Dataset Viewer (First 5GB)
Auto-converted to Parquet Duplicate
GrandPrix
string
Session
string
Driver
string
Lap
int64
CornerID
int64
Compound
string
TireLife
int64
Stint
int64
speed_0
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speed_1
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speed_2
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speed_3
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speed_4
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speed_5
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speed_6
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speed_7
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speed_9
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speed_10
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speed_11
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speed_12
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speed_13
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speed_14
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speed_15
float64
speed_16
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speed_17
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speed_18
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speed_19
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speed_20
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speed_21
float64
speed_22
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speed_23
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speed_24
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speed_25
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speed_26
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speed_27
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speed_28
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speed_29
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speed_30
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speed_31
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speed_32
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speed_33
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speed_34
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speed_35
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speed_36
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speed_37
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speed_44
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speed_49
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rpm_0
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rpm_3
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rpm_4
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rpm_5
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rpm_44
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rpm_45
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rpm_46
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rpm_47
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rpm_48
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rpm_49
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throttle_0
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throttle_1
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throttle_2
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throttle_49
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brake_49
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Abu Dhabi Grand Prix
Race
ALB
1
2
SOFT
1
1
145
149.3
154.8
155
164
167.3
172
172.45
178
179.485714
186
188.45
192.825
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196.87
201.37
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Abu Dhabi Grand Prix
Race
ALB
1
3
SOFT
1
1
227.420063
227
225.744445
223.311111
223
221
221
221
220.033333
220
220.828571
221
222
222.46
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226.866667
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229.854773
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233.24
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10,410
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10,400
10,405.8
10,406
10,430.028571
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41.197221
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51
54
56.9
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60.314286
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80.7
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Abu Dhabi Grand Prix
Race
ALB
1
4
SOFT
1
1
258.71
262
262.457143
266
267.32
268
268.4
270.65
271
273.914286
274
276
278
278.371429
278.942857
280
281
281.433333
284.433333
285
285.7
286
287
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10,802
10,818.342857
10,945
10,982.62
11,002
11,018.4
11,110.65
11,125
11,240.6
11,244
11,313
11,500
11,498.514286
11,496.228571
11,492
11,535
11,544.316667
11,608.816667
11,621
11,681.2
11,707
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99
99
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99
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37.062
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Abu Dhabi Grand Prix
Race
ALB
1
5
SOFT
1
1
284
282.8
278
263.533333
247
247
247
216
200.7375
179
164.861493
157
112.264
109
106.2
103
102.1
101
99.71
98.96
98
97.64
97
95.11
94
94.144444
94.7
94.866667
95
97.485714
98
99.9
100
103.4
104
104.64
108
108.444444
112.333333
113
120.166667
123
132
133.12
139.42
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11,504.2
10,789
10,427.8
10,015
9,654.475
9,578
9,613
9,653.8375
9,712
9,428.587207
9,271
7,795.644
7,688
7,499.933333
7,285
7,268.8
7,249
7,137.2
7,072.2
6,989
6,971
6,939
6,872.22
6,833
6,854.955556
6,939.4
6,964.733333
6,985
7,231.085714
7,282
7,496.7
7,508
7,794.45
7,845
7,893.48
8,148
8,192
8,577
8,643
9,108.833333
9,293
9,467
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10,117.84
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0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6.3
10
12.022222
19.8
22.133333
24
28.142857
29
31.85
32
37.95
39
39.64
43
43.622222
49.066667
50
55.016667
57
61
61.88
66.83
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18.721292
18.765367
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26.558184
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Abu Dhabi Grand Prix
Race
ALB
1
6
SOFT
1
1
196.95
184
173.25
169
159.8625
152
146.068535
137.595013
135
124.366667
124
83.482353
83
78.05
74
68
66.7
62.2
62
54.909091
52
50
49.6
48
44.755556
44
44
44
44.7
45
45
44.84
44
44
44
43
42.366667
42
42
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-1,000
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9,049.875
8,481
8,627.916667
8,686
8,795.65
8,890
8,805.912756
8,685.788121
8,649
8,463.4
8,457
6,724.623529
6,704
6,305.8
5,980
5,471
5,352.916667
4,944.166667
4,926
4,428.927273
4,225
3,986
3,949.2
3,802
3,814.977778
3,818
3,750.68
3,716
3,678.9
3,663
3,831
3,962.84
4,655
4,606.24
4,549
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4,437.766667
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Abu Dhabi Grand Prix
Race
ALB
1
7
SOFT
1
1
32.57
32
32
32
32
32
32
32
32
32
32
32
32.466667
33
33.185714
34
33.77
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4,017.21
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3,921
3,922.4
3,923.9
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3,931
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Abu Dhabi Grand Prix
Race
ALB
1
8
SOFT
1
1
128
131.3
135.3
137
144.909091
146.954545
152
154.45
159
159
159
175
175.327273
196.6
199
201.8
203
203
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218.2
221
222.965001
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225.871429
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11,156
10,861.933333
10,505.488889
10,354
10,762.109091
10,867.654545
11,128
11,079
10,988
10,943.866667
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10,988
10,983.581818
10,696.4
10,664
10,918.8
11,028
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65
67.933333
71.488889
73
78.8
80.3
84
87.15
93
93.8
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100
100
100
100
100
100
100
100
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Abu Dhabi Grand Prix
Race
ALB
1
9
SOFT
1
1
202
184.675
181
175.5
171.333333
166
161
160.5
151
148.542857
147
145.48
145
140.7
140
138
137.4
136
133.35
133
130
130.66
130.86
131
132.8
133
135.7
136
137.177778
138
138.490909
140.945455
141
143
143.06
144.155
145
145.129167
146
146
146
147.76569
148
151.9
153
157.208617
161
161.91
167.51
168
10,180
9,944.875
9,895
9,882.9
9,873.733333
9,862
9,634
9,690
10,754
10,553.742857
10,428
10,261.56
10,209
10,096.34
10,078
9,897
9,843.9
9,720
9,661.7
9,654
9,621
9,650.7
9,659.7
9,666
9,759.6
9,770
9,976.1
9,999
10,119.133333
10,203
10,222.636364
10,320.818182
10,323
10,528
10,528.24
10,532.620001
10,536
10,540.262509
10,569
10,734.387581
10,771
10,905.192473
10,923
11,224.86
11,310
11,520.43087
11,710
11,570.64
10,713.04
10,638
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0
0
0
0
0
0
0
0
0
0
2.58
3
9
11.7
18
24.183333
25
34
38.62
40.02
41
46.4
47
47
47
46.411111
46
45.836364
45.018182
45
46
46
46
46
46.129167
47
49.456251
50
51.76569
52
55.12
56
58.104309
60
60.13
60.93
61
1
1
1
1
1
1
1
1
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91.182
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91.421
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91.821
92.053
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Abu Dhabi Grand Prix
Race
ALB
1
10
SOFT
1
1
217
221
221.3
222.9875
224
226
226.55
227
227.72
229
229.96
230
226
225.22
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11,429
10,449
10,457
10,502
10,529
10,705
10,729.2
10,749
10,763.04
10,788
10,880.16
10,884
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10,515.7
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100
100
100
100
100
100
100
100
100
100
93.28
93
13
12.22
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3,703.411944
3,713.234167
3,715.211063
3,726.394707
3,733.145278
3,743.189722
3,751.498798
3,758.323056
3,762.890991
3,771.045278
3,783.314517
3,783.823056
3,793.8675
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95.302
95.462
95.494
95.674
95.782
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96.074
96.182
96.254
96.382
96.574
96.582
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96.754
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-1,000
Abu Dhabi Grand Prix
Race
ALB
1
11
SOFT
1
1
200
197.768747
193
177
176.642498
173.392498
165.592498
164
157.069638
156
152.411214
150
147.779999
146
135.855556
135
128
126.5375
119.225
119
113.266667
111
107.7
106
102
102
102
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9,971
10,155.556482
10,550
9,509
9,520.522567
9,625.272567
9,876.672567
9,928
9,553.760435
9,496
9,217.270972
9,030
8,889.029919
8,776
8,279.844444
8,238
8,881
8,803.8125
8,417.875
8,406
8,071.316667
7,939
7,756.18
7,662
7,457
7,809.8
7,961
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0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
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-232
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3,816.811944
3,819.581807
3,825.389722
3,841.123056
3,841.641671
3,846.305581
3,857.16863
3,859.345278
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3,874.901944
3,882.949706
3,888.276944
3,892.820978
3,896.388056
3,908.870621
3,909.888056
3,918.421389
3,920.204239
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3,928.999167
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97.193
97.302
97.622
97.633
97.733
97.973
98.022
98.333
98.381
98.573
98.702
98.813
98.902
99.234
99.262
99.502
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99.814
99.822
99.994
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Abu Dhabi Grand Prix
Race
ALB
1
12
SOFT
1
1
54.6
54
53.045
52.045
52
51.370833
50.2875
50
49.521303
49
48.640898
47.44389
47
47
47
47
47
47
47
47.733333
49
49.96
50
51.6125
53
54.15
55
55.933333
57
58.65
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4,481.9
4,418
4,361.177481
4,301.677481
4,299
4,298.370833
4,297.2875
4,297
4,118.924781
3,925
3,970.426441
4,121.847912
4,178
4,092.9
3,956
3,959.35
4,015.183333
4,023
4,022
4,027.866667
4,038
4,223.28
4,231
4,376.125
4,501
4,649.925
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5,224.3
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0
0
0
0
0
0
0
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0.359102
1.55611
2
3.15
5
5
5
5
6
6.733333
8
8.96
9
10.6125
12
12
12
14.8
18
19.1
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4,014.771211
4,015.476944
4,018.260926
4,021.127125
4,021.254722
4,023.377781
4,026.972077
4,027.921389
4,030.541933
4,033.352222
4,034.306202
4,037.430222
4,038.5875
4,039.787031
4,041.720833
4,041.877642
4,044.489458
4,044.854167
4,047.465278
4,049.236211
4,052.365278
4,055.029703
4,055.143056
4,057.639455
4,059.854167
4,061.252165
4,062.298611
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103.694
103.742
103.933
104.133
104.142
104.293
104.553
104.622
104.813
105.021
105.093
105.333
105.422
105.514
105.662
105.674
105.874
105.902
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106.234
106.462
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106.982
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107.382
107.514
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-1,000
-1,000
Abu Dhabi Grand Prix
Race
ALB
1
13
SOFT
1
1
74.51
75
78.028571
79
80.65
81
81
81
81
81
81
81
81
81
81
81
82
83.32
84
85
86.091667
87
88
88.137501
91
92.510001
93
94.144654
95
98.017794
99
103.8
108
108.9
110
116
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-1,000
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6,468.92
6,507
6,781.842857
6,870
6,868.35
6,868
6,978.94
6,997
6,867
6,868.533333
6,871
6,904.171429
6,925
6,933.34
7,044.54
7,064
7,156
7,279.42
7,343
7,343
7,426.512541
7,496
7,570
7,586.68343
7,934
8,017.050035
8,044
8,257.478052
8,417
8,619.946665
8,686
9,111.066667
9,483
9,553.65
9,640
10,083
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23.93
24
22.485714
22
17.875
17
12.7
12
10
10
10
11.842857
13
13.3
17.3
18
21
21
21
21
21.545834
22
24
24
24
27.775002
29
30.716982
32
38.035589
40
43.733333
47
48.8
51
53
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0
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0
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Readme Dataset

This dataset is used in RacingDNA.

Dataset Overview This repository includes:

  • 2024-2025 Curve Dataset: Includes Race and Qualifying sessions.
  • Normalization: Data is already processed and normalized (see below).
  • 2025 Raw Data: Raw data from the 2025 season is included.

Credits: Raw telemetry data is sourced from TracingInsights on Hugging Face.

Dataset Structure

The dataset is a CSV file where each row represents a specific curve taken by a driver in a given lap.

Identification Columns

The first columns provide metadata for the event and the curve:

  • GrandPrix: Grand Prix.
  • Session: Session (e.g., Race).
  • Driver: Driver (e.g., ALB).
  • Lap: Lap number.
  • CornerID: Numeric identifier of the curve.
  • Compound: Tire compound.
  • TireLife: Tire life.
  • Stint: Stint number.

Telemetry Data (Time Series)

The subsequent columns contain telemetry data sampled within the curve. Each feature has 50 dedicated columns (indexed 0 to 49), corresponding to time samples.

The included features are:

  • speed_[0-49]: Speed.
  • rpm_[0-49]: Engine RPM.
  • throttle_[0-49]: Throttle percentage.
  • brake_[0-49]: Brake percentage.
  • acc_x, acc_y, acc_z _[0-49]: Accelerations along the three axes.
  • x, y, z _[0-49]: Spatial coordinates.
  • distance_[0-49]: Distance traveled in the lap.
  • time_[0-49]: Lap timestamp.

Padding

The dataset uses padding to ensure a fixed size of 50 points per curve.

  • Padding Value: -1000.0
  • If a curve has fewer than 50 sampled points, the remaining values are filled with -1000.0.

Curve Extraction Logic

The logic for identifying and "cutting" curves from the continuous telemetry stream is defined in src/analysis/CurveDetector.py. The process aims to isolate driver behavior in each single curve while ensuring structural consistency for the dataset.

The process occurs in 4 main phases:

1. Search Window Definition (Spatial Bounding Box)

For each known curve (defined in the corners.json file), the algorithm restricts analysis to a specific portion of the track to avoid false positives from other curves.

  • The time index corresponding to the geometric apex point of the curve (the trajectory point closest to the theoretical apex X,Y coordinates) is identified.
  • A search window (lower_bound and upper_bound) is defined based on distance traveled (distance):
    • Window Start: Typically 150 meters before the apex (or halfway to the previous curve if very close).
    • Window End: Typically 250 meters after the apex (or halfway to the next curve).

2. Physical Detection (G-Force Thresholds)

Within this spatial window, the algorithm searches for the actual start and end of the cornering action by analyzing lateral acceleration (acc_y).

  • Smoothing: The acc_y signal is first filtered with a moving average (3-sample window) to reduce noise.
  • Curve Entry: Triggered when |acc_y_smooth| > 3.0 G.
  • Curve Exit: Deactivated when |acc_y_smooth| < 2.5 G. Using two different thresholds (Hysteresis) prevents premature exits due to small fluctuations in G-force during cornering.

3. Apex Validation

A sequence of points identified as a "curve" in the previous step is considered valid only if it includes the apex index.

  • If the algorithm detects a G-force peak that starts and ends before the apex, it is discarded.
  • This ensures that the extracted data is actually related to traversing the target curve and not to adjacent corrections or maneuvers.

4. Cutting and Centering (Clamping)

To standardize the sequence length for neural network input, a hard cut (Clamping) is applied based on the apex position.

  • Regardless of what is detected with G-forces, the extracted curve cannot extend beyond 25 samples before and after the apex.
  • Start: If the detected entry is more than 25 samples before the apex, it is cut to apex - 25.
  • End: If the detected exit is more than 25 samples after the apex, it is cut to apex + 25.

Final Result

The result is a time window of maximum 50 samples (slice [start:end]) centered on the apex.

  • If the curve is very long and fast, it will be truncated at the +/- 25 margins.
  • If the curve is short (e.g., slow chicane or quick exit), the window will be fewer than 50 samples. In this case, the padding (-1000.0 values) described above intervenes to fill the empty columns up to 50.

Data Normalization

Data normalization is handled by the src/analysis/dataset_normalization.py script and follows specific logic to preserve the temporal and structural consistency of the curves.

1. Grouping by Feature (Grouped Stats)

Columns representing the same physical quantity over time (e.g., from speed_0 to speed_49) are treated as a single group.

  • Instead of calculating mean and standard deviation for each individual time column (which could introduce artifacts if the distribution varies along the curve), a global statistic is calculated for the entire feature.
  • Example: The mean of speed is calculated on all samples of all curves (all rows, columns 0-49).

2. Padding Management

During statistics calculation and normalization, padding values (-1000.0) are strictly ignored.

  • Statistics Calculation: Padding values are replaced with NaN before calculating mean and standard deviation to avoid skewing results.
  • Normalized Output: In the final normalized dataset, padding values are explicitly set to 0.0. This is important for neural network input (especially if using masking or zero-padding mechanisms).

3. Z-Score Normalization

The normalization applied is Z-Score (Standardization): [ x_{norm} = \frac{x - \mu}{\sigma} ] Where $\mu$ and $\sigma$ are the global mean and standard deviation of the feature group to which $x$ belongs.

4. Categorical Variable Encoding

The Compound column undergoes different treatment:

  • One-Hot Encoding is applied for categories: HARD, INTERMEDIATE, WET, MEDIUM, SOFT.
  • This generates 5 additional binary columns (e.g., Compound_SOFT, Compound_MEDIUM...) which are not Z-Score normalized (remaining 0/1).

The result is saved in an .npz file containing:

  • data: Matrix of normalized data.
  • mask: Binary matrix (1=valid, 0=padding).
  • mean, std: Vectors to denormalize data (e.g., to reconstruct original curves).

Readme Dataset (Italiano)

Questo dataset è utilizzato in RacingDNA.

Dataset Overview Questa repository include:

  • Dataset curve 2024-2025: Include sessioni di Gara e Qualifica.
  • Normalizzazione: I dati sono già processati e normalizzati (vedi sotto).
  • Dati Raw 2025: Sono inclusi i dati grezzi della stagione 2025.

Credits: I dati raw telemetrici provengono da TracingInsights su Hugging Face.

Struttura del Dataset

Il dataset è un file CSV dove ogni riga rappresenta una specifica curva percorsa da un pilota in un determinato giro.

Colonne Identificative

Le prime colonne forniscono i metadati dell'evento e della curva:

  • GrandPrix: Gran Premio.
  • Session: Sessione (es. Race).
  • Driver: Pilota (es. ALB).
  • Lap: Numero del giro.
  • CornerID: Identificativo numerico della curva.
  • Compound: Mescola delle gomme.
  • TireLife: Vita delle gomme.
  • Stint: Numero dello stint.

Dati Telemetrici (Time Series)

Le colonne successive contengono i dati telemetrici campionati all'interno della curva. Ogni feature ha 50 colonne dedicate (da indice 0 a 49), corrispondenti ai campioni temporali.

Le feature incluse sono:

  • speed_[0-49]: Velocità.
  • rpm_[0-49]: Giri motore.
  • throttle_[0-49]: Percentuale acceleratore.
  • brake_[0-49]: Percentuale freno.
  • acc_x, acc_y, acc_z _[0-49]: Accelerazioni lungo i tre assi.
  • x, y, z _[0-49]: Coordinate spaziali.
  • distance_[0-49]: Distanza percorsa nel giro.
  • time_[0-49]: Timestamp del giro.

Padding

Il dataset utilizza padding per garantire una dimensione fissa di 50 punti per curva.

  • Valore di padding: -1000.0
  • Se una curva ha meno di 50 punti campionati, i valori rimanenti vengono riempiti con -1000.0.

Logica di Estrazione delle Curve

La logica con cui vengono individuate e "tagliate" le curve dal flusso telemetrico continuo è definita in src/analysis/CurveDetector.py. Il processo mira a isolare il comportamento del pilota in ogni singola curva, garantendo al contempo una consistenza strutturale per il dataset.

Il processo avviene in 4 fasi principali:

1. Definizione della Finestra di Ricerca (Bounding Box Spaziale)

Per ogni curva nota (definita nel file corners.json), l'algoritmo restringe l'analisi a una specifica porzione del tracciato per evitare falsi positivi da altre curve.

  • Viene identificato l'indice temporale corrispondente al punto geometrico dell'apice della curva (il punto della traiettoria più vicino alle coordinate X,Y dell'apice teorico).
  • Viene definita una finestra di ricerca (lower_bound e upper_bound) basata sulla distanza percorsa (distance):
    • Inizio finestra: Tipicamente 150 metri prima dell'apice (o a metà strada con la curva precedente se molto vicina).
    • Fine finestra: Tipicamente 250 metri dopo l'apice (o a metà strada con la curva successiva).

2. Rilevamento Fisico (Soglie G-Force)

All'interno di questa finestra spaziale, l'algoritmo cerca l'inizio e la fine effettiva dell'azione in curva analizzando l'accelerazione laterale (acc_y).

  • Smoothing: Il segnale acc_y viene prima filtrato con una media mobile (finestra di 3 campioni) per ridurre il rumore.
  • Ingresso Curva: Si attiva quando |acc_y_smooth| > 3.0 G.
  • Uscita Curva: Si disattiva quando |acc_y_smooth| < 2.5 G. L'uso di due soglie diverse (Isteresi) previene uscite premature dovute a piccole fluttuazioni del segnale G durante la percorrenza.

3. Validazione dell'Apice

Una sequenza di punti identificata come "curva" nel passaggio precedente viene considerata valida solo se include l'indice dell'apice.

  • Se l'algoritmo rileva un picco di G-force che inizia e finisce prima dell'apice, viene scartato.
  • Questo assicura che i dati estratti siano effettivamente relativi alla percorrenza della curva target e non a correzioni o manovre adiacenti.

4. Taglio e Centratura (Clamping)

Per uniformare la lunghezza delle sequenze da dare in pasto alla rete neurale, viene applicato un taglio rigido (Clamping) basato sulla posizione dell'apice.

  • Indipendentemente da quanto rilevato con le forze G, la curva estratta non può estendersi oltre 25 campioni prima e dopo l'apice.
  • Start: Se l'ingresso rilevato è oltre 25 campioni prima dell'apice, viene tagliato a apice - 25.
  • End: Se l'uscita rilevata è oltre 25 campioni dopo dell'apice, viene tagliata a apice + 25.

Risultato Finale

Il risultato è una finestra temporale di massimo 50 campioni (slice [start:end]) centrata sull'apice.

  • Se la curva è molto lunga e veloce, verrà troncata ai margini +/- 25.
  • Se la curva è breve (es. chicane lenta o uscita rapida), la finestra sarà inferiore ai 50 campioni. In questo caso, interviene il padding (valori -1000.0) descritto sopra per riempire le colonne vuote fino ad arrivare a 50.

Normalizzazione dei Dati

La normalizzazione dei dati è gestita dallo script src/analysis/dataset_normalization.py e segue una logica specifica per preservare la consistenza temporale e strutturale delle curve.

1. Raggruppamento per Feature (Grouped Stats)

Le colonne che rappresentano la stessa grandezza fisica nel tempo (es. da speed_0 a speed_49) vengono trattate come un unico gruppo.

  • Invece di calcolare media e deviazione standard per ogni singola colonna temporale (che potrebbe introdurre artefatti se la distribuzione varia lungo la curva), viene calcolata una statistica globale per l'intera feature.
  • Esempio: La media di speed è calcolata su tutti i campioni di tutte le curve (tutte le righe, colonne 0-49).

2. Gestione del Padding

Durante il calcolo delle statistiche e la normalizzazione, i valori di padding (-1000.0) vengono rigorosamente ignorati.

  • Calcolo Statistiche: I valori di padding vengono sostituiti con NaN prima di calcolare media e deviazione standard, per non falsare i risultati.
  • Output Normalizzato: Nel dataset finale normalizzato, i valori di padding vengono impostati esplicitamente a 0.0. Questo è importante per l'input delle reti neurali (soprattutto se si usano meccanismi di mascheramento o zero-padding).

3. Z-Score Normalization

La normalizzazione applicata è di tipo Z-Score (Standardizzazione): [ x_{norm} = \frac{x - \mu}{\sigma} ] Dove $\mu$ e $\sigma$ sono la media e deviazione standard globali del gruppo di feature a cui appartiene $x$.

4. Encoding delle Variabili Categoriche

La colonna Compound (mescola) subisce un trattamento diverso:

  • Viene applicato One-Hot Encoding per le categorie: HARD, INTERMEDIATE, WET, MEDIUM, SOFT.
  • Questo genera 5 colonne binarie aggiuntive (es. Compound_SOFT, Compound_MEDIUM...) che non vengono normalizzate con Z-Score (rimangono 0/1).

Il risultato è salvato in un file .npz che contiene:

  • data: Matrice dei dati normalizzati.
  • mask: Matrice binaria (1=valido, 0=padding).
  • mean, std: Vettori per poter denormalizzare i dati (es. per ricostruire le curve originali).
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