Contents. Hyperformance: Trucks and Neural Nets. Machine Learning Part II 1 August 2017 Robert Berman.

Transcription

1 Contents Hyperformance: Trucks and Neural Nets Machine Learning Part II 1 August 2017 Robert Berman rberman@csir.co.za

2 2016 Logistics Barometer % GDP 88.2% 11.8% 6.3% 3.3% 2.2% Other Logistics Other Road Freight Logistics Fuel Road freight logistics = 5.52% of GDP R223 bn Fuel = 2.21% of GDP R93 bn 2 *2016 Logistics Barometer

3 Road Background Safety- South African Roads Unsafe! Thank you OECD (2011), Moving Freight with Better Trucks: Improving Safety, Productivity and Sustainability, OECD Publishing. 3 3

4 4.3m Heavy Vehicle Regulation Prescriptive Standards Performance-Based Standards Max GCM 56t What the vehicle looks like Easy to enforce Governs mass and dimensions Constrains productivity 4 Constrains innovation What the vehicle can do Governs actual on-road performance Allows heavier and/or larger vehicles Promotes innovation Promotes productivity

5 Baseline vs PBS Vehicles Baseline: 7 Axles, 22 m, 56 tonnes (36 tonne payload) PBS: 9 Axles, 27 m, 73 tonnes (49 tonne payload) 5

6 Background: Static Rollover Threshold Baseline PBS Baseline: 36 tonne payload PBS: 49 tonne payload 6

7 Background: Rearward Amplification Three trailers 7 Baseline vehicle Very poor Rearward Amplification performance (whipping effect on the rearmost trailer causing it to rollover). PBS vehicle Four trailers (higher payload, improved productivity) Meets all performance standards (including Rearward Amplification). A more stable and safer vehicle. *Computer simulations performed by, and animations courtesy of, Wits University

8 PBS in South Africa 8

9 PBS Process in South Africa 9

10 PBS Process in South Africa Expectation is the root of all heartache Shakespeare 10

11 PBS Assessment Challenges Development Designers Regulators Highly Iterative Rules of Thumb 11 No Assessment Tools

12 PBS Assessment Tools Full MDS Exact Performance All Vehicle Configurations Only Accredited Assessors Slow Design Iterations Specialised Software Costly Pro-Forma No Accreditation Required Fast Design Iterations Generic Software Cheap Approximate Performance Specific Vehicle Combinations 12

13 Data Science If you torture the data long enough, it will confess to anything. 13

14 How I Captured the Data B-Doubles are the most common vehicle combination in RSA 14

15 How I Captured the Data Full MDS model of 9-axle B-Double 15

16 Assumptions Tyres 385/65 R /80 R22.5 Dual 16

17 Assumptions Suspension Steel Underslung Air Overslung 17

18 Vehicle Parameters Geometry 18

19 How I Took the Data Prisoner Payload Properties 19

20 Generate Unique Vehicles Uniform Distribution Set upper & lower limits Simulate 35k + 48 Parameters 20

21 Simulation 5 High-Speed Standards Performance Standard 1) Static Rollover Threshold (SRT) Tilt Table Manoeuvre/Test 2) High-Speed Transient Offtracking (HSTO) 3) Rearward Amplification (RA) High-Speed Lane-Change (88 km/h) 4) Tracking Ability on a Straight Path (TASP) High-Speed on Uneven Road (90 km/h) 5) Yaw Damping Coefficient (YD) High-Speed Pulse Steer (100 km/h) 21

22 The Data Vehicle Combinations Vehicle Parameters k SRT (g) HSTO (m) RA - TASP (m) YD - 22

23 The Goal Vehicle Parameters P1 Regression P2 P3 P48 Prediction Model PBS Performance 23

24 Biologically inspired mathematical model of neurons in the brain Complex network of interconnected neurons Each neuron is a simple input-output unit 24

25 Vehicle parameters used in the Input Layer Use vehicle parameters and known PBS result to train the hidden layers 25

26 Very Simple NN Only 1 main Hyperparameter 26

27 Training Data Train Prediction Model Testing Data Test Accuracy 27

28 Hyperparameter Optimisation 28 [1] Brochu, Eric and Cora, Vlad M and De Freitas, Nando, 2010: A Tutorial On Bayesian Optimization Of Expensive Cost Functions, With Application To Active User Modeling And Hierarchical Reinforcement Learning, arxiv preprint arxiv:

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30 Number of parameters Training Data RBFNN Spread NN Hidden Layers Max Absolute Percentage Error (%) Ave Absolute Percentage Error (%) SRT (g) HSTO (m) RA (g) TASP (m) YD (-) [20, 6, 12]

31 Time 0.08 sec to solve Client Meetings 31

32 32