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Added consideration of race position and gap to ahead cars

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This commit is contained in:
Aditya Pulipaka
2025-10-19 04:28:49 -05:00
parent 098d881d15
commit 47f592c88b
15 changed files with 413 additions and 17537 deletions
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hpcsim/__pycache__/adapter.cpython-313.pyc
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hpcsim/__pycache__/enrichment.cpython-313.pyc
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27
hpcsim/adapter.py
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@@ -9,6 +9,9 @@ def normalize_telemetry(payload: Dict[str, Any]) -> Dict[str, Any]:
Accepted aliases for lap-level data:
- lap_number: lap, Lap, LapNumber, lap_number
- total_laps: TotalLaps, total_laps
- position: position, Position, Pos
- gap_to_leader: gap_to_leader, GapToLeader, gap_leader
- gap_to_ahead: gap_to_ahead, GapToAhead, gap_ahead
- lap_time: lap_time, LapTime, Time
- average_speed: average_speed, avg_speed, AvgSpeed
- max_speed: max_speed, MaxSpeed, max
@@ -22,6 +25,9 @@ def normalize_telemetry(payload: Dict[str, Any]) -> Dict[str, Any]:
aliases = {
"lap_number": ["lap_number", "lap", "Lap", "LapNumber"],
"total_laps": ["total_laps", "TotalLaps"],
"position": ["position", "Position", "Pos"],
"gap_to_leader": ["gap_to_leader", "GapToLeader", "gap_leader"],
"gap_to_ahead": ["gap_to_ahead", "GapToAhead", "gap_ahead"],
"lap_time": ["lap_time", "LapTime", "Time"],
"average_speed": ["average_speed", "avg_speed", "AvgSpeed"],
"max_speed": ["max_speed", "MaxSpeed", "max"],
@@ -53,6 +59,24 @@ def normalize_telemetry(payload: Dict[str, Any]) -> Dict[str, Any]:
except (TypeError, ValueError):
total_laps = 51
position = pick("position", 10)
try:
position = int(position)
except (TypeError, ValueError):
position = 10
gap_to_leader = pick("gap_to_leader", 0.0)
try:
gap_to_leader = float(gap_to_leader)
except (TypeError, ValueError):
gap_to_leader = 0.0
gap_to_ahead = pick("gap_to_ahead", 0.0)
try:
gap_to_ahead = float(gap_to_ahead)
except (TypeError, ValueError):
gap_to_ahead = 0.0
lap_time = pick("lap_time", None)
if lap_time:
out["lap_time"] = str(lap_time)
@@ -97,6 +121,9 @@ def normalize_telemetry(payload: Dict[str, Any]) -> Dict[str, Any]:
out.update({
"lap_number": lap_number,
"total_laps": total_laps,
"position": position,
"gap_to_leader": gap_to_leader,
"gap_to_ahead": gap_to_ahead,
"average_speed": average_speed,
"max_speed": max_speed,
"tire_compound": tire_compound,

74
hpcsim/enrichment.py
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@@ -10,6 +10,9 @@ import pandas as pd
# {
# "lap_number": 27,
# "total_laps": 51,
# "position": 5,
# "gap_to_leader": 12.345,
# "gap_to_ahead": 2.456,
# "lap_time": "0 days 00:01:27.318000",
# "average_speed": 234.62,
# "max_speed": 333.0,
@@ -36,6 +39,8 @@ class EnricherState:
"""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
positions: List[int] = field(default_factory=list) # Recent positions
gaps_to_ahead: List[float] = field(default_factory=list) # Recent gaps to car ahead
current_tire_age: int = 0
current_tire_compound: str = "medium"
tire_stint_start_lap: int = 1
@@ -63,6 +68,9 @@ class Enricher:
# Extract lap data
lap_number = int(lap_data.get("lap_number", 0))
total_laps = int(lap_data.get("total_laps", 51))
position = int(lap_data.get("position", 10))
gap_to_leader = float(lap_data.get("gap_to_leader", 0.0))
gap_to_ahead = float(lap_data.get("gap_to_ahead", 0.0))
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))
@@ -77,6 +85,8 @@ class Enricher:
# Update state
self.state.lap_times.append(lap_time_seconds)
self.state.lap_speeds.append(average_speed)
self.state.positions.append(position)
self.state.gaps_to_ahead.append(gap_to_ahead)
self.state.current_tire_age = tire_life_laps
self.state.current_tire_compound = tire_compound
self.state.total_laps = total_laps
@@ -85,6 +95,8 @@ class Enricher:
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:]
self.state.positions = self.state.positions[-10:]
self.state.gaps_to_ahead = self.state.gaps_to_ahead[-10:]
# Set baseline (best lap time)
if self._baseline_lap_time is None or lap_time_seconds < self._baseline_lap_time:
@@ -96,6 +108,8 @@ class Enricher:
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)
competitive_pressure = self._compute_competitive_pressure(position, gap_to_ahead)
position_trend = self._compute_position_trend()
# Build enriched telemetry
enriched_telemetry = {
@@ -104,7 +118,9 @@ class Enricher:
"pace_trend": pace_trend,
"tire_cliff_risk": round(tire_cliff_risk, 3),
"optimal_pit_window": pit_window,
"performance_delta": round(performance_delta, 2)
"performance_delta": round(performance_delta, 2),
"competitive_pressure": round(competitive_pressure, 3),
"position_trend": position_trend
}
# Build race context
@@ -118,10 +134,12 @@ class Enricher:
},
"driver_state": {
"driver_name": "Alonso",
"current_position": 5, # Mock - could be passed in
"current_position": position,
"current_tire_compound": tire_compound,
"tire_age_laps": tire_life_laps,
"fuel_remaining_percent": self._estimate_fuel(lap_number, total_laps)
"fuel_remaining_percent": self._estimate_fuel(lap_number, total_laps),
"gap_to_leader": gap_to_leader,
"gap_to_ahead": gap_to_ahead
}
}
@@ -238,6 +256,56 @@ class Enricher:
return self._baseline_lap_time - current_lap_time # Negative if slower
def _compute_competitive_pressure(self, position: int, gap_to_ahead: float) -> float:
"""
Calculate competitive pressure based on position and gap to car ahead.
Returns 0-1 (0 = no pressure, 1 = extreme pressure).
High pressure scenarios:
- Close gap to car ahead (potential overtake opportunity)
- Poor position (need to push harder)
"""
# Position pressure: worse position = higher pressure
position_pressure = min(1.0, (position - 1) / 15.0) # Normalize to 0-1
# Gap pressure: smaller gap = higher pressure (opportunity to attack)
if gap_to_ahead <= 0.0:
gap_pressure = 0.0 # Leading or no gap data
elif gap_to_ahead < 1.0:
gap_pressure = 1.0 # Very close - DRS range
elif gap_to_ahead < 3.0:
gap_pressure = 0.7 # Close - push to close gap
elif gap_to_ahead < 10.0:
gap_pressure = 0.3 # Moderate gap
else:
gap_pressure = 0.1 # Large gap - low pressure
# Combined pressure (weighted average)
return round((position_pressure * 0.4 + gap_pressure * 0.6), 3)
def _compute_position_trend(self) -> str:
"""
Analyze recent positions to determine trend.
Returns: "gaining", "stable", or "losing"
"""
if len(self.state.positions) < 3:
return "stable"
recent_positions = self.state.positions[-5:] # Last 5 laps
# Calculate trend (lower position number = better)
avg_first_half = sum(recent_positions[:len(recent_positions)//2]) / max(1, len(recent_positions)//2)
avg_second_half = sum(recent_positions[len(recent_positions)//2:]) / max(1, len(recent_positions) - len(recent_positions)//2)
diff = avg_first_half - avg_second_half # Positive if gaining positions
if diff > 0.5: # Position number decreased = gained positions
return "gaining"
elif diff < -0.5: # Position number increased = lost positions
return "losing"
else:
return "stable"
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)))