Driving Style Analysis based on Trip Segmentation.

A Comparative Multi-Technique ApproachMarco Brambilla, Andrea Mauri, Paolo Mascetti

@marcobrambi

Agenda

IntroProblem DefinitionDatasetData Exploration and PreliminariesTrip Segmentation TechniquesValidation Conclusions

Intro: Relevance1.24 million traffic-related fatalities occur annually world wideCurrently the leading cause of death for people aged between 15 and 29 yearsMajority of cases due to improper or risky driving behavior

Source: World Health Organisation (WHO)

Intro: Driving Process

Driving Process: driving a car is a complex task that requires to take informed decisions based on information pertaining different levels such as his own state and other drivers’ behavior.

Intro: Relevant Information

Vehicle’s Status

Contextual Info• Road State• Weather

Conditions• Traffic Info• Road Risk• Traffic

Problem Statement

Data-driven driver profilingwith respect to driving risk

Essentially: Multivariate Time Series Segmentation

Application scenarios in insurance, promotingpay-how-you-drive (PHYD) business models

State of the Art and Challenges

State of the art: many works on identification and recognition of behavioural patterns (line following, accelerations, braking etc) and maneuversrecognition, behavioural scoring, prediction of driver intentions.

Supervised Learning techniques require intensive end expensive gathering process.

Proposed Solution

Unsupervised techniques to profile drivers behaviour based on identified recurrent patternson driving path segmentationComparison of 3 different approaches and use of all of them for consolidated results

1. Unsupervised Segmentation Based on Clustering2. Unsupervised Segmentation Based on HMM3. Unsupervised Topic Extraction

Contextual Scenes

Observed driving behaviours that are repeated in each driver's behaviour and also across different drivers.A reduced representation of the original Multivariate Time Series conveying a simplified characterizationFurther reasoning is then applied

ETL Process3 Steps:

Extract: read collected files and selection of candidate featuresTransform:

Filter and Grouping Features computation

Load: produce a unique dataset

PreProcessing

Transform

Globaldataset.csv

Load

TripFile.csv

Extract

DatasetsCollection Device : Xsens MTi-G-710 (27 users)And cell phones (10 users)

Retrieved Signals : Acceleration measurementsAltitudeGPS PositioningSpeedingOrientation

Mounted in-vehicle aligned with direction of movement.

No Ground truth knowledge

Features Selected

Acceleration (on Y and X axes),Speed (on Y and X axes) Difference in yaw

Pre-Analysis 1: Data Exploration

Pre-Analysis 1: Data Exploration

Pre-Analysis 2: Application of Driving Safety Existing AnalysesVaiana et.al. Propose a Driving Safety Diagram based on longitudinal and lateral accelerations analysis.

Aggressiveness Index formulation:(A = Aggressive, S = Safe points)

Graphical representation:

DP-Means

1. Unsupervised Segmentation Based on DP-Means Clustering

Problem: Bayesian nonparametric techniques require expensive sampling methods or variational techniques.

DP-means: proposed by Kulis et. al. revisiting k-means: K-means like objective function + penalty

A new cluster is created whenever a point is farther than λ away from every already existing centroid.

Note:Clustering results depends on data ordering.

Clusters

Silhouette

Results

Centroids

Results

Centroids

Distribution of features across clusters

Distribution of features across clusters

Trip Segmentation Examples

Trip Segmentation Examples

Hidden Markov Models

Unsupervised Segmentation based on HMM

Goal: identify latent structure given observed data points, assuming existance of Gaussian hidden states.Assign to each observed point the corresponding hidden state.Hidden Markov Models (HMM):

Observation and hidden states

Markovian propertiesContinous observation

Unsupervised Segmentation based on HMM

Training:Baum-Welch EM algorithm to learn model parameters

Decoding:Viterbi decoding to assign to each observed point the most likely hidden state

HMM Results

Also a different variation applied: inertial HMM: lower transition probabilities enforcing state persistence. Sensible for driving.

HMM Results

Clusters as hidden states.

HMM Results

Clusters as hidden states.

Example of Trip Segmentation

Topic Extraction

Topic Extraction ApproachWhat is topic extraction ?

Model topical concepts belonging to a set of textual documents.Data are described as documents and the components are distributions of terms that reflect recurring patterns, name Topics.

Hierarchical Dirichlet Processes (HDPs)soft-clustering technique based on non-parametric Bayesian theory.number of topics is not set a priori, but learned from data.Posteriori probability approximated by Variational Inference algorithm by Wang et.al.

Results:Most relevant topics for each document and terms distribution in each topic.

Topic Extraction Process

DataQuantization

Documents creation

TopicsExtraction

TopicsEvaluation

Quantization – Binning Processwith static binning strategy

Documents

Terms Relevance on Top 7 Topics

Linguist…

Terms Relevance on Top 7 Topics

… and data analyst perspectives

…

Comparison and Validation

Big Issue: How to Compare?

1) Point-to-point or point distribution2) Resulting grouping of trips3) Perceived user similarity of trips

Solution 1: Point-to-Point

Overlap of clusters? Per trip? Overall?

Solution 1: Point-to-Point

Solution 1: Point-to-Point

Solution 2: Moving from Points to TripsCan we cluster trips based on how observation points have been clustered?

à Simple K-means clustering of trips for each approach. à Comparison of overlap of the different clusters

Coherent with original question: grouping of trips (and thus drivers) by driving behavior

Result of overlap analysis

K-means with K=6 clusters.

DP-means vs. HMM: 74% overlapDP-means vs. Topic: 44% HMM vs. Topic: 48%

Human Validation of Trip Groups

Experts (knowledgeable about driving styles and driving paths recorded) identify possible groups of trips in the dataset

Problem: - Unable to distinguish 6 categories of groups- Only 3 categories are feasible- Best matching 6à3 categories for each method

Results

Conclusions

Three different clustering techniques of driving behavior over trips

-> segmentationClustering of trips based on behavior

-> up to 74% overlap over 6 clusters-> 100% overlap over 3 clusters

User Validation-> 96% precision over 3 clusters

Future Work

About collection process:Gathering process including contextual information (road risk, traffic status, weather conditions)Larger dataset to improve inference performance

About implemented methods:Smarter data ordering for DP-meansRelax independency assumption in HMMImprovements in data discretization process for HDP

Marco Brambilla, @marcobrambi, marco.brambilla@polimi.it

http://datascience.deib.polimi.it

Thanks! Questions?