Driver Rating System

1. Driver Rating System Rahul Prakash Mundke Advisors Prof. Kavi Arya Prof. Parmesh Ramanathan Prof. Krithi Ramamritham

2. Outline • Problem Overview • Data Collection • Analysis Techniques • Results • Solution Applicability

3. BEST Overview • Public Sector City Transport 3500+ buses • Service covers around 90% of Mumbai roads • Serves around 4.5 million passengers every day • Average distance traveled per day by a bus is around 217 Km • Daily consumption of around 80-100 liters of diesel per bus • Average mileage of 3.0 KMPL With rising fuel prices, BEST needs a solution which can bring down their expenditure on fuel.

4. Problem • Rating of driver needs to be done based on fuel efficiency criteria • We don’t have access to fuel consumption readings directly as the current (even new) buses have no provision of measuring instant fuel consumption or even daily per driver reading • Relevant input variables using which the fuel consumption needs to be determined, changes with time and are continuous and have dependency with each other • Many hidden variables play their role in determining fuel consumption • New fittings of any kind need to be non intrusive as much as possible with least downtime as per as bus operations are concerned

5. Problem Contd… • Fuel Inefficient driving style consumes 12-15% more fuel • Fuel inefficient driving also means more pollution • Driving style has been discussed in many literature work but no standard definition available • Data to be collected in our case needs to be done in non-intrusive manner • No existing dataset available and collecting data is a costly process in our setup • Model need to be fare and accurate and should in no case declare a fuel efficient driver as fuel inefficient driver • Solution needs to withstand harsh environment inside a vehicle also production cost needs to be under $100 • Solution should be generic enough to adapt to other vehicles

6. Different Road Conditions

7. Driver Model WHAT IS GOOD DRIVING? • Ability to read the road ahead • Use of clutch plate only at right time • Driving maximally at the optimal engine recommendations and reaching this point quickly • Use of brakes only when required • Behavior with the acceleration and brake pedal • Average velocity during trip • Average gear in which the vehicle is drove during trip • Frequency response analysis of acceleration profile • Compliance with the manufacturer recommended Speed vis-à-vis Gear • Shifting higher gear well before higher engine RPMs (< 3500 rpm) • Gear shifting practices with respect to clutch • Switching off ignition at long duration signal stops

8. Driver-Vehicle-Ground System Recording System Ground Conditions Visual and other Inputs Driver Ground Vehicle Performance Accelerator, Brakes Handling Steering System Ride Surface Irregularities Aerodynamic loads Recording System with its Environment

9. Input Variables to System(for the prototype version…) • Visual data using camera • GPS Information (position/velocity of bus) • Engine’s Revolution Per Minute (RPM) • Vehicle Velocity sensor • Lateral and Longitudinal acceleration • Gear position • Clutch, Accelerator Pedal position • Brake Status (On/Off) Final system would need only a subset of these depending on the model requirements.

10. Sampling Rates • Acceleration: accident/crash analysis report suggests a time window of around 250 millisecond for analyzing the deceleration curves. The sampling rate decision comes from this literature. • Velocity: Here the sensor is magnetic pickup. For a max speed of around 80 Kmph (4000 RPM) we would need to sample at least at double the rate hence need a sampling at rate of 66 Hz which is more than required minimum. • GPS: 1 waypoint over a second should be sufficient enough, A GPS generally has the accuracy of 6-10 meter, vehicle traveling at speed of more than36 Km/hr would cover 10 meter per second. At lower speed we would get redundant data.

11. Approach • Analysis done with the synthetic data • The data generation application mimics the driver interaction with the vehicles - A Markov chain with different probabilities • Probability factor for shifting gears during overlap speed • Probability factor mapping aggressiveness in driving with respect to accelerator • Probability factor for interactions with brake • A simpler Vehicle Model for generating outputs based on interaction

12. Simplified System Diagram Analysis Software Database Synthetic Application Recording Software Event Data Recorder Acceleration(2 Axis) Velocity, Engine RPM Pedal Positions Physical System involving Driver and Vehicle

13. Data Generation

14. Few Driving Profiles

15. ACCELERATION Graph(captured using the Custom Data recorder)

16. Problems in sensing… • Poor repeatability of results due to different settings while deploying mechanical sensors • Non linear responses of sensors • Rough conditions, jerks, engine vibrations, high temperature conditions • Different Drivers have different travel time, Needs way to compare them against each other • A bus would be driven by more than one driver in a day needs a way to effectively handle the case • A driving style would change quite often depending on conditions, how do we address this issue i.e. how much data should be considered for analysis • Gear Position reading needs to be inferred by algorithm as gearbox is part of the engine chassis and not vehicle chassis mounting sensors on engine chassis would fail when vehicle takes turn changing the position information of sensor

17. Feature Construction • Time Fraction of total travel time in different gears • Time Fraction of hard braking instances • Time Fraction of violations of recommended Gear Vs. Speed data • Detection of Half-clutch and unnecessary use of clutch plate • STFT analysis of acceleration signals • Average, Mean, Max, standard Deviation etc. values of acceleration, velocity • Strong presence of vertical and lateral acceleration • Time to get into optimal driving conditions • Acceleration Energy per Km • Short Time Fourier Transform coefficients for acceleration • Etc…

18. Breakup of Journey • We brake the total journey into • Cruise Mode • Condition under which the speed of the vehicle is more than 3 Km/h and the acceleration of vehicle is greater than -0.15 m/s2 and less than 0.3 m/s2 • Acceleration Mode • Condition under which vehicle speed is more than 3 Km/h and acceleration is greater than 0.3 m/s2 • De acceleration Mode • Condition under which vehicle speed is more than 3 Km/h and acceleration is less than -0.15 m/s2 • Idle Mode • Condition under which the vehicle speed is less than 3 Km/h Time fraction spent in each mode is important feature. Also depending on the mode one can look for more specific features so as to effectively rate the driver

19. Features

20. Features Contd…

21. Approach I - Regression • The value of a single response variable is assumed to be a function of a set of predictor variables. • These methods are generally classed as regression analysis, consequently data can be partitioned into a 'response' (y) variable and a set of 'predictor' variables (x1 ... xP). We assume that the value of y is some function of x, i.e.in a generalized format y = f(x). There are a range of techniques that are suitable for this type of analysis. They differ in the nature of both y and f(x) and include: • Multiple Regression • Discriminant Analysis • Logistic Regression • Logit models and • General linear models.

22. Discriminant Analysis • Identifying the features which are responsible for splitting a set of observations into two or more groups, If we have information about individual sampling units, obtained from a number of variables, it is reasonable to ask if these variables can be used to define the groups. Discriminant analysis works by combining the variables in such a way that the differences between the predefined groups are maximized. It also provides a classification rule (an equation or discriminant function) that can be used with other cases to predict which group they belong to. • Discriminant analysis can be considered to be a special case of regression analysis where the response variable identifies group membership, for example used and unused habitat. It is possible to use normal regression analysis programs to carry out discriminant analysis. This approach is not been tried on the synthetic data.

23. Logistic Regression • If the dependent variable has only two possible values, for example 0 and 1 (this could be any binary variable, such as gender, in which one value (e.g. male) is assigned 0 and the other is assigned a value of 1), methods such as multiple regression become invalid since predicted values of y would not be constrained within the 0 and 1 limits. Discriminant analysis can be used in such circumstances. However discriminant analysis will only produce optimal solutions if its assumptions are supported by the data. An alternative approach is logistic regression. In logistic regression the dependent variable is the probability that an event will occur, hence y is constrained between 0 and 1. The logistic model is written as: • where z is b0 + b1x1 + b2x2 + ... bpxp

24. Approach II - Classifier • Different classifiers would be used and best of it would be used • One of the important step would be to come up with minimal set of features needed to come up with the model. • Using the Matlab scripts to do the Classification on a test dataset

25. Ensemble Approach Idea • Combine the classifiers to improve the performance • Ensembles of Classifiers • Combine the classification results from different classifiers to produce the final output Different Classifiers • Conduct classification on a same set of class labels • May use different input or have different parameters • May produce different output for a certain  example Learning Different Classifiers • Use different training examples • Use different features

26. Ensemble Approach Performance • Each of the classifiers is not perfect • Complementary • Examples which are not correctly classified by one classifier may be correctly classified by the other classifiers Potential Improvements? • Utilize the complementary property

27. Weka • Weka is a open source platform with collection of machine learning algorithms for data mining tasks. • One can develop a new algorithm and plug into Weka. • Feature Subset Evaluation to reduce the model size is possible using Weka

29. K-Means Clustering on Data Cluster boundaries using two features Classification using the Naïve Bayes on synthetic data give accuracy of 97.18% for the class of type I while the 97.66% for class of type II.

30. Clustering • Many clustering algorithms are available; they differ with respect to the method used to measure similarities (or dissimilarities) and the points between which distances are measured. Thus, although clustering algorithms are objective, there is scope for subjectivity in the selection of an algorithm.

31. Clustering • Polythetic agglomerative • Fusion principle • Nonhierarchical k means or iterative relocation algorithm. • Each case is initially placed in one of k clusters, cases are then moved between clusters if it minimises the differences between cases within a cluster.

32. D(Q,C) Euclidean Distance Metric Given two time series Q = q1…qn and C = c1…cn their Euclidean distance is defined as: C Q

33. Data Reduction • Another way is to reduce the size of the data before applying a learning algorithm (preprocessing) • Some strategies • Dimensionality reduction • Data compression • Numerosity reduction

34. Dimensionality Reduction • Remove irrelevant, weakly relevant, and redundant attributes • Attribute selection • Many methods available • E.g., forward selection, backwards elimination • Often much smaller problem • Often little degeneration in predictive performance or even better performance

35. Numerosity Reduction • Replace data with an alternative, smaller data representation • Histogram 1,1,5,5,5,5,5,8,8,10,10,10,10,12,14,14,14,15,15,15, 15,15,15,18,18,18,18,18,18,18,18,20,20,20,20,20, 20,20,21,21,21,21,25,25,25,25,25,28,28,30,30,30 count 1-10 11-20 21-30

36. Other Numerosity Reduction • Clustering • Data objects (instance) that are in the same cluster can be treated as the same instance • Must use a scalable clustering algorithm • Sampling • Randomly select a subset of the instances to be used

37. Attribute Selection • Before inducing a model we almost always do input engineering • The most useful part of this is attribute selection (also called feature selection) • Select relevant attributes • Remove redundant and/or irrelevant attributes • Why?

38. Reasons for Attribute Selection • Simpler model • More transparent • Easier to interpret • Faster model induction • What about overall time? • Structural knowledge • Knowing which attributes are important may be inherently important to the application • What about the accuracy?

39. Why only STFT works ? Discrete Fourier Transform Would help in finding out the periodicity in the signal Can be quickly computed But suffers on short duration waves Solution is Short Time Fourier Transform But how short this time should be ?

40. STFT of the Acceleration

41. Conclusion • We have devised a driver model with a view on fuel-efficient modes of driving and built hardware that permits us to monitor various parameters (sensor data) which we feel, had a bearing on fuel-efficient driving. • The driver rating solution should be able to cater to a larger class of petrol/fuel driven engines /vehicles and perhaps even hybrid engines /vehicles. We believe that the use of such devices will lead to fuel efficient use of vehicles on roads which will be less polluting with less amount of wear and tear.

42. Applicability of Solution • To improve driver’s driving skills • More Kilometers per liter of fuel • To reduce wear and tear of the vehicle • To increase the time between services • To cut down expenditure cost • To find causes of crash • Mapping of the Traffic bottlenecks of city, real time predictions about bus arrival timings at bus stops • Insurance companies can offer less premiums for the drivers with good driving styles • Transport Organizations and fleet management companies could bring down their operational expenses by checking on driving styles

43. Device Driver Details • For Speed Sensor we get input in PWM format where the Pulse width indicates the speed. We need to measure the pulse width. A quick sampling would do the trick but this needs polling done from the kernel level. • With a speed of 2400 RPM the time taken to complete one Rotation would be 25 mSec , we would need to measure this time precisely calibration can be done later on for now if we assume we are doing a sampling at 1 mSec, could give us possibly a error of 2 mSec. i.e. 8 % worst case • Driver is a Windows Driver Model hence would work on all Windows versions. Has the interrupt handling routine so that polling can be taken away in later development

44. Application Components • A Parallel Port device driver which polls the status line at 1 ms rate to compute the pulse width • A dynamic link library and visual basic application on top of this for recording and displaying data in real time. • A program for feature construction which extracts features from the raw data

45. EDR Systems

46. Deployed System

47. References Book • [1] Usama M.Fayyad, Gregory Piatesky Shapiro,Padhraic Smyth,and Ramasamy Uthrusamy, Advances in Knowledge Discovery and Data Mining, The MIT Press • [2] R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973. Reports and Papers from Conferences and Journals • [3] Drive for Less Fuel, P. Kageson, Report by European Federation for Transport and Environment • [4] Carla M. Silva and Tiago L. Farias, Effects of the Driving Behavior on the Emissions and Fuel Consumption of Vehicles Equipped with Gasoline Internal Combustion Engines. • [5] Haworth, N. and Symmons M, Driving to reduce fuel consumption and improve road safety. • [6] J. S. SA, N. H. Chung, and M. H. Sunwoo, Experimental analysis of driving patterns and fuel economy for passenger cars, International Journal of Automotive Technology, Vol. 4, No. 2, pp. 101-108 (2003) • [7] Shinichiro Horiuchi , An Analytical Approach to the Prediction of Handling Qualities of Vehicles with Advanced Steering Control System Using Multi-Input Driver Model, Transaction of ASME, 2000 • [8] Britt Holm´en and Debbie Niemeier, Characterizing the Effects of Driver Variability on Real-World Vehicle Emissions.

48. [9] C.M. SiIva, T.L. Farias & J.M. C. Mendes-Lopes, EcoGest - Numerical modeling of the dynamic, fuel consumption and tailpipe emissions of vehicles equipped with spark ignition engines, Conference on Urban Transport • [10] YongSeog Kim, W. Nick Street, and Filippo Menczer, Feature Selection in Data Mining, • [11] Nader, J. "Measurement of the impact of driving technique on fuel consumption: Preliminary results", Roads & Transportation, Technical Note TN-172, 1991, pp. 1-6. • [12] Van de Burgwal, H. C., & Gense, N. L., "Interpretation of driving style tips", TNO Report 02.OR.VM.004.1/HVDB. TNO Automotive. 2002. • [13] Robertson, D. I., Winnett, M. A., & Herrod, R. T. "Acceleration signatures", Traffic Engineering and Control 33, 1992, pp. 485-491. • [14] Michael Varat and Stein Husher,Vehicle Impact Response Analysis through the use of Accelerometer data, SAE Technical Paper Series March 2000. Websites and Electronic Data • [15] www.ecodrive.org • [16] http://www.path.berkeley.edu • [17] http://www.tsdm.org/index.html.Thesis and Project Reports • [18] Technical Options for Improving the Fuel Economy of U.S. Cars and Light Trucks by 2010–2015, Report by The Energy Foundation,USA • [19] Jiong Yang Wei Wang Philip S. Yu,Mining, Asynchronous Periodic Patterns in Time Series Data, • [20] Johan Bengtsson, Adaptive Cruise Control and Driver Modeling , MS Thesis report Department of Automatic Control Lund Institute of Technology, 2001

49. Thanks

50. Explain These • Ensemble of Classifiers • Subset of features • Subsets of different classifiers • Regression Approach • If the fuel consumption is known • Explain about the clustering approach • Synthetic Program logic • Device Driver for meeting the timing requirements • Explain each fuel efficient driver criteria in detail • Explain logic of using only optimal subset of feature for analysis • Show graphs of different at least 4 driving patterns • Sampling rate decision • Cruise mode , acceleration mode, de acceleration mode concept