Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Applications of Deep Learning in Telematics

193 views

Published on

Smart phones are equipped with many sensors which provide detailed and continuous information of the device's location and movement. The use of such signals for vehicle movement inference presents many challenges due to signal noise, unknown phone orientation, varying device sensor quality and so on. Signal processing and feature engineering are generally difficult and require deep domain knowledge and manual pattern recognition. We discuss how deep learning can be leveraged in this context for automatic signal processing and feature engineering. We present several applications of deep learning in vehicle telematics as well as the deep learning architecture designed for learning sensor embeddings for vehicle movement events. One challenge we face is that model training requires huge volumes of sensor data, which must be processed efficiently. We present a solution using Spark for model development and batch deployment.

Published in: Data & Analytics
  • Be the first to comment

  • Be the first to like this

Applications of Deep Learning in Telematics

  1. 1. Wayne Zhang, Uber Applications of Deep Learning in Telematics #UnifiedAnalytics #SparkAISummit
  2. 2. We want Uber to be the safest transportation platform on the planet. Safety should be our number-one priority. We have to, as a company, stand for safety.” Dara Khosrowshahi (2018) Stand for Safety
  3. 3. Driving Hours Limit Speed Limit Alert Ride Check Example Driving Safety Products
  4. 4. Telematics 4 Source: Smartphone-based Vehicle Telematics - A Ten-Year Anniversary - Wide availability - Cheap - Short upgrade cycle - Lower quality - Measure phone motion
  5. 5. Sensor Data (Driver Device) ● GPS ○ Absolute location, velocity and time ○ Low frequency (~0.5Hz) ● IMU ○ Relative motion of phone ○ Accelerometer: 3D linear acceleration ○ Gyroscope: 3D angular velocity ○ High frequency (~25Hz)
  6. 6. Motivation ● High-frequency signals ○ Intricate and diverse patterns ○ Dynamic over time Driving WalkingDrivingHandling Phone On Train
  7. 7. Motivation
  8. 8. Sequence Classification Classify whole sequence to certain events: ● Crash ● Driving events (brake, turn, speeding) ● Phone handling ● Rider complaint ● ...
  9. 9. Sequence-Sequence Prediction Input sequence (Phone sensor data) Output sequence Align to other sensor phone => vehicle Pinpoint telematics events (turn, activity) Vehicle sensor Turn event (binary encoding)
  10. 10. Pre-filtering ● High-frequency data result in huge # time steps ○ Pre-filtering: identify specific time window of interest ○ Window segmentation: divide input sequence into small windows Design Choice Window Segmentation window0 window1 window2 windowT
  11. 11. Raw data LSTM Embedding Window Prediction [optional]
  12. 12. Feature Extraction window 1 window 2 window t window T - Time domain stats (min, max, mean, sd) - Frequency domain feature (FFT) 1-D CNNSample Summary Raw data New Feature Vector LSTM LSTM
  13. 13. Data Augmentation ● Sensor readings depend on phone orientation ● Create augmented data by artificially rotating phone ○ New sensor readings ○ Label stays the same
  14. 14. Model - SparkML Transformer - XgBoost - xM trips in training - xM trips in validation - Saved model pipeline Data - Sensor (Driver) Score - Sensor embedding Model Dev Pipeline Data - Sensor (Driver) - Map - Trip - Other Label/Feature - Telematics - Trip Label/Feature - Event definition - Feature Model - Multi-layer LSTM - xM trips in training - Saved protocol buffer Non-DL DL Score - Score and classify
  15. 15. Horovod ● Open source library developed at Uber ● Distributed training for TensorFlow, Keras & PyTorch ● Uses bandwidth-optimal communication protocols & makes use of advanced networking ● Seamlessly installs via pip install horovod
  16. 16. ● Open source library developed at Uber ATG ● Enables deep learning directly from Parquet ● Supports Tensorflow, PyTorch, and PySpark Petastorm Apache Parquet as a dataframe with tensors nd-arrays, scalars (e.g. images, lidar point clouds) Apache Parquet store Fog Horse Hedgehog
  17. 17. Performance DL model DL model
  18. 18. Proprietary and confidential © 2019 Uber Technologies, Inc. All rights reserved. No part of this document may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval systems, without permission in writing from Uber. This document is intended only for the use of the individual or entity to whom it is addressed and contains information that is privileged, confidential or otherwise exempt from disclosure under applicable law. All recipients of this document are notified that the information contained herein includes proprietary and confidential information of Uber, and recipient may not make use of, disseminate, or in any way disclose this document or any of the enclosed information to any person other than employees of addressee to the extent necessary for consultations with authorized personnel of Uber.
  19. 19. Thank You! Wayne Zhang actuaryzhang@uber.com

×