2018 ncode User Group Meeting

Transcription

1 2018 ncode User Group Meeting February 28 March 1, 2018 Novi, MI USA

2 Analysis of Connected Vehicle Data Dr. Andrew Halfpenny Director of Technology ncode Products

3 Contents 3 1. What are Connected Vehicles 2. The importance of Reliability and Durability 3. How to collect data 4. GlyphWorks analysis 5. Customer examples

4 What are Connected Vehicles 4 From the sales brochure: Smartphone connectivity Mirror screen Where did I leave my car app Route planning from your armchair Electronic handbook Vehicle maintenance Electrical vehicle charge management SOS Assistance Eco navigation The really clever stuff: Advanced vehicle diagnostics Automatic firmware updates Active safety systems Advanced driver assist functions ADAS level 1 automation look, no feet! Highly automated driving functions ADAS level 2 automation look, no hands or feet! More to come: Fully autonomous vehicles ADAS level 4 & 5 automation look, I m reading a book! V2X networked communications

5 Challenges 5 Lots of potentially useful data Acquire, communicate, store and retrieve useful information from these data ADAS levels 1 & 2 represent 100x more data than a traditional vehicle ADAS levels 4 & 5 even more! What do we need it for? Continual development using advanced statistical methods Create synthetic situations to enable decision making Develop simulation models for: Situational environment, Driver response, Vehicle systems response, Reliability target 10 9 Confidence requires >> 10 9 miles of data

6 Vehicle data Data protocols Priorities/Collisions and requests 6 Engine ECU Parameters Transmission ECU Parameters Higher Priority Message Diagnostic Fault Code Other Message Recording Device Time Message Delayed High Priority Requests Additional parameters and information may be requested from an ECU. Ability to request data depends on the recording device capabilities. 6

7 Vehicle data 7 Traditional data Constant sample rate Broadcast Complete data High sample rates Anti aliased Synchronous across channels Simultaneous sample & hold buffer High precision (analogue) Vehicle bus data Non constant sample rate Event driven (burst data) Channel contention Heart beat data Holes/lag due to channel contention Often low sample rates Aliased Asynchronous Often low precision (integer/enumerations) Designed solely for the purpose of vehicle analysis and design Designed primarily for real time control. Analysis and design are often secondary considerations. The designer is a parasite living off data intended for other people

8 The nature of networked data 8 Continuous data are measured using analogue sensors Often expressed as floats Discrete data are counted Often expressed as integers, enumerations or text. Values: Discrete Continuous Engine speed Throttle position Wheel speeds Steering angle Oil pressure Water temperature Gear position Cooling fan speed Turbo waste gate position Cold start switch Window position Oil level Fault raised Window switch position Break light activated Clutch engaged Wiper switch position Indicator switch position Crash switch Time series Time: Time stamped Rule of thumb: Continuous data are measured Discrete data are counted Time series data are sampled regularly Often called Broadcast channels Time stamped data occur at irregular intervals Often called eventdriven channels

9 GlyphWorks 9 Sample rate tolerance CAN data is typically time stamped GlyphWorks does a sample rate check to decide whether to represent the data as Time series or Multi column ASC, BLF and MDF files have a new property called Sample Rate Tolerance. This allows pseudo iso sampled data to be considered as Time series.

10 Implicit data compression 10 Discrete data Only record values as they change. Assume a step change at the new value don t interpolate. Continuous data Noise threshold Record values as they change along with previous point, avoids interpolation errors Interpolation error Continuous data compressed and resampled on the fly Consider also monotonic increasing/decreasing data

11 GlyphWorks 11 Resampling MDF files MDF files that are still considered as Multi Column can be automatically resampled to constant sample rate by using additional preferences:

12 Native Vehicle bus Data Formats 12 IPETronik VECTOR VECTOR VECTOR IMC ETAS Bin, IAD ASC BLF MDF3, MDF4 Raw MDF3 Raw CAN streams can be converted using a DBC file

13 Example GlyphWorks analysis 13 Native support for Multi column data in Stats, Amplitude Distribution and Joint Distribution glyphs: Single channel value counting, Time at Level and amplitude stats. e.g. Engine RPM vs. Time at Level Gear selected vs. Time at Level Multi channel counting, Time at Level and amplitude stats. E.g. Engine RPM vs. torque vs. Time in Gear Gas pedal position vs. gear vs. engine RPM Oil (or Coolant) Temp vs. gear vs. RPM Etc

14 Conclusion 16 Connected cars generate huge data sets ADAS and Autonomous driving requires validation GlyphWorks can analize these data to produce synthetic situations Huge quantities of time series data processed faster than real time Sparse data (time stamped) or iso sampled. Massive analysis capabilities anomaly detection, FFT based algorithms, stats, correlations, counting histograms, fatigue damage, reliability, etc. Accessible and simple to use by engineers (no need to be a data scientist as well!) s ncode s s Data From large quantities of low cost raw data to engineering decisions

15 To develop software for the efficient handling, analysis and display of streamed engineering bus data Dr. Andrew Halfpenny Director of Technology ncode Products s ncode s s Data