_number,
|
||||
"weather_condition": "Wet" if rainfall else "Dry",
|
||||
"track_temp_celsius": track_temperature
|
||||
},
|
||||
"driver_state": {
|
||||
"driver_name": "Alonso",
|
||||
"current_position": 5, # Mock - could be passed in
|
||||
"current_tire_compound": tire_compound,
|
||||
"tire_age_laps": tire_life_laps,
|
||||
"fuel_remaining_percent": self._estimate_fuel(lap_number, total_laps)
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
"enriched_telemetry": enriched_telemetry,
|
||||
"race_context": race_context
|
||||
}
|
||||
|
||||
def _build_race_context(
|
||||
self,
|
||||
lap: int,
|
||||
total_laps: int,
|
||||
track_name: str,
|
||||
track_temp: Optional[float],
|
||||
rainfall: bool,
|
||||
driver_name: str,
|
||||
current_position: int,
|
||||
tire_compound: str,
|
||||
tire_life_laps: int,
|
||||
fuel_level: float
|
||||
) -> Dict[str, Any]:
|
||||
"""Build complete race context structure for AI layer."""
|
||||
|
||||
# Normalize tire compound for output
|
||||
tire_map = {
|
||||
"soft": "soft",
|
||||
"medium": "medium",
|
||||
"hard": "hard",
|
||||
"inter": "intermediate",
|
||||
"intermediate": "intermediate",
|
||||
"wet": "wet"
|
||||
}
|
||||
normalized_tire = tire_map.get(tire_compound.lower(), "medium")
|
||||
|
||||
# Determine weather condition
|
||||
if rainfall:
|
||||
weather_condition = "Wet"
|
||||
else:
|
||||
weather_condition = "Dry"
|
||||
|
||||
race_context = {
|
||||
"race_info": {
|
||||
"track_name": track_name,
|
||||
"total_laps": total_laps,
|
||||
"current_lap": lap,
|
||||
"weather_condition": weather_condition,
|
||||
"track_temp_celsius": float(track_temp) if track_temp is not None else 25.0
|
||||
},
|
||||
"driver_state": {
|
||||
"driver_name": driver_name,
|
||||
"current_position": current_position,
|
||||
"current_tire_compound": normalized_tire,
|
||||
"tire_age_laps": tire_life_laps,
|
||||
"fuel_remaining_percent": fuel_level * 100.0 # Convert 0..1 to 0..100
|
||||
},
|
||||
"competitors": self._generate_mock_competitors(current_position, normalized_tire, tire_life_laps)
|
||||
}
|
||||
|
||||
return race_context
|
||||
# --- HPC-Simulated Computation Methods ---
|
||||
|
||||
def _generate_mock_competitors(
|
||||
self,
|
||||
current_position: int,
|
||||
current_tire: str,
|
||||
current_tire_age: int
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Generate realistic mock competitor data for race context."""
|
||||
competitors = []
|
||||
def _compute_tire_degradation_rate(self, tire_compound: str, tire_age: int, track_temp: float) -> float:
|
||||
"""
|
||||
Simulate HPC computation of tire degradation rate.
|
||||
Returns 0-1 value (higher = worse degradation).
|
||||
"""
|
||||
base_rate = _TIRE_DEGRADATION_RATES.get(tire_compound, 0.020)
|
||||
|
||||
# Driver names pool
|
||||
driver_names = [
|
||||
"Verstappen", "Hamilton", "Leclerc", "Perez", "Sainz",
|
||||
"Russell", "Norris", "Piastri", "Alonso", "Stroll",
|
||||
"Gasly", "Ocon", "Tsunoda", "Ricciardo", "Bottas",
|
||||
"Zhou", "Magnussen", "Hulkenberg", "Albon", "Sargeant"
|
||||
]
|
||||
# Temperature effect: higher temp = more degradation
|
||||
temp_multiplier = 1.0
|
||||
if track_temp > 45:
|
||||
temp_multiplier = 1.3
|
||||
elif track_temp > 40:
|
||||
temp_multiplier = 1.15
|
||||
elif track_temp < 20:
|
||||
temp_multiplier = 0.9
|
||||
|
||||
tire_compounds = ["soft", "medium", "hard"]
|
||||
# Age effect: exponential increase after certain threshold
|
||||
age_multiplier = 1.0
|
||||
if tire_age > 20:
|
||||
age_multiplier = 1.0 + ((tire_age - 20) * 0.05) # +5% per lap over 20
|
||||
|
||||
# Generate positions around the current driver (±3 positions)
|
||||
positions_to_show = []
|
||||
for offset in [-3, -2, -1, 1, 2, 3]:
|
||||
pos = current_position + offset
|
||||
if 1 <= pos <= 20 and pos != current_position:
|
||||
positions_to_show.append(pos)
|
||||
degradation = base_rate * tire_age * temp_multiplier * age_multiplier
|
||||
return min(1.0, degradation)
|
||||
|
||||
def _compute_pace_trend(self) -> str:
|
||||
"""
|
||||
Analyze recent lap times to determine pace trend.
|
||||
Returns: "improving", "stable", or "declining"
|
||||
"""
|
||||
if len(self.state.lap_times) < 3:
|
||||
return "stable"
|
||||
|
||||
for pos in sorted(positions_to_show):
|
||||
# Calculate gap (negative if ahead, positive if behind)
|
||||
gap_base = (pos - current_position) * 2.5 # ~2.5s per position
|
||||
gap_variation = (hash(str(pos)) % 100) / 50.0 - 1.0 # -1 to +1 variation
|
||||
gap = gap_base + gap_variation
|
||||
|
||||
# Choose tire compound (bias towards similar strategy)
|
||||
tire_choice = current_tire
|
||||
if abs(hash(str(pos)) % 3) == 0: # 33% different strategy
|
||||
tire_choice = tire_compounds[pos % 3]
|
||||
|
||||
# Tire age variation
|
||||
tire_age = max(0, current_tire_age + (hash(str(pos * 7)) % 11) - 5)
|
||||
|
||||
competitor = {
|
||||
"position": pos,
|
||||
"driver": driver_names[(pos - 1) % len(driver_names)],
|
||||
"tire_compound": tire_choice,
|
||||
"tire_age_laps": tire_age,
|
||||
"gap_seconds": round(gap, 2)
|
||||
}
|
||||
competitors.append(competitor)
|
||||
recent_laps = self.state.lap_times[-5:] # Last 5 laps
|
||||
|
||||
return competitors
|
||||
|
||||
# --- Internals ---
|
||||
def _update_lap_stats(self, lap: int, speed: float, throttle: float) -> None:
|
||||
if lap <= 0:
|
||||
return
|
||||
# Store simple aggregates for consistency metrics
|
||||
self.state.lap_speeds[lap] = speed
|
||||
self.state.lap_throttle_avg[lap] = 0.8 * self.state.lap_throttle_avg.get(lap, throttle) + 0.2 * throttle
|
||||
self.state.last_lap = lap
|
||||
|
||||
def _compute_aero_efficiency(self, speed: float, throttle: float, brake: float) -> float:
|
||||
# Heuristic: favor high speed with low throttle variance (efficiency) and minimal braking at high speeds
|
||||
# Normalize speed into 0..1 assuming 0..330 km/h typical
|
||||
speed_n = max(0.0, min(1.0, speed / 330.0))
|
||||
brake_penalty = 0.4 * brake
|
||||
throttle_bonus = 0.2 * throttle
|
||||
base = 0.5 * speed_n + throttle_bonus - brake_penalty
|
||||
return max(0.0, min(1.0, base))
|
||||
|
||||
def _compute_tire_degradation(self, lap: int, speed: float, throttle: float, tire_compound: str, track_temp: Optional[float]) -> float:
|
||||
base_wear = _TIRES_BASE_WEAR.get(tire_compound, _TIRES_BASE_WEAR["medium"]) # per lap
|
||||
temp_factor = 1.0
|
||||
if isinstance(track_temp, (int, float)):
|
||||
if track_temp > 42:
|
||||
temp_factor = 1.25
|
||||
elif track_temp < 15:
|
||||
temp_factor = 0.9
|
||||
stress = 0.5 + 0.5 * throttle + 0.2 * max(0.0, (speed - 250.0) / 100.0)
|
||||
wear_this_lap = base_wear * stress * temp_factor
|
||||
# Update cumulative wear but cap at 1.0
|
||||
self.state.cumulative_wear = min(1.0, self.state.cumulative_wear + wear_this_lap)
|
||||
return self.state.cumulative_wear
|
||||
|
||||
def _compute_ers_charge(self, ers: Optional[float], throttle: float, brake: float) -> float:
|
||||
if isinstance(ers, (int, float)):
|
||||
# simple recovery under braking, depletion under throttle
|
||||
ers_level = float(ers) + 0.1 * brake - 0.05 * throttle
|
||||
# Calculate trend (simple linear regression)
|
||||
avg_first_half = sum(recent_laps[:len(recent_laps)//2]) / max(1, len(recent_laps)//2)
|
||||
avg_second_half = sum(recent_laps[len(recent_laps)//2:]) / max(1, len(recent_laps) - len(recent_laps)//2)
|
||||
|
||||
diff = avg_second_half - avg_first_half
|
||||
|
||||
if diff < -0.5: # Getting faster by more than 0.5s
|
||||
return "improving"
|
||||
elif diff > 0.5: # Getting slower by more than 0.5s
|
||||
return "declining"
|
||||
else:
|
||||
# infer ers trend if not provided
|
||||
ers_level = 0.6 + 0.05 * brake - 0.03 * throttle
|
||||
return max(0.0, min(1.0, ers_level))
|
||||
|
||||
def _compute_fuel_optimization(self, fuel_level: float, throttle: float) -> float:
|
||||
# Reward keeping throttle moderate when fuel is low and pushing when fuel is high
|
||||
fuel_n = max(0.0, min(1.0, fuel_level))
|
||||
ideal_throttle = 0.5 + 0.4 * fuel_n # higher fuel -> higher ideal throttle
|
||||
penalty = abs(throttle - ideal_throttle)
|
||||
score = 1.0 - penalty
|
||||
return max(0.0, min(1.0, score))
|
||||
|
||||
def _compute_driver_consistency(self) -> float:
|
||||
# Use last up to 5 laps speed variance to estimate consistency (lower variance -> higher consistency)
|
||||
laps = sorted(self.state.lap_speeds.keys())[-5:]
|
||||
if not laps:
|
||||
return 0.5
|
||||
speeds = [self.state.lap_speeds[l] for l in laps]
|
||||
mean = sum(speeds) / len(speeds)
|
||||
var = sum((s - mean) ** 2 for s in speeds) / len(speeds)
|
||||
# Map variance to 0..1; assume 0..(30 km/h)^2 typical range
|
||||
norm = min(1.0, var / (30.0 ** 2))
|
||||
return max(0.0, 1.0 - norm)
|
||||
|
||||
def _compute_weather_impact(self, rain_prob: Optional[float], track_temp: Optional[float]) -> str:
|
||||
score = 0.0
|
||||
if isinstance(rain_prob, (int, float)):
|
||||
score += 0.7 * float(rain_prob)
|
||||
if isinstance(track_temp, (int, float)):
|
||||
if track_temp < 12: # cold tires harder
|
||||
score += 0.2
|
||||
if track_temp > 45: # overheating
|
||||
score += 0.2
|
||||
if score < 0.3:
|
||||
return "low"
|
||||
if score < 0.6:
|
||||
return "medium"
|
||||
return "high"
|
||||
return "stable"
|
||||
|
||||
def _compute_tire_cliff_risk(self, tire_compound: str, tire_age: int) -> float:
|
||||
"""
|
||||
Compute probability of hitting tire performance cliff.
|
||||
Returns 0-1 (0 = no risk, 1 = imminent cliff).
|
||||
"""
|
||||
# Different compounds have different cliff points
|
||||
cliff_points = {
|
||||
"soft": 15,
|
||||
"medium": 25,
|
||||
"hard": 35,
|
||||
"inter": 20,
|
||||
"wet": 18
|
||||
}
|
||||
|
||||
cliff_point = cliff_points.get(tire_compound, 25)
|
||||
|
||||
if tire_age < cliff_point - 5:
|
||||
return 0.0
|
||||
elif tire_age >= cliff_point + 5:
|
||||
return 1.0
|
||||
else:
|
||||
# Linear risk increase in 10-lap window around cliff point
|
||||
return (tire_age - (cliff_point - 5)) / 10.0
|
||||
|
||||
def _compute_optimal_pit_window(self, current_lap: int, total_laps: int, tire_age: int, tire_compound: str) -> List[int]:
|
||||
"""
|
||||
Calculate optimal pit stop window based on tire degradation.
|
||||
Returns [start_lap, end_lap] for pit window.
|
||||
"""
|
||||
cliff_risk = self._compute_tire_cliff_risk(tire_compound, tire_age)
|
||||
|
||||
if cliff_risk > 0.7:
|
||||
# Urgent pit needed
|
||||
return [current_lap + 1, current_lap + 3]
|
||||
elif cliff_risk > 0.4:
|
||||
# Pit soon
|
||||
return [current_lap + 3, current_lap + 6]
|
||||
else:
|
||||
# Tire still good, estimate based on compound
|
||||
if tire_compound == "soft":
|
||||
laps_remaining = max(0, 18 - tire_age)
|
||||
elif tire_compound == "medium":
|
||||
laps_remaining = max(0, 28 - tire_age)
|
||||
else: # hard
|
||||
laps_remaining = max(0, 38 - tire_age)
|
||||
|
||||
pit_lap = min(current_lap + laps_remaining, total_laps - 5)
|
||||
return [max(current_lap + 1, pit_lap - 2), pit_lap + 2]
|
||||
|
||||
def _compute_performance_delta(self, current_lap_time: float) -> float:
|
||||
"""
|
||||
Calculate performance delta vs baseline lap time.
|
||||
Negative = slower than baseline, Positive = faster.
|
||||
"""
|
||||
if self._baseline_lap_time is None:
|
||||
return 0.0
|
||||
|
||||
return self._baseline_lap_time - current_lap_time # Negative if slower
|
||||
|
||||
def _estimate_fuel(self, current_lap: int, total_laps: int) -> float:
|
||||
"""Estimate remaining fuel percentage based on lap progression."""
|
||||
return max(0.0, 100.0 * (1.0 - (current_lap / total_laps)))
|
||||
|
||||
def _parse_lap_time(self, lap_time_str: Optional[str]) -> float:
|
||||
"""Convert lap time string to seconds."""
|
||||
if not lap_time_str:
|
||||
return 90.0 # Default ~1:30
|
||||
|
||||
try:
|
||||
# Handle pandas Timedelta string format
|
||||
td = pd.to_timedelta(lap_time_str)
|
||||
return td.total_seconds()
|
||||
except:
|
||||
return 90.0
|
||||
|
||||
Reference in New Issue
Block a user