Duckietown Challenges | Home | Challenges | Submissions |
Last computed:
#1 FANG MEIYI 3131 | #2 jiang peng 3281 | #3 jiang peng 3296 |
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Rank (user) | User | Submission | complete | User label | Traveled distance↑ | Survival time↑ | Lateral deviation ↓ | Major infractions ↓ |
1 | FANG MEIYI 🇸🇬 | 3131 | 1/1 | test | 3.72 | 16. | 0.65 | 1.4 |
2 | jiang peng | 3281 | 1/1 | Second test - TH | 3.72 | 16. | 0.77 | 1. |
- | jiang peng | 3296 | 1/1 | Third test - TH | 3.72 | 16. | 1.17 | 1.2 |
- | jiang peng | 3731 | 1/1 | LF sim test - wh | 3.72 | 16. | 1.43 | 0.2 |
- | jiang peng | 3189 | 1/1 | First test - TH | 3.1 | 16. | 0.59 | 1. |
- | jiang peng | 3394 | 1/1 | RL-TH-shi | 3.1 | 16. | 0.75 | 0. |
- | jiang peng | 3587 | 1/1 | Fourth test - TH | 3.1 | 16. | 0.84 | 4.6 |
3 | Anastasiya Nikolskaya 🇷🇺 | 3338 | 1/1 | JetBrains Research | 3.1 | 16. | 1.08 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3190 | 1/1 | NN Solution | 3.1 | 16. | 1.11 | 1.8 |
4 | Bea Baselines 🐤 | 3503 | 1/1 | Baseline-IL-logs-tensorflow | 3.1 | 16. | 1.18 | 1.4 |
This list includes repeated entries from the same user and the entries from the organizers.
#1 FANG MEIYI 3131 | #2 jiang peng 3281 | #3 jiang peng 3296 |
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Rank (user) | User | Submission | complete | User label | Traveled distance↑ | Survival time↑ | Lateral deviation ↓ | Major infractions ↓ |
1 | FANG MEIYI 🇸🇬 | 3131 | 1/1 | test | 3.72 | 16. | 0.65 | 1.4 |
- | FANG MEIYI 🇸🇬 | 2910 | 1/1 | test | 3.72 | 16. | 0.67 | 0. |
2 | jiang peng | 3281 | 1/1 | Second test - TH | 3.72 | 16. | 0.77 | 1. |
- | FANG MEIYI 🇸🇬 | 3232 | 1/1 | test | 3.72 | 16. | 0.91 | 0. |
- | FANG MEIYI 🇸🇬 | 3207 | 1/1 | test | 3.72 | 16. | 0.91 | 0. |
- | jiang peng | 3296 | 1/1 | Third test - TH | 3.72 | 16. | 1.17 | 1.2 |
- | jiang peng | 3731 | 1/1 | LF sim test - wh | 3.72 | 16. | 1.43 | 0.2 |
- | jiang peng | 3189 | 1/1 | First test - TH | 3.1 | 16. | 0.59 | 1. |
- | jiang peng | 3299 | 1/1 | Third test - TH | 3.1 | 16. | 0.72 | 0. |
- | FANG MEIYI 🇸🇬 | 3545 | 1/1 | test | 3.1 | 16. | 0.75 | 0. |
- | jiang peng | 3394 | 1/1 | RL-TH-shi | 3.1 | 16. | 0.75 | 0. |
- | FANG MEIYI 🇸🇬 | 3666 | 1/1 | test | 3.1 | 16. | 0.81 | 0. |
- | jiang peng | 3587 | 1/1 | Fourth test - TH | 3.1 | 16. | 0.84 | 4.6 |
- | jiang peng | 3285 | 1/1 | Third test - TH | 3.1 | 16. | 0.97 | 3.4 |
- | jiang peng | 3272 | 1/1 | First test - TH | 3.1 | 16. | 1.01 | 0.8 |
3 | Anastasiya Nikolskaya 🇷🇺 | 3338 | 1/1 | JetBrains Research | 3.1 | 16. | 1.08 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3190 | 1/1 | NN Solution | 3.1 | 16. | 1.11 | 1.8 |
- | Anastasiya Nikolskaya 🇷🇺 | 3194 | 1/1 | NN Solution | 3.1 | 16. | 1.17 | 1.6 |
4 | Bea Baselines 🐤 | 3503 | 1/1 | Baseline-IL-logs-tensorflow | 3.1 | 16. | 1.18 | 1.4 |
5 | Rami Al-Naim 🇷🇺 | 3126 | 1/1 | [0.27;0.75] | 3.1 | 16. | 1.19 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3192 | 1/1 | NN Solution | 3.1 | 16. | 1.19 | 2. |
- | Anastasiya Nikolskaya 🇷🇺 | 3209 | 1/1 | SSegm | 3.1 | 16. | 1.2 | 2.2 |
6 | Artem Ioselevskii | 3239 | 1/1 | SSegm | 3.1 | 16. | 1.23 | 1.8 |
- | Anastasiya Nikolskaya 🇷🇺 | 3199 | 1/1 | NN Solution | 3.1 | 16. | 1.25 | 1.6 |
- | jiang peng | 3274 | 1/1 | Second test - TH | 3.1 | 16. | 1.26 | 2.4 |
- | jiang peng | 3270 | 1/1 | First test - TH | 3.1 | 16. | 1.31 | 0.4 |
- | Anastasiya Nikolskaya 🇷🇺 | 3448 | 1/1 | JetBrains Research | 3.1 | 14. | 0.95 | 0.6 |
- | FANG MEIYI 🇸🇬 | 2601 | 1/1 | baseline-IL-logs-tensorflow | 2.48 | 16. | 0.39 | 1.4 |
- | jiang peng | 3588 | 1/1 | RL - TH -xyz | 2.48 | 16. | 0.57 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3744 | 1/1 | colcnt fix | 2.48 | 16. | 0.75 | 0. |
- | FANG MEIYI 🇸🇬 | 3336 | 1/1 | test | 2.48 | 16. | 0.75 | 0. |
- | Rami Al-Naim 🇷🇺 | 3122 | 1/1 | v_d_1_K_t_-0.75_no_cache | 2.48 | 16. | 0.8 | 0.2 |
- | jiang peng | 3406 | 1/1 | RL-TH-shi | 2.48 | 16. | 0.84 | 0.8 |
- | jiang peng | 3331 | 1/1 | RL-TH-shi | 2.48 | 16. | 0.9 | 1.6 |
7 | Peter Almasi 🇭🇺 | 3114 | 1/1 | Baseline solution using reinforcement learning | 2.48 | 16. | 0.95 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3712 | 1/1 | Submission 01 | 2.48 | 16. | 0.98 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3591 | 1/1 | Submission 01 | 2.48 | 16. | 1. | 0.2 |
- | Anastasiya Nikolskaya 🇷🇺 | 3256 | 1/1 | JetBrains Research | 2.48 | 16. | 1. | 0.2 |
- | Anastasiya Nikolskaya 🇷🇺 | 3191 | 1/1 | NN Solution | 2.48 | 16. | 1.04 | 1.8 |
- | FANG MEIYI 🇸🇬 | 3651 | 1/1 | test | 2.48 | 14. | 0.69 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3258 | 1/1 | JetBrains Research | 2.48 | 14. | 0.76 | 0.2 |
- | Anastasiya Nikolskaya 🇷🇺 | 3196 | 1/1 | NN Solution | 2.48 | 14. | 0.9 | 2.2 |
- | Rami Al-Naim 🇷🇺 | 3123 | 1/1 | [1;0.6] | 2.48 | 14. | 1.07 | 0. |
- | Peter Almasi 🇭🇺 | 3533 | 1/1 | Baseline solution using reinforcement learning | 2.48 | 12. | 0.73 | 0.4 |
- | jiang peng | 3389 | 1/1 | RL-TH-shi | 1.86 | 16. | 0.25 | 0.4 |
- | jiang peng | 3395 | 1/1 | RL-TH-shi | 1.86 | 16. | 0.62 | 1. |
- | Anastasiya Nikolskaya 🇷🇺 | 3711 | 1/1 | submission-val-icra2019 | 1.86 | 16. | 0.95 | 0. |
- | jiang peng | 3320 | 1/1 | RL - TH - shi | 1.86 | 16. | 0.96 | 2. |
8 | Tony Hsu 🇹🇼 | 3669 | 1/1 | Baseline solution using reinforcement learning | 1.86 | 16. | 1.31 | 2. |
- | Tony Hsu 🇹🇼 | 3648 | 1/1 | Baseline solution using reinforcement learning | 1.86 | 16. | 1.31 | 2. |
- | Tony Hsu 🇹🇼 | 3621 | 1/1 | Baseline solution using reinforcement learning | 1.86 | 16. | 1.31 | 2. |
- | jiang peng | 3390 | 1/1 | RL-TH-shi | 1.86 | 14. | 0.26 | 1.8 |
- | jiang peng | 3656 | 1/1 | RL-TH-xyz | 1.86 | 14. | 0.4 | 1. |
9 | Angel Woo 🇭🇰 | 3634 | 1/1 | Baseline-IL-logs-tensorflow | 1.86 | 14. | 0.55 | 0. |
- | Rami Al-Naim 🇷🇺 | 3121 | 1/1 | v_d_1_K_t_-0.75_no_cache | 1.86 | 14. | 0.89 | 0. |
- | Rami Al-Naim 🇷🇺 | 3128 | 1/1 | modified configs | 1.86 | 12. | 0.89 | 0. |
- | jiang peng | 3301 | 1/1 | Second test - TH-jp | 1.86 | 12. | 0.89 | 0.2 |
- | Rami Al-Naim 🇷🇺 | 3127 | 1/1 | [0.27;0.6] | 1.86 | 12. | 0.96 | 1.2 |
- | Artem Ioselevskii | 3304 | 1/1 | SSegm | 1.86 | 10. | 0.7 | 0.8 |
- | Anastasiya Nikolskaya 🇷🇺 | 3337 | 1/1 | JetBrains Research | 1.86 | 10. | 0.71 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3236 | 1/1 | JetBrains Research | 1.86 | 10. | 0.74 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3187 | 1/1 | Classic Solution | 1.86 | 10. | 0.74 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3186 | 1/1 | Classic Solution | 1.86 | 10. | 0.79 | 0.4 |
- | jiang peng | 3661 | 1/1 | RL-TH-xyz | 1.86 | 8. | 0.25 | 1.8 |
- | Artem Ioselevskii | 3288 | 1/1 | SSegm | 1.86 | 8. | 0.41 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3198 | 1/1 | NN Solution | 1.86 | 8. | 0.48 | 1.2 |
- | Peter Almasi 🇭🇺 | 3538 | 1/1 | My solution using reinforcement learning | 1.86 | 8. | 0.52 | 0. |
- | Artem Ioselevskii | 3307 | 1/1 | SSegm | 1.86 | 8. | 0.53 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3200 | 1/1 | NN Solution | 1.86 | 8. | 0.53 | 0.4 |
- | Artem Ioselevskii | 3305 | 1/1 | SSegm | 1.86 | 8. | 0.63 | 1. |
- | Rami Al-Naim 🇷🇺 | 3125 | 1/1 | [0.35;0.75] | 1.86 | 8. | 0.72 | 0. |
- | Tony Hsu 🇹🇼 | 3678 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 16. | 0.14 | 7.4 |
- | jiang peng | 3244 | 1/1 | Second test - TH-jp | 1.24 | 16. | 0.38 | 0. |
- | Tony Hsu 🇹🇼 | 3709 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 16. | 0.45 | 7.4 |
- | Tony Hsu 🇹🇼 | 3704 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 16. | 0.45 | 7.4 |
- | Tony Hsu 🇹🇼 | 3686 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 16. | 0.45 | 7.4 |
- | Tony Hsu 🇹🇼 | 3676 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 16. | 0.45 | 7.4 |
- | jiang peng | 3713 | 1/1 | final test - wh | 1.24 | 16. | 0.59 | 0. |
- | Tony Hsu 🇹🇼 | 3671 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 16. | 0.62 | 0. |
- | Tony Hsu 🇹🇼 | 3659 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 16. | 0.62 | 0. |
- | Tony Hsu 🇹🇼 | 3623 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 16. | 0.62 | 0. |
- | Julian Zilly | 2974 | 1/1 | Baseline-IL-logs-tensorflow | 1.24 | 16. | 0.67 | 0. |
- | jiang peng | 3235 | 1/1 | RL - TH -hhk | 1.24 | 16. | 0.69 | 1.4 |
- | jiang peng | 3516 | 1/1 | Second test - TH-jp | 1.24 | 16. | 0.84 | 0. |
- | Rami Al-Naim 🇷🇺 | 3118 | 1/1 | v_d_3/2 | 1.24 | 16. | 0.85 | 0. |
- | jiang peng | 3321 | 1/1 | Third test - TH | 1.24 | 16. | 0.94 | 1. |
- | jiang peng | 3380 | 1/1 | RL-TH-shi | 1.24 | 16. | 1.26 | 0. |
- | jiang peng | 3520 | 1/1 | Second test - TH-jp | 1.24 | 14. | 0.49 | 0. |
- | Angel Woo 🇭🇰 | 3098 | 1/1 | Baseline-IL-logs-tensorflow | 1.24 | 14. | 0.58 | 0. |
- | Angel Woo 🇭🇰 | 3095 | 1/1 | Baseline-IL-logs-tensorflow | 1.24 | 14. | 0.58 | 0. |
- | Angel Woo 🇭🇰 | 3065 | 1/1 | Baseline-IL-logs-tensorflow | 1.24 | 14. | 0.58 | 0. |
- | jiang peng | 3429 | 1/1 | RL - TH -xyz | 1.24 | 12. | 0.52 | 2.6 |
- | Rami Al-Naim 🇷🇺 | 3120 | 1/1 | v_d_1.75_no_cache | 1.24 | 12. | 0.79 | 0. |
- | jiang peng | 3417 | 1/1 | RL - TH -xyz | 1.24 | 10. | 0.28 | 1.2 |
- | jiang peng | 3589 | 1/1 | RL - TH -xyz | 1.24 | 10. | 0.29 | 1.6 |
- | jiang peng | 3423 | 1/1 | RL - TH -xyz | 1.24 | 10. | 0.31 | 1.4 |
- | Angel Woo 🇭🇰 | 3639 | 1/1 | HKDUCK | 1.24 | 10. | 0.32 | 0.6 |
- | Julian Zilly | 3757 | 1/1 | Baseline-IL-logs-tensorflow | 1.24 | 10. | 0.39 | 1. |
- | Julian Zilly | 2957 | 1/1 | Baseline-IL-logs-tensorflow | 1.24 | 10. | 0.39 | 2.2 |
10 | Andy Ser | 3153 | 1/1 | challenge-aido_LF-baseline-duckietown | 1.24 | 10. | 0.49 | 0.4 |
- | Rami Al-Naim 🇷🇺 | 3119 | 1/1 | v_d_3/2_no_cache | 1.24 | 10. | 0.58 | 0. |
- | Artem Ioselevskii | 3268 | 1/1 | SSegm | 1.24 | 10. | 0.58 | 1.8 |
- | Liam Paull 🇨🇦 | 3483 | 1/1 | duckietown lane following demo | 1.24 | 10. | 0.64 | 0. |
11 | Nicky Eichmann | 3585 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 8. | 0.22 | 0. |
- | Angel Woo 🇭🇰 | 3640 | 1/1 | HKDUCK | 1.24 | 8. | 0.31 | 1. |
- | Andrea Daniele 🇮🇹 | 2877 | 1/1 | minimal_agent (Python 3) | 1.24 | 8. | 0.37 | 0.4 |
- | Liam Paull 🇨🇦 | 2370 | 1/1 | minimal_agent (Python 3) | 1.24 | 8. | 0.37 | 0.4 |
- | jiang peng | 3273 | 1/1 | RL - TH -hhk | 1.24 | 8. | 0.37 | 3. |
12 | Alexander Karavaev | 3130 | 1/1 | test | 1.24 | 8. | 0.43 | 0.2 |
- | Rami Al-Naim 🇷🇺 | 2677 | 1/1 | challenge-aido_LF-baseline-duckietown | 1.24 | 8. | 0.49 | 0.2 |
- | Artem Ioselevskii | 3227 | 1/1 | challenge-aido_LF-baseline-duckietown | 1.24 | 8. | 0.55 | 0.2 |
- | Anastasiya Nikolskaya 🇷🇺 | 3197 | 1/1 | NN Solution | 1.24 | 8. | 0.55 | 1. |
- | Artem Ioselevskii | 3267 | 1/1 | SSegm | 1.24 | 6. | 0.22 | 0.4 |
- | Peter Almasi 🇭🇺 | 3117 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 6. | 0.23 | 0.2 |
- | Manfred Diaz | 3692 | 1/1 | challenge-aido_LF-template-tensorflow | 1.24 | 6. | 0.24 | 1. |
- | Rami Al-Naim 🇷🇺 | 3124 | 1/1 | [0.4;0.7] | 1.24 | 6. | 0.26 | 0.8 |
- | Artem Ioselevskii | 3289 | 1/1 | SSegm | 1.24 | 6. | 0.27 | 0. |
- | Artem Ioselevskii | 3294 | 1/1 | SSegm | 1.24 | 6. | 0.27 | 0.2 |
- | Artem Ioselevskii | 3291 | 1/1 | SSegm | 1.24 | 6. | 0.28 | 0. |
- | Artem Ioselevskii | 3295 | 1/1 | SSegm | 1.24 | 6. | 0.28 | 0.8 |
- | jiang peng | 3241 | 1/1 | Second test - TH | 1.24 | 6. | 0.32 | 0.8 |
- | Artem Ioselevskii | 3293 | 1/1 | SSegm | 1.24 | 6. | 0.34 | 0. |
- | Artem Ioselevskii | 3302 | 1/1 | SSegm | 1.24 | 6. | 0.34 | 0.6 |
- | Anastasiya Nikolskaya 🇷🇺 | 3226 | 1/1 | JetBrains Research | 1.24 | 6. | 0.34 | 0.8 |
- | Artem Ioselevskii | 3297 | 1/1 | SSegm | 1.24 | 6. | 0.35 | 1.8 |
- | Artem Ioselevskii | 3290 | 1/1 | SSegm | 1.24 | 6. | 0.39 | 0. |
- | Artem Ioselevskii | 3306 | 1/1 | SSegm | 1.24 | 6. | 0.39 | 0.8 |
- | Artem Ioselevskii | 3287 | 1/1 | SSegm | 1.24 | 6. | 0.46 | 0. |
- | Rami Al-Naim 🇷🇺 | 3097 | 1/1 | challenge-aido_LF-baseline-duckietown | 1.24 | 6. | 0.46 | 0. |
- | Anastasiya Nikolskaya 🇷🇺 | 3339 | 1/1 | SSegm | 1.24 | 6. | 0.5 | 0. |
13 | Konstantin Chaika | 2691 | 1/1 | challenge-aido_LF-baseline-duckietown | 1.24 | 6. | 0.52 | 1.2 |
- | Anastasiya Nikolskaya 🇷🇺 | 3682 | 1/1 | Submission 01 | 1.24 | 6. | 0.58 | 0.2 |
- | Andrea Censi 🇨🇭 | 3578 | 1/1 | rotation | 1.24 | 4. | 0.1 | 0.2 |
- | Bea Baselines 🐤 | 3517 | 1/1 | Straight max | 1.24 | 4. | 0.1 | 0.2 |
14 | Ashwin Ram 🇸🇬 | 3311 | 1/1 | challenge-aido_LF-template-random | 1.24 | 4. | 0.13 | 0.8 |
- | Liam Paull 🇨🇦 | 2376 | 1/1 | minimal_agent_python2 (Python 2) | 1.24 | 4. | 0.15 | 0.2 |
- | Peter Almasi 🇭🇺 | 2867 | 1/1 | Baseline solution using reinforcement learning | 1.24 | 4. | 0.16 | 0.6 |
- | Nicky Eichmann | 3627 | 1/1 | 1.24 | 4. | 0.17 | 0.8 | |
- | Artem Ioselevskii | 3298 | 1/1 | SSegm | 1.24 | 4. | 0.23 | 0.2 |
- | Anastasiya Nikolskaya 🇷🇺 | 3195 | 1/1 | NN Solution | 1.24 | 4. | 0.24 | 0.8 |
- | Nicky Eichmann | 3577 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 16. | 0.32 | 8. |
- | Angel Woo 🇭🇰 | 3111 | 1/1 | HKU Duckietown Project | 0.62 | 16. | 0.44 | 2.2 |
- | jiang peng | 3723 | 1/1 | Second test -RGB- TH-jp | 0.62 | 16. | 0.45 | 0. |
- | jiang peng | 3310 | 1/1 | RL - TH -hhk | 0.62 | 16. | 0.49 | 0. |
- | Angel Woo 🇭🇰 | 3105 | 1/1 | HKU Duckietown Project | 0.62 | 16. | 0.64 | 5.6 |
- | Angel Woo 🇭🇰 | 3104 | 1/1 | HKU Duckietown Project | 0.62 | 16. | 0.64 | 5.6 |
- | Angel Woo 🇭🇰 | 3100 | 1/1 | HKU Duckietown Project | 0.62 | 16. | 0.65 | 5.6 |
- | Angel Woo 🇭🇰 | 3102 | 1/1 | HKU Duckietown Project | 0.62 | 16. | 0.66 | 5.6 |
- | jiang peng | 3247 | 1/1 | Second test - TH-jp | 0.62 | 12. | 0.57 | 0.2 |
- | Angel Woo 🇭🇰 | 3156 | 1/1 | After debugging | 0.62 | 10. | 0.21 | 4.2 |
- | Angel Woo 🇭🇰 | 3638 | 1/1 | HKDUCK | 0.62 | 10. | 0.42 | 3.4 |
- | Alexander Karavaev | 2857 | 1/1 | challenge-aido_LF-baseline-duckietown | 0.62 | 8. | 0.22 | 0.8 |
- | Julian Zilly | 2587 | 1/1 | baseline-IL-logs-tensorflow | 0.62 | 8. | 0.32 | 0. |
- | jiang peng | 3234 | 1/1 | First test - TH -hhk | 0.62 | 8. | 0.39 | 1.8 |
- | Rami Al-Naim 🇷🇺 | 2704 | 1/1 | challenge-aido_LF-baseline-duckietown | 0.62 | 8. | 0.44 | 0.4 |
- | Angel Woo 🇭🇰 | 3113 | 1/1 | HKU Duckietown Project | 0.62 | 6. | 0.22 | 0.2 |
- | Artem Ioselevskii | 3292 | 1/1 | SSegm | 0.62 | 6. | 0.27 | 0.2 |
- | Angel Woo 🇭🇰 | 3162 | 1/1 | Roasted duck | 0.62 | 6. | 0.29 | 0.2 |
- | Angel Woo 🇭🇰 | 3323 | 1/1 | Roasted duck | 0.62 | 6. | 0.3 | 0.2 |
- | Angel Woo 🇭🇰 | 3312 | 1/1 | Roasted duck | 0.62 | 6. | 0.3 | 0.2 |
- | Angel Woo 🇭🇰 | 3313 | 1/1 | Roasted duck | 0.62 | 6. | 0.31 | 0.2 |
- | Angel Woo 🇭🇰 | 3303 | 1/1 | Roasted duck | 0.62 | 6. | 0.31 | 0.2 |
- | Angel Woo 🇭🇰 | 3164 | 1/1 | Roasted duck | 0.62 | 6. | 0.31 | 0.2 |
- | Angel Woo 🇭🇰 | 3158 | 1/1 | Roasted duck | 0.62 | 6. | 0.31 | 0.2 |
- | Peter Almasi 🇭🇺 | 2893 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 6. | 0.35 | 1.4 |
15 | Lei Chen | 3376 | 1/1 | challenge-aido_LF-baseline-duckietown | 0.62 | 6. | 0.39 | 0. |
- | Ashwin Ram 🇸🇬 | 3166 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.1 | 0.4 |
- | Peter Almasi 🇭🇺 | 2871 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 4. | 0.1 | 0.8 |
- | Nicky Eichmann | 3586 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 4. | 0.11 | 1.4 |
- | Nicky Eichmann | 3546 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 4. | 0.12 | 0. |
16 | Isabelle Guyon 🇺🇸 | 3351 | 1/1 | challenge-aido_LF-template-ros - Template solution using ROS | 0.62 | 4. | 0.12 | 0.4 |
- | Bhairav Mehta | 2755 | 1/1 | challenge-aido_LF-template-ros - Template solution using ROS | 0.62 | 4. | 0.12 | 0.4 |
17 | Egor Zamotaev | 2641 | 1/1 | challenge-aido_LF-template-ros - Template solution using ROS | 0.62 | 4. | 0.12 | 0.4 |
- | Konstantin Chaika | 2605 | 1/1 | challenge-aido_LF-template-ros - Template solution using ROS | 0.62 | 4. | 0.12 | 0.4 |
- | Alexander Karavaev | 2561 | 1/1 | challenge-aido_LF-template-ros - Template solution using ROS | 0.62 | 4. | 0.12 | 0.4 |
- | Andrea Censi 🇨🇭 | 2548 | 1/1 | challenge-aido_LF-template-ros - Template solution using ROS | 0.62 | 4. | 0.12 | 0.4 |
- | Nicky Eichmann | 3537 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 4. | 0.13 | 0. |
18 | sebastian glatz | 3747 | 1/1 | 0.62 | 4. | 0.14 | 0.4 | |
19 | Victor Adolfo Romero Cano 🇨🇴 | 3595 | 1/1 | 0.62 | 4. | 0.14 | 0.4 | |
20 | Amaury Camus 🇨🇭 | 3551 | 1/1 | Running the real lane following -- without challenge container | 0.62 | 4. | 0.14 | 0.4 |
- | Amaury Camus 🇨🇭 | 3364 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
21 | Kuan-Lin Chen 🇹🇼 | 3253 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
22 | Shen Yu Chia 🇹🇼 | 3249 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
23 | Brian Chuang | 3248 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
- | Amaury Camus 🇨🇭 | 3213 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
24 | Ian Li 🇹🇼 | 3176 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
25 | Chao-Chun Hsu 🇹🇼 | 3174 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
26 | SIN KIAT LIM 🇲🇾 | 3173 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
27 | Jui-Te Huang 🇹🇼 | 3172 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
28 | Pokai Chang | 3168 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
- | Amaury Camus 🇨🇭 | 3138 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
- | Liam Paull 🇨🇦 | 2819 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
- | Andrea Censi 🇨🇭 | 2805 | 1/1 | challenge-aido_LF-template-random - random_agent | 0.62 | 4. | 0.14 | 0.4 |
- | Andrea Censi 🇨🇭 | 2629 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
- | Andrea Censi 🇨🇭 | 2617 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
- | Liam Paull 🇨🇦 | 2468 | 1/1 | challenge-aido_LF-template-random | 0.62 | 4. | 0.14 | 0.4 |
- | Jacopo Tani | 2440 | 1/1 | random_agent | 0.62 | 4. | 0.14 | 0.4 |
- | Andrea Censi 🇨🇭 | 2417 | 1/1 | random_agent | 0.62 | 4. | 0.14 | 0.4 |
- | Liam Paull 🇨🇦 | 2392 | 1/1 | random_agent | 0.62 | 4. | 0.14 | 0.4 |
- | Andrea Censi 🇨🇭 | 2363 | 1/1 | random_agent | 0.62 | 4. | 0.14 | 0.4 |
- | Artem Ioselevskii | 3300 | 1/1 | SSegm | 0.62 | 4. | 0.18 | 0. |
- | Angel Woo 🇭🇰 | 3579 | 1/1 | Baseline-IL-logs-tensorflow | 0.62 | 4. | 0.2 | 0.2 |
- | Angel Woo 🇭🇰 | 3431 | 1/1 | Baseline-IL-logs-tensorflow | 0.62 | 4. | 0.21 | 0.2 |
- | Angel Woo 🇭🇰 | 3523 | 1/1 | Baseline-IL-logs-tensorflow | 0.62 | 4. | 0.23 | 0.2 |
- | Angel Woo 🇭🇰 | 3377 | 1/1 | Baseline-IL-logs-tensorflow | 0.62 | 4. | 0.24 | 0.2 |
29 | Riza Velioglu 🇩🇪 | 3607 | 1/1 | 0.62 | 2. | 0.05 | 0.6 | |
- | Peter Almasi 🇭🇺 | 2872 | 1/1 | challenge-aido_LF-template-pytorch | 0.62 | 2. | 0.06 | 0.2 |
- | Angel Woo 🇭🇰 | 3155 | 1/1 | After debugging | 0.62 | 2. | 0.07 | 0. |
30 | Eric Lu | 3487 | 1/1 | Testing the RL solution | 0.62 | 2. | 0.07 | 0.4 |
31 | Victor Guerra 🇫🇷 | 2403 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 2. | 0.07 | 0.4 |
- | Ashwin Ram 🇸🇬 | 2909 | 1/1 | challenge-aido_LF-template-random | 0.62 | 2. | 0.08 | 0. |
- | Andrea Censi 🇨🇭 | 2384 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 2. | 0.08 | 0. |
- | Liam Paull 🇨🇦 | 2492 | 1/1 | challenge-aido_LF-template-pytorch | 0.62 | 2. | 0.08 | 0.2 |
- | Bhairav Mehta | 2921 | 1/1 | challenge-aido_LF-template-pytorch | 0.62 | 2. | 0.09 | 0. |
- | Bhairav Mehta | 2945 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 2. | 0.1 | 0. |
- | Nicky Eichmann | 3536 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 2. | 0.11 | 0.6 |
- | jiang peng | 3188 | 1/1 | Baseline solution using reinforcement learning | 0.62 | 2. | 0.11 | 0.8 |
- | Andrea Censi 🇨🇭 | 3055 | 1/1 | rotation | -0. | 16. | 0.45 | 7.8 |
- | jiang peng | 3544 | 1/1 | Fourth test - TH | 0. | 16. | 0.47 | 7.2 |
- | Nicky Eichmann | 3548 | 1/1 | Baseline solution using reinforcement learning | 0. | 16. | 0.5 | 7.2 |
- | Angel Woo 🇭🇰 | 3622 | 1/1 | Baseline-IL-logs-tensorflow | -0. | 16. | 0.64 | 0. |
- | Tony Hsu 🇹🇼 | 3672 | 1/1 | Baseline solution using reinforcement learning | -0.62 | 8. | 0.26 | 2.6 |
- | Nicky Eichmann | 2447 | 1/1 | Baseline solution using reinforcement learning | -1.24 | 16. | 1.41 | 2.6 |