Moto Trackday Project Script Auto Race Inf: M Verified

Lap 10: 1:48.22 Sector times: - S1 (0–850m): 32.10s - S2 (850–1850m): 34.05s <<< anomaly: +0.5s vs best - S3 (1850–3024m): 42.07s Auto-race-inf detection flags that meter 1,850 is the entry to a fast right-left chicane. The script pulls throttle position data and reveals you’re lifting 20 meters early every lap at that exact spot.

Solution: Adjust brake marker. Next session, you gain 0.4 seconds. moto trackday project script auto race inf m verified

pip install gpxpy geopy numpy scipy matplotlib pandas Here’s a simplified script skeleton that detects corner entries based on yaw rate (GPS-derived heading change): Lap 10: 1:48

Within one season, you’ll stop riding by feel alone. You’ll ride by – and drop seconds off your lap time. Have you built a trackday script? Share your GitHub or RaceStudio template in the comments. Let’s verify every meter, together. Next session, you gain 0

corner_meters = [] for peak in peaks: cumulative_dist = 0 for i, pt in enumerate(gpx.tracks[0].segments[0].points): if i <= peak: cumulative_dist += pt.distance_2d(prev_pt) prev_pt = pt corner_meters.append(round(cumulative_dist, 1))

def verify_distance(gps_dist, wss_pulse_count, rolling_circumference_m=1.98): wss_dist = wss_pulse_count * rolling_circumference_m error = abs(gps_dist - wss_dist) verified = error < 1.0 # less than 1 meter error print(f"GPS: gps_dist:.1fm | WSS: wss_dist:.1fm | Verified: verified") return verified, wss_dist For most trackday projects, a combination of is enough for "m verified." Part 5: Real-World Use Case – Fixing a "Losing Time at Meter 1,850" You run your script after a session. Output:

print(f"Auto-detected len(corner_meters) corners at meters: corner_meters") return corner_meters detect_corners("my_lap.gpx") To verify distance, compare GPS against wheel speed sensor (WSS) pulses: