From Black Box to Black Magic, Pycon Ireland 2014
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Machine learning algorithms in automotive field. If you are interested in, I suggest also this presentation: http://www.slideshare.net/bix883/machine-learning-virtual-sensors-automotive-intelligent-tire
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From Black Box to Black Magic, Pycon Ireland 2014
- 1. FROM BLACK BOX TO BLACK MAGIC * :ŸKR ZK
- 2. NQI R ZK ͦŃ4N ŃCI[ Daniele Trainini Lovera Gloria 1
- 3. Automotive Sensor Market Worth $35.78 Billion by 2022 2
- 7. Data gathering Raw Data TEST Data storage Data analysis Features selection Data preprocessing Results analysis Experiments Params calibration Model Selection WORKFLOW DB
- 8. Data analysis Features selection Data preprocessing Fx and Fy as functions of the longitudinal slip “k” and side slip angle β k_slip Fx [N] Fy [N]
- 9. Clean Noisy Data analysis Features selection Data preprocessing • Noisy signals • Quantization errors • Missing data
- 10. Random irrelevant patterns ML Model : “Grey cars are very fast!”
- 11. Random irrelevant patterns ML Model : “ ??? ”
- 12. wheel rad 9,65[inch] 24,5[cm] 205[mph] 330[km/h] CONVENTION DOESN’T EXIST
- 13. BELFAGOR : OUR PREPROCESSING TOOL
- 15. Data analysis Features selection Data preprocessing Curse of dimensionality Samples distinguishibility features nr. Features ranking
- 16. Data analysis Features selection Raw features Engineers features Scikit-Learn Chi2, Variance Threshold, … Wrappers features selection Scikit-Learn ensemble methods, SVM Scikit-Learn metrics Statistical features selection Proprietary algorithms Domain knowledge Data preprocessing
- 17. Data analysis Features selection Data preprocessing Wrappers features selection SVM
- 18. Data analysis Features selection Data preprocessing SVM example: Evaluate speed and steer signals as features subset for Yaw Rate classification ✓
- 19. Data analysis Features selection Data preprocessing SVM example: Evaluate speed and battery current signals as features subset for Yaw Rate classification ✗
- 20. Params calibration Neural Networks Model Selection x1 x2 Neuron/Perceptron w1 w2 Σ | y
- 21. Params calibration Model Selection Neural Networks example: Yaw Rate classification x1 class 0 = yawr -3 class 1 = yawr =-3 h1 h2 x2 h3 y h4 h5 b1 b2
- 22. Params calibration Model Selection Neural Networks example: Yaw Rate classification class 0 = yawr -3 class 1 = yawr =-3 x = class 0 x = class 1 x = correct x = error Labels Predictions
- 24. RESULTS EVALUATION CONFUSION MATRIX Predicted class class = 1 class = 0 class = 1 f11 f10 class = 0 f01 f00 True class Accuracy = # of correct predictions / # of predictions = (f11 + f00) / (f11 + f10 + f01 + f00) Error rate = # of wrong predictions / # of predictions = (f10 + f01) / (f11 + f10 + f01 + f00)
- 25. WHERE WE WERE 24
- 26. DISTORTION “One Tool to rule them all, ! One Tool to find them,! One Tool to bring them all ! and in the BlackBox correlate them” 25
- 27. DISTORTION MAP Adapter Data Uploader Job Manager Worker[s] Algorithms API … 26
- 28. JOB MANAGER 27
- 29. JOB MANAGER 28
- 30. VIEW OPEN TRIGGER WHY ? RELATIONAL PL 29
- 31. WHY PYTHON? • it’s awesome 30
- 32. E M B E D D E D Resources Optimization Processor Specific Tuning Multi-Core Polyedrical Optimization Microprocessors and FPGA Targets ! SW in-the-loop HW in-the-loop 31
- 33. WHAT’S FOR THE FUTURE… • Libraries versions management (e.g. ANACONDA virtual env.) • Data/Results analysis tools • More Design of Experiment • Some technical details: • preemption management • data caching in worker module • Suggestions? 32
- 34. Questions? it.linkedin.com/in/dani84bs/it @Dani84bs it.linkedin.com/pub/gloria-lovera/5b/ 152/4a8/ @LoveraGloria