naturalistic driving studies in support of road safety ... · 1. introduction • south africa has...
TRANSCRIPT
1
Venter, K., Muronga, K., Kemp, M., Sallie, I., De Franca, V., Botha, R., De Saxe, C., Berman, R., Binda, P
(CSIR Smart Mobility Cluster)
De Beer., E (CapaSity) & Sinclair, M (University of Stellenbosch)
NATURALISTIC DRIVING STUDIES IN SUPPORT OF ROAD SAFETY
RESEARCH
Southern African Transport ConferenceSafety & Security Session 9 July 2019, 14:00 -14:30
PRESENTATION OUTLINE
1. Introduction
2 Overview of NDS methodology
3. Problem/challenges
4. Project objectives & finding solutions
5. Conclusion
1. INTRODUCTION
• South Africa has one of the highest road traffic fatality rates in the world
• In 2016, 25/100,000 people lost their lives on SA roads (DoT, 2015/ RTMC, 2016 )
73.4
45.136.2 35 33.7 33.2 32.9 32.4 32.1 32.1 31.6 31.3 31.1 31.1 30 25.1
01020304050607080
Liby
a
Vene
zuel
a
Thai
land
Mal
awi
Libe
ria DRC
Tanz
ania
CAR
Rwan
da Iran
Moz
ambi
que
Buru
ndi
Togo
Soa
Tom
e an
d…
Burk
ina
Faso
Sout
h Af
rica
1 2 3 4 5 6 7 8 9 9 11 12 13 13 15 42
Peop
le p
er 1
00 0
00 k
illed
Country ranking
Road deaths per 100,000 people(Business Tech, 2016)
“Fourteen thousand citizensdie on SA roads (38 per day) and …
an estimated 524 000 persons – based on hospitalisation figures and fatality rates – are annually
injured in road crashes.” Klopper, De Rebus 1 June 2018
http://www.derebus.org.za/the-right-to-road-safety.
UNDoA: Towards a safe system
• SA became signatory to the UNDoA for road safety (May 2011)
• SA pledged to half fatal crashes from 2011 baseline by 2020/ 2030
The SAFE SYSTEM APPROACH forms the basis of the UNDoA strategy
=
Pillar 1
Pillar 2
Pillar 3
Pillar 4
Pillar 5
These pillars are not entities that work in isolation …They are components of the larger road and traffic
management system.
ISO 39001 – RTSMS
2017/18 financial year :
Observation of learner driver behaviour prior to
licensing.
• NDS experiment 1st study (SA) where learner
driver behaviour - observed over a period of
time.
• Large quantity of driving data to be analysed over
time.
Need for local road safety research that informs the
design and implementation of local interventions.
Local research in support of road safety
Internationally, evidence-based research informs the
actions and interventions that are successfully
implemented in countries, aiming for a zero fatality rate.
Traditional vs. new research approachesTr
aditi
onal
met
hods
for
data
col
lect
ion
New
m
etho
dolo
gies
2. OVERVIEW OF NDS METHODOLOGY
Install DAS 12.1.2018 Download data weekly
DAS
Cameras
GPS
On-board logger
Recruit participants
Questionnaires complete and
return
Learner driver assigned number
31 LD25 weeks
53 LD 50 weeks
DAS collects
Qualitative data
Quantitative data
Transcribe Synthesize Analyse Interpret
MaxQDA
Excel Stat
Capture Analyse Interpret
Instrumented vehicle
Test vehicle
• Golf Polo 1.6 litre• Dual controls
• DELTA V South Africa sponsored theDAS for the experiment
• Assisted with modifying the powersource.
• DAS connects to the Controller AreaNetwork (CAN bus) Ethic stipulations -modified to plug into the cigarettelighter to unplug from -no filmingtakes place.
Description of data collected
Driver characteristicsINSTRUMENT TYPE DATA DESCRIPTION
Questionnaire Demographics Age, gender, driving experience; perception of the driving environmentand previous crash involvement.
DAS (cameras) Performancerelated attributes
Visual; hearing; reaction time; field of view
DAS (cameras) Behaviour-relatedattributes
Driver behaviour; seatbelt use; headway selection; impairments;engagement in secondary tasks; percentage eyes closed; eye glare
Road characteristicsINSTRUMENT TYPE DATA DESCRIPTION
DAS (cameras and GPS) Environmentvariables
Weather; light; adjacent land-use; Other drivers’ behaviour; NMTusers’ behaviour
DAS (cameras and GPS) Roadwayvariables
Surface condition, traffic density, kind of locality, relation to thejunction, traffic control and road alignment
Vehicle characteristicsINSTRUMENT TYPE DATA DESCRIPTION
DAS (cameras and on-board computer)
Performanceattributes
Speed, acceleration, deceleration and hard-braking measured in g-force events.
DAS (cameras and GPS) Location attributes
Positioning of vehicle (GPS coordinates; distance travelled)
Datasets
Qualitative data coded and exported
Consolidate DAS & Code data for analysis
Data summary (end 25 weeks)
DATA COLLECTED FOR 6 MONTHS Lessons recorded 162 lessons
Lesson time 287 hoursDistance travelled 4702 km
Driving data collected 202 hours
Distance driven practicing driving 4513 km
Parking data collected 85 hours
For the 2018 study: Only the first and last lesson were analysed
for the 1st 6 months …
3. PROBLEM / CHALLENGES
Analysis to date (mid 2018): • Manual and extremely time consuming
Although this has proven to be successful,the approach is not scalable, and is notsustainable.
Quantitative data - provide additional insightinto driver behaviour and interaction withthe driving environment in various settings.
The fast pace of technology development–potential to further enhance data collection/analysis efforts.
4.1. Data Acquisition System: Explore methodology to analyse and interpret the data as a
standalone data set or data that supports the qualitativefindings.
Explore methodology to integrate different data sets
4.2. New technologies to enhance data collection and analysisefforts Explore possible machine learning techniques to code at least
front facing videos Explore value that alternative technologies for data collection
processes
4. PROJECT OBJECTIVE
4.1. Integration of data sets
2) A desktop application has been developed to:
• Display and integrate the different sets of data
• Consolidate and manage the quantitative
datasets automatically
1) Software code routines were developed to:
• Import the data from the CSV files into a database
• Record the location of the video files to enable the data to be linked with the .csv files.
• Automate the process of reading through the driver/lesson folders and populating the local database with the
data recorded in the CSV files & data included:
– GPS position, altitude, speed and XYZ accelerations recorded at one second intervals
– GPS coordinates were used to create shape files for mapping purposes
3) The desktop application assist to:
• Facilitate navigation through the list of drivers
and their lessons
• Display the relevant data for the selected lesson
Software Application: User interface
Novice driver lesson & demographic information Route information and display(data collected from GPS)
Front facing video
Driver facing video
A data tree where the driver and lesson can be selected
DAS: A data table that shows the data imported from the .csv files
Data grid and map are synchronised with the dual video display
Map shows the lesson route: • position of the vehicle - marker • colour for average speed along
the route
4.2. Machine learning to enhance analysis
a) PROBLEM:• Vehicle video feeds help link sensor data to observed internal and external events:
“The vehicle is at a stop street” or “A pedestrian walked into the street”• Processing the video data was done manually, at a huge labour cost• More versatile data collection tools to be explored
b) OBJECTIVES:• Demonstrate examples of automated video processing to collected data, which could be used
going forward • Proposed demonstration topics:
• Pedestrian detection• Stop sign/ intersection detection
• Explore other data collection solutions for future use
Image processing: proof of concept
• Open source and licenced software solutions being explored (MATLAB, OpenCV)
• Pedestrian detection has been demonstrated using Python + OpenCV on some of the existing
datasets. The algorithm is undergoing tuning to reduce false positives
• Next stop sign & intersection detection will be explored (MATLAB/Open source)
5. CONCLUSION
Project illustrates that it is possible to apply NDS in the South African
context.
Despite challenges experienced:
• NDS is able to provide rich contextual driver behaviour
information in everyday driving situations.
• Comprehensive understanding of behaviour in a number of
different traffic situations.
• It is possible to determine relationships between behaviour
the person and the road environment
• NDS is result-driven and inform the development and
conceptualisation of new research questions and projects for
understanding of road user behaviour.
• Develop machine learning concepts for front and backcamera material
• Expand dashboard to include coding functionalities
• A reporting facility is under development
• Display analysed data in the form of:• data tables• charts
• Redesign coding application in Excel to improveconsistency of coding
What next?
Techniques
What next?
Existing data:
• Analysis of the full 53 ND datasetfor hazard perception development- over time
• Focus on quantitative data toidentify “events of interest”
• Investigation into “near-misses”:• identify factors associated
with potential learner drivercrashes.
• recommendations forinterventions for learnerdrivers
