index
int64 | v_kmph
float64 | ax_mpss
float64 | ay_mpss
float64 | yaw_rate_radps
float64 | frame
image | d_lanecenter_m
float64 | alias
string | steering_rack_pos_m
float64 | steering_torque_N
float64 | lane_curvature_radpm
float64 | stationary
float64 | segment
int64 | split
string | road_type
string | driving_situation_rural
string | driving_situation_federal
string | driving_situation_highway
string | rep_id
int64 | frame_nr
int64 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 20.25 | 0.185714 | -0.294286 | -0.94 | 7.996094 | 001 | -0.66 | -1.28 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 0 | 5,850 |
|
3 | 20.299999 | -0.005714 | 0.045714 | -0.337143 | 7.996094 | 001 | -0.66 | -1.126667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 1 | 5,853 |
|
6 | 20.299999 | -0.066667 | -0.746667 | -0.396667 | 7.996094 | 001 | -0.7 | -1.246667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 2 | 5,857 |
|
9 | 20.49 | 0.026667 | -0.41 | -0.403333 | 7.996094 | 001 | -0.48 | -0.996667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 3 | 5,860 |
|
12 | 20.41 | 0.182857 | 0.02 | -0.56 | 7.996094 | 001 | -0.175 | -0.71 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 4 | 5,864 |
|
15 | 20.469999 | 0.211429 | -0.174286 | 0.48 | 7.996094 | 001 | -0.16 | -1.0875 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 5 | 5,867 |
|
18 | 20.530001 | 0.062857 | 0.051429 | -0.625714 | 7.996094 | 001 | -0.16 | -1.015 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 6 | 5,871 |
|
21 | 20.58 | 0.083333 | -0.083333 | -0.013333 | 7.996094 | 001 | -0.16 | -0.873333 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 7 | 5,875 |
|
24 | 20.58 | 0.02 | -0.42 | 0.525714 | 7.996094 | 001 | -0.16 | -0.94 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 8 | 5,878 |
|
27 | 20.61 | 0.151429 | -0.388571 | -0.137143 | 7.996094 | 001 | -0.073333 | -1.01 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 9 | 5,882 |
|
30 | 20.585 | 0.168571 | -0.262857 | -0.114286 | 7.996094 | 001 | -0.06 | -1.043333 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 10 | 5,885 |
|
33 | 20.639999 | 0.12 | -0.222857 | 0.054286 | 7.996094 | 001 | -0.126667 | -1.106667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 11 | 5,889 |
|
36 | 20.639999 | 0.14 | 0.11 | -0.656667 | 7.996094 | 001 | -0.14 | -1.11 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 12 | 5,892 |
|
39 | 20.826667 | 0.063333 | -0.513333 | -0.33 | 7.996094 | 001 | -0.246667 | -1.383333 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 13 | 5,896 |
|
42 | 20.826667 | 0.105714 | -0.554286 | 0.428571 | 7.996094 | 001 | -0.335 | -1.195 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 14 | 5,900 |
|
45 | 20.79 | 0.102857 | -0.208571 | -0.474286 | 7.996094 | 001 | -0.29 | -1.055 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 15 | 5,903 |
|
48 | 20.75 | 0.137143 | -0.128571 | -0.354286 | 7.996094 | 001 | -0.3 | -1.095 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 16 | 5,907 |
|
51 | 20.826667 | 0.123333 | -0.383333 | 0.216667 | 7.996094 | 001 | -0.32 | -0.913333 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 17 | 5,910 |
|
54 | 20.92 | 0.1 | -0.302857 | -0.228571 | 7.996094 | 001 | -0.26 | -0.993333 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 18 | 5,914 |
|
57 | 21.030001 | 0.177143 | -0.305714 | -0.177143 | 7.996094 | 001 | -0.12 | -0.946667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 19 | 5,917 |
|
60 | 21.09 | 0.022857 | -0.131429 | -0.011429 | 7.996094 | 001 | 0 | -0.626667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 20 | 5,921 |
|
63 | 21.15 | 0.08 | 0.316667 | 0.386667 | 7.996094 | 001 | -0.053333 | -0.456667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 21 | 5,924 |
|
66 | 21.26 | 0.286667 | -0.433333 | 0.013333 | 7.996094 | 001 | -0.04 | -0.613333 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 22 | 5,928 |
|
69 | 21.370001 | 0.371429 | -0.305714 | 0.291429 | 7.996094 | 001 | 0.006667 | -0.516667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 23 | 5,932 |
|
72 | 21.446667 | 0.365714 | -0.4 | 0.38 | 7.996094 | 001 | 0.02 | -0.17 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 24 | 5,935 |
|
75 | 21.560001 | 0.197143 | -0.002857 | -0 | 7.996094 | 001 | 0.02 | -0.505 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 25 | 5,939 |
|
78 | 21.6 | 0.186667 | 0.113333 | -0.743333 | 7.996094 | 001 | 0.005 | -0.015 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 26 | 5,942 |
|
81 | 21.689999 | 0.25 | -0.246667 | 0.48 | 7.996094 | 001 | 0 | -0.173333 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 27 | 5,946 |
|
84 | 21.82 | 0.282857 | -0.157143 | 0.225714 | 7.996094 | 001 | 0 | -0.256667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 28 | 5,949 |
|
87 | 21.99 | 0.24 | -0.165714 | 0.071429 | 7.996094 | 001 | 0.006667 | -0.72 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 29 | 5,953 |
|
90 | 22.075 | 0.154286 | 0.302857 | 0.265714 | 7.996094 | 001 | -0.12 | -0.406667 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 30 | 5,957 |
|
93 | 22.27 | -0.086667 | -1.43 | 0.146667 | 7.996094 | 001 | -0.053333 | -0.51 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 31 | 5,960 |
|
96 | 22.379999 | 0.166667 | -0.29 | 0.15 | 7.996094 | 001 | 0.086667 | -0.403333 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 32 | 5,964 |
|
99 | 22.57 | 0.748571 | -0.182857 | -0.077143 | 7.996094 | 001 | 0.08 | 0.213333 | 0.031235 | 1 | 3 | val_val | misc | not valid | not valid | not valid | 33 | 5,967 |
|
102 | 22.853333 | 0.805714 | -0.237143 | -0.114286 | 7.996094 | 001 | 0.07 | 0.2975 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 34 | 5,971 |
|
105 | 22.966667 | 1.188571 | 0.331429 | -0.434286 | 7.996094 | 001 | 0.06 | 0.86 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 35 | 5,974 |
|
108 | 23.186667 | 0.906667 | -0.95 | 1.51 | 7.996094 | 001 | 0.15 | 0.19 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 36 | 5,978 |
|
111 | 23.526667 | 0.733333 | -0.51 | 0.586667 | 7.996094 | 001 | 0.28 | -0.14 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 37 | 5,981 |
|
114 | 23.73 | 0.622857 | -0.917143 | 0.257143 | 7.996094 | 001 | 0.273333 | -0.29 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 38 | 5,985 |
|
117 | 23.745 | 0.188571 | -0.468571 | 0.657143 | 7.996094 | 001 | 0.293333 | -1.046667 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 39 | 5,989 |
|
120 | 23.9975 | -0.191429 | 0.785714 | -0.645714 | 7.996094 | 001 | -0.046667 | -0.75 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 40 | 5,992 |
|
123 | 24.01 | 0.013333 | 0.103333 | -0.28 | 7.996094 | 001 | -0.72 | -0.13 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 41 | 5,996 |
|
126 | 23.773333 | -0.08 | -0.756667 | 0.383333 | 7.996094 | 001 | -0.806667 | -1.183333 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 42 | 5,999 |
|
129 | 23.64 | -0.325714 | -1.54 | 0.64 | 7.996094 | 001 | -0.44 | -1.95 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 43 | 6,003 |
|
132 | 23.486667 | 0.245714 | -0.288571 | 0.454286 | 7.996094 | 001 | 0.05 | -2.1575 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 44 | 6,006 |
|
135 | 23.85 | 0.414286 | 0.788571 | -0.645714 | 7.996094 | 001 | -0.29 | -1.23 | 0.031235 | 1 | 4 | val_val | misc | not valid | not valid | not valid | 45 | 6,010 |
|
138 | 23.993333 | 0.286667 | 0.093333 | -1.683333 | 7.996094 | 001 | -1.115 | -1.1375 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 46 | 6,014 |
|
141 | 24.066667 | 0.19 | -0.686667 | -1.78 | 7.996094 | 001 | -1.66 | -1.55 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 47 | 6,017 |
|
144 | 23.735001 | -0.06 | -1.868571 | -0.211429 | 7.996094 | 001 | -1.78 | -2.323333 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 48 | 6,021 |
|
147 | 23.620001 | 0.217143 | -1.271429 | -1.942857 | 7.996094 | 001 | -1.826667 | -2.063333 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 49 | 6,024 |
|
150 | 23.9 | 0.12 | 0.977143 | -3.508571 | 7.996094 | 001 | -1.84 | -1.45 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 50 | 6,028 |
|
153 | 24.01 | -0.05 | 0.076667 | -1.66 | 7.996094 | 001 | -2.06 | -0.653333 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 51 | 6,031 |
|
156 | 24.07 | -0.003333 | -0.81 | -1.593333 | 7.996094 | 001 | -1.953333 | -0.726667 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 52 | 6,035 |
|
159 | 24.163333 | -0.002857 | -1.091429 | -0.737143 | 7.996094 | 001 | -1.24 | -1.3 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 53 | 6,039 |
|
162 | 24.05 | -0.028571 | 0.322857 | -0.874286 | 7.996094 | 001 | -0.82 | -0.66 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 54 | 6,042 |
|
165 | 23.73 | 0.111429 | 0.457143 | -1.871429 | 7.996094 | 001 | -0.96 | -0.4425 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 55 | 6,046 |
|
168 | 24.129999 | 0.083333 | 0.296667 | -1.876667 | 7.996094 | 001 | -0.935 | 0.225 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 56 | 6,049 |
|
171 | 24.129999 | 0.163333 | -0.196667 | 0.286667 | 7.996094 | 001 | -0.64 | -0.033333 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 57 | 6,053 |
|
174 | 24.18 | 0.022857 | -1.02 | 0.754286 | 7.996094 | 001 | -0.146667 | -0.246667 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 58 | 6,056 |
|
177 | 24.225 | -0.108571 | -0.462857 | -0.34 | 7.996094 | 001 | 0.153333 | -0.966667 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 59 | 6,060 |
|
180 | 24.255 | -0.005714 | 0.022857 | -0.108571 | 7.996094 | 001 | 0.02 | -0.693333 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 60 | 6,063 |
|
183 | 24.163333 | 0.11 | 0.666667 | -0.666667 | 7.996094 | 001 | -0.653333 | -0.363333 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 61 | 6,067 |
|
186 | 23.976666 | 0.023333 | -0.33 | -1.643333 | 7.996094 | 001 | -0.76 | -1.06 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 62 | 6,071 |
|
189 | 24.07 | -0.111429 | -0.911429 | 0.297143 | 7.996094 | 001 | -0.72 | -0.726667 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 63 | 6,074 |
|
192 | 24.24 | 0.002857 | -0.897143 | 0.477143 | 7.996094 | 001 | -0.62 | -1.4275 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 64 | 6,078 |
|
195 | 24.336666 | 0.071429 | 0.625714 | -1.751429 | 7.996094 | 001 | -0.695 | -0.7675 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 65 | 6,081 |
|
198 | 24.073332 | 0.043333 | -0.396667 | -0.726667 | 7.996094 | 001 | -0.68 | -0.5625 | 0.031235 | 0 | 4 | val_val | misc | not valid | not valid | not valid | 66 | 6,085 |
|
201 | 10.72 | 1.442857 | 0.985714 | 8.877143 | 7.996094 | 001 | 18.04 | 3.14 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 67 | 7,607 |
|
204 | 11.17 | 1.368571 | 1.1 | 10.422857 | 7.996094 | 001 | 20.58 | 3.19 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 68 | 7,610 |
|
207 | 11.62 | 1.38 | 1.271429 | 12.231429 | 7.996094 | 001 | 23.16 | 3.2875 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 69 | 7,614 |
|
210 | 12.07 | 1.31 | 1.37 | 14.28 | 7.996094 | 001 | 25.53 | 3.3075 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 70 | 7,617 |
|
213 | 12.4275 | 1.18 | 1.696667 | 15.973334 | 7.996094 | 001 | 27.453334 | 3.306667 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 71 | 7,621 |
|
216 | 12.8075 | 1.12 | 1.582857 | 17.622857 | 7.996094 | 001 | 28.933334 | 3.306667 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 72 | 7,624 |
|
219 | 13.24 | 1.228571 | 1.431429 | 18.914285 | 7.996094 | 001 | 29.440001 | 3.05 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 73 | 7,628 |
|
222 | 13.683333 | 1.38 | 1.282857 | 19.434286 | 7.996094 | 001 | 29.366667 | 2.666667 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 74 | 7,632 |
|
225 | 14.19 | 1.426667 | 1.453333 | 19.853333 | 7.996094 | 001 | 29.360001 | 2.62 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 75 | 7,635 |
|
228 | 14.66 | 1.29 | 1.65 | 20.45 | 7.996094 | 001 | 29.360001 | 2.693333 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 76 | 7,639 |
|
231 | 15.143333 | 1.402857 | 1.608571 | 21.354286 | 7.996094 | 001 | 29.360001 | 2.753333 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 77 | 7,642 |
|
234 | 15.67 | 1.491429 | 1.888571 | 21.837142 | 7.996094 | 001 | 29.360001 | 2.995 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 78 | 7,646 |
|
237 | 16.233334 | 1.508571 | 2.045714 | 22.205715 | 7.996094 | 001 | 29.47 | 3.22 | 0.031235 | 0 | 11 | val_val | misc | not valid | not valid | not valid | 79 | 7,649 |
|
240 | 16.723333 | 1.346667 | 2.12 | 23.48 | 7.996094 | 001 | 29.559999 | 3.23 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 80 | 7,653 |
|
243 | 17.235 | 1.216667 | 1.806667 | 24.013334 | 7.996094 | 001 | 29.559999 | 3.073333 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 81 | 7,657 |
|
246 | 17.684999 | 0.994286 | 2.248571 | 24.305714 | 7.996094 | 001 | 29.553333 | 2.943333 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 82 | 7,660 |
|
249 | 18.165 | 0.931429 | 2.528571 | 24.937143 | 7.996094 | 001 | 29.493333 | 3.01 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 83 | 7,664 |
|
252 | 18.426666 | 0.848571 | 2.597143 | 25.202858 | 7.996094 | 001 | 29.4 | 2.923333 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 84 | 7,667 |
|
255 | 18.936666 | 0.8 | 2.89 | 25.746666 | 7.996094 | 001 | 29.093334 | 2.93 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 85 | 7,671 |
|
258 | 19.456666 | 1.026667 | 2.623333 | 26.46 | 7.996094 | 001 | 28.606667 | 2.853333 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 86 | 7,674 |
|
261 | 19.796666 | 0.894286 | 2.582857 | 26.554286 | 7.996094 | 001 | 28.186666 | 2.906667 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 87 | 7,678 |
|
264 | 20.43 | 0.874286 | 2.72 | 26.537143 | 7.996094 | 001 | 27.985 | 2.9025 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 88 | 7,682 |
|
267 | 20.94 | 0.771429 | 3.048571 | 26.437143 | 7.996094 | 001 | 27.434999 | 2.7525 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 89 | 7,685 |
|
270 | 21.406667 | 0.806667 | 2.923333 | 26.34 | 7.996094 | 001 | 25.89 | 2.84 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 90 | 7,689 |
|
273 | 21.9325 | 0.886667 | 2.953333 | 24.693333 | 7.996094 | 001 | 24.24 | 2.893333 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 91 | 7,692 |
|
276 | 22.3675 | 1.008571 | 2.685714 | 23.577143 | 7.996094 | 001 | 23.053333 | 2.566667 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 92 | 7,696 |
|
279 | 22.9475 | 1.728571 | 2.405714 | 23.525714 | 7.996094 | 001 | 21.52 | 2.533333 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 93 | 7,699 |
|
282 | 23.563334 | 1.691429 | 2.422857 | 22.262857 | 7.996094 | 001 | 19.773333 | 2.64 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 94 | 7,703 |
|
285 | 24.203333 | 1.52 | 2.516667 | 20.613333 | 7.996094 | 001 | 18.786667 | 2.646667 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 95 | 7,706 |
|
288 | 24.84 | 1.18 | 2.89 | 20.17 | 7.996094 | 001 | 18.033334 | 2.61 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 96 | 7,710 |
|
291 | 25.343334 | 1.034286 | 2.711429 | 19.682857 | 7.996094 | 001 | 16.786667 | 2.786667 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 97 | 7,714 |
|
294 | 25.91 | 1.277143 | 2.405714 | 18.468571 | 7.996094 | 001 | 15.565 | 2.735 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 98 | 7,717 |
|
297 | 26.563334 | 1.542857 | 2.222857 | 17.371429 | 7.996094 | 001 | 14.41 | 2.4875 | 0.031235 | 1 | 11 | val_val | misc | not valid | not valid | not valid | 99 | 7,721 |
Dataset Card for Dataset SADC
There is evidence that the driving style of an autonomous vehicle is important to increase the acceptance and trust of the passengers. The driving situation has been found to have a significant influence on human driving behavior. However, current driving style models only partially incorporate driving environment information, limiting the alignment between an agent and the given situation.
Therefore, we propose a dataset for situation-aware driving style modeling.
Dataset Details
Dataset Description
The dataset is composed as follows: the pretrain set DP is split into a training subset DP,T with 242 887 samples, and a validation subset DP,V with 61 400 samples. Similarly, the validation set DV is split into a training subset DV,T and a validation subset DV,V with 138 572 and 34 767 samples. Each subset consists of 1280 × 960 images, driving behavior indicators like the distance to the lane center, vehicle signals like velocity or accelerations, as well as traffic conditions and road type labels.
- Curated by: Johann Haselberger
- License: CC-BY-4.0
Dataset Sources
We collected over 16 hours of driving data from single test driver as pretrain data. For the driving style adaptation, we collected driving behavior data from five different subjects driving on the same route for one hour, denoted as validation data.
Usage
Download Script
For an easy usage of our dataset, we provide a download script with our repo: https://github.com/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation/blob/master/utils/download_dataset.py.
python download_dataset.py --target_dir ../data --split pretrain_train
List Available Split Names
from datasets import load_dataset, get_dataset_split_names
split_names = get_dataset_split_names("jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation")
print(f"Available split names: {split_names}")
Inspect some Samples
from datasets import load_dataset, get_dataset_split_names
from matplotlib import pyplot as plt
import pandas as pd
dataset = load_dataset("jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation", split="val_val", streaming=True)
samples = dataset.take(50)
df = pd.DataFrame.from_dict([s for s in samples])
print(df.head())
Visualize some Time-Series
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(df["frame_nr"],df["v_kmph"],"ko-",label="velocity")
ax2.plot(df["frame_nr"],df["steering_torque_N"],"ro-",label="steering torque")
ax1.set_xlabel('Frame')
ax1.set_ylabel('Velocity in km/h', color='k')
ax2.set_ylabel('Steering Torque in N', color='r')
plt.show()
Visualize the Camera Image
plt.imshow(df["frame"].iloc[-1])
plt.axis('off')
plt.show()
Dataset Structure
Dataset Splits
Split | Number of Samples | Description |
---|---|---|
Used for the Experiments in the Paper | ||
pretrain | 304287 | The full pretrain dataset. |
pretrain_train | 242887 | Subset of pretrain used for training. |
pretrain_val | 61400 | Subset of pretrain used for validation. |
val_train | 138572 | Subset of validation used for training. |
val_val | 34767 | Subset of validation used for validation. |
Additional Data | ||
pretrain_unfiltered | 1180252 | The full unfiltered pretrain dataset. |
val_unfiltered | 686328 | The full unfiltered validation dataset. |
Files
- The folder
driving_data
contains the vehicle signals. Downloading these files is optional and is only required if you do not want to download the entire image data set. - The folder
image_lists
contains the image lists used for training of the featrue encoders and NN-based behavior predictors. Downloading these files is optional.
Personal and Sensitive Information
To blur vehicle license plates and human faces in the camera frames, we utilize EgoBlur https://github.com/facebookresearch/EgoBlur.
Furthermore, all subject-related data, including the socio-demographics, are anonymized.
Bias, Risks, and Limitations
Considering the limitations of our dataset, real-world tests should be conducted with care in a safe environment. To publish the data concerning privacy policies, we utilized a state-of-the-art anonymization framework to blur human faces and vehicle license plates to mitigate privacy concerns.
Citation [optional]
BibTeX:
@misc{haselberger2024situation,
title={Situation Awareness for Driver-Centric Driving Style Adaptation},
author={Johann Haselberger and Bonifaz Stuhr and Bernhard Schick and Steffen Müller},
year={2024},
eprint={2403.19595},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
APA:
Johann Haselberger, Bonifaz Stuhr, Bernhard Schick, & Steffen Müller. (2024). Situation Awareness for Driver-Centric Driving Style Adaptation.
- Downloads last month
- 0