This document discusses using Python and Spark for time series analysis and visualization of data from self-driving cars. It introduces DaSense, a language and framework for analyzing large sets of sensor data from autonomous vehicles on Spark. DaSense allows users to write Python code for time series analysis without knowledge of parallel programming. It also includes features like data extractors, lazy evaluation, optimized data structures, and the ability to integrate custom algorithms. The document concludes with an example analysis pipeline using DaSense to calibrate the automatic distance control system on a vehicle by analyzing histograms of distances to preceding vehicles.

1. 1NorCom Information Technology AG
Python time series analysis and
visualization for self-driving cars
Pyconweb, Munich, July 1, 2018
Andreas Pawlik

2. 2NorCom Information Technology AG
Outline
‚Autonomous Driving and the Data Tsunami
‚Big Data Technologies: Hadoop, Spark
‚DaSense & DaSense Language for Time Series
‚From Development to Deployment
‚Application: Calibration of Automatic Distance Control

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Autonomous Driving
Image: Newshawktime.com

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Data Tsunami is Coming
Source: Stephan Heinrich (Lucid Motors) presented at “Flash Memory Summit“ [2017]

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Big Data = Hadoop
2003 Google
Distributed File
System Paper MapReduce Paper
2006 Hadoop
is born
from Nutch 2008 Facebook launches Hive
2004 Doug Cutting adds
DFS and MapReduce to Nutch
2009 Yahoo! used Hadoop
to sort one terabyte in 62 seconds
2018
-Innovation
-Operation
-Stability
2010 Spark Paper
2000 2005 2010 2015

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Leverage Big Data Technology for Automotive
- DaSense Technology
- Automotive Formats
- Geo-Distributed Analyses
- Engineer Self Service
- Enterprise Level Implementation
- Tiered Storage
- Security & Access Control
- IT Process Integration
- Open-Source HADOOP Technology:
- Internet-native measurement
data-analysis framework
- Scalable, Cost Effective, Flexible,
Fast Access, Resilient
6

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Ready-To-Go Development Environment Based on Jupyter
Bring your own
libraries!

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DaSense Language - Time Series Analysis in Spark
Data
Logger
‚Python/Spark framework for time series
‚Reduces complexity – program as usual, no knowledge of parallelization required
‚Preserves lazy evaluation – optimize computation graphs
‚Open architecture – combine it with your Python libraries of choice, go Spark native
‚Data Structures optimized for Time Series Analysis
‚Plugin-Structure for existing algorithms

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DaSense Language Concepts – Lazy Evaluation
Build the Expression Tree
locally on the driver
Evaluate it on the data
distributed in the cluster
histogram
where
rpm <
speed 50
Data Extractor
Function
Math
Data Extractor Float
Function
Inspect Result
locally on the driver
DATA

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DaSense Language Concepts – Data Extractors
‚Interface to the actual data
uUnits (km/h, mph)
uChannel name aliases
(e.g., Velocity vs VehSpd)
uResampling/Fusion
‚Assumes data is stored in Apache
Parquet, Big Data conversion routines
for many sensor data formats
‚Data Schema optimized for Time
Series Analysis

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No knowledge of parallel programming needed
DATA
histogram
where
rpm <
speed 50
Data Extractor
Function
Math
Data Extractor Float
Function

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Open architecture – combine it with your libraries of choice
Pandas Dataframe –
proceed as usual
Spark Dataframe –
for experts

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Data Structures optimized for Time Series Analysis
Time Interval List
Time Series Time Interval
Time Series List
Identical API for local
and Spark computing!

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Plugin-structure for parallelizing existing algorithms
Register your
custom function
Select it for execution
in the expression tree

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From Development to Deployment
Interactive Development
Build
DaSense App
PROD
INT
DEV

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Time Interval Lists (TILs) chain Apps into Big Data Workflows
Input: TILs
Output: Modifizierte TILs
Input: TILs
Output: Snippets
Input: TILs
Output: Report (HTML/PDF)

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Application: calibration of automatic distance control
Should the automatic distance control
(ADC) system be more flexible? Does
the vehicle ahead matter to the driver?
When and why will he take over? Lets
have a look!
Compare sensor data to video data:1 2
BRAKE
Get to know the driver!4
Analyze data3
The ADC system works just great – I feel
totally safe and comfortable!
I`d rather turn ADC off. I will brake and
respect the distance myself.

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Analysis pipeline
Search for
transitions in ADC
Check if a next
vehicle exists
Determine class
of vehicle
Determine
histogram:
Distance to
preceding vehicle
Determine
histogram:
Distance to
preceding vehicle
truck
car
Visualize result

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Analysis pipeline
Search for
transitions in ADC
Check if a next
vehicle exists
Determine class
of vehicle
Determine
histogram:
Distance to
preceding vehicle
Determine
histogram:
Distance to
preceding vehicle
truck
car
Visualize result
State transitions: ADC on -> off
Event Search
Driver node
Spark Driver
Worker node
Task
Executor
Task
Worker node
Task
Executor
Task
Task i
Taskj

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Analysis pipeline
Search for
transitions in ADC
Check if a next
vehicle exists
Determine class
of vehicle
Determine
histogram:
Distance to
preceding vehicle
Determine
histogram:
Distance to
preceding vehicle
truck
car
Visualize result
Event Search
State transitions: ADC on -> off

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Search for
transitions in ADC
Check if a next
vehicle exists
Determine class
of vehicle
Determine
histogram:
Distance to
preceding vehicle
Determine
histogram:
Distance to
preceding vehicle
car
Visualize result
Video data: CNN classification
Car Truck
State transitions: ADC on -> off
truck
Analysis pipeline

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Analysis pipeline
Search for
transitions in ADC
Check if a next
vehicle exists
Determine class
of vehicle
Determine
histogram:
Distance to
preceding vehicle
Determine
histogram:
Distance to
preceding vehicle
truck
car
Visualize result
Car Truck
Data
analysis
Car Truck
State transitions: ADC on -> off

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Result
Search for
transitions in ADC
Check if a next
vehicle exists
Determine class
of vehicle
Determine
histogram:
Distance to
preceding vehicle
Determine
histogram:
Distance to
preceding vehicle
truck
car
Visualize result
‚Python/Spark based approach for flexible analysis
‚Makes use of DaSense Language for Time Series
u Easy to write
u Develop and run
‚Big Data Workflow
Distance to next car
truck
car

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Summary
‚DaSense Language
u Python/Spark based approach for
flexible analysis of large sets of
sensor data
https://github.com/Dasense

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Thank you for your
attention!
Andreas Pawlik
apw@norcom.de
NorCom Information Technology AG
Gabelsbergerstraße 4
80333 München