Naturalistic Research on Powered Two-Wheelers · 2020. 4. 7. · Piaggio Liberty 125 8 2017 03 07...
Transcript of Naturalistic Research on Powered Two-Wheelers · 2020. 4. 7. · Piaggio Liberty 125 8 2017 03 07...
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Naturalistic Research
on
Powered Two-Wheelers
Martin Winkelbauer (KFV)
Martin Donabauer (KFV)
Alexander Pommer (KFV)
Reinier Jansen (SWOV)
UDRIVE Webinar2017 03 07
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Two worlds…
… two populations
2017 03 07 UDRIVE Webinar2
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Typical Riding Purposes
2017 03 073 UDRIVE Webinar
• 75% leisure riders
• 25% commuters
• Hardly any overlaps
• (Austria, 2012,
n=1038)
Leisure riders
Commuters
Group riding
Travelling
Sport Riding
Track&Offroad
Returning ridersPermanent riders
Returning ridersPermanent riders
Returning ridersPermanent riders
Returning ridersPermanent riders
Returning ridersPermanent riders
Returning ridersPermanent riders
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Camera
positions
Forward cameras
Feet camera
Face camera
Driver’s action camera
Passengercompertmentcamera
Right blind spot camera
Rear View
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Camera Position PTW
2017 03 08UDRIVE Webinar5
Forward cameras
Face camera
Side cameras
Top case
78°
78°
90° 90°
78°
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DAS overview
UDRIVE Webinar
Cars TrucksPTW
Cars
Cars TrucksPTW
Cars Trucks
PTW
Cars Trucks
Cars Trucks
Cars TrucksPTW
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UDRIVE PTW
Piaggio Liberty 125
2017 03 07 UDRIVE Webinar8
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2017 03 08 UDRIVE Webinar9
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Research questions particular in PTW
• Everyday riding– 50 km/h
– right turn, left turn
– Acceleration from stop
– etc
• Safety Critical Event
(SCE) detection– Test triggers
– Validate by video
2017 03 07 UDRIVE Webinar10
• Time Headway– Read endings, 62% drivers
at fault
– Car data only
– Use mobileye
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Preliminary results: Time Headway
• 10% PTW crashes rear ending
• 62% car at fault
• research based on car data
• using Mobileye
• queries direction on SGL database
• avoid traffic jam– v > 30 km/h
– v > 0,5 speed limit
• sidewards distance < 3 m
• lead vehicle present > 10 s
2017 03 0711 UDRIVE Webinar
• 134 mio records i.e. 1242 h
• 22% behind car
• 1.25% behind truck
• 0.07% behind PTW
• for v > 85 km/h distance
detection probably not
exact enough (currently too
few data)
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Frequency of distance, v < 55 km/h
2017 03 07 UDRIVE Webinar12
0 0,5 1 1,5 2 2,5 3 3,5 4
0,00%
1,00%
2,00%
3,00%
4,00%
5,00%
6,00%
7,00%
Distance (s)
Freq
uen
cy (
%)
Bike Car Truck
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Frequency of distance, 55 < v < 85 km/h
2017 03 07 UDRIVE Webinar13
0 0,5 1 1,5 2 2,5 3 3,5 4
0,00%
1,00%
2,00%
3,00%
4,00%
5,00%
6,00%
7,00%
8,00%
9,00%
Distance (s)
Freq
uen
cy (
%)
Bike Car Truck
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Average of distance
2017 03 07 UDRIVE Webinar14
• 1.1 s behind cars
• 1.2 s behind PTWs
• 0.9 s behind trucks
• Explanation for rear endings?
• Back to conspicuity?
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Everyday riding: Setup
• Aim: – To detect, understand, and possibly prevent motorscooter crashes
• Approach:– Descriptives on everyday riding at urban intersections
• Measures:– Speed choice & g-forces
• Depending on:– Scenarios: Flow, Full stop
– Manoeuvres: Left turn, Right turn, Straight ahead
– Driver personalities based on questionnaires
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16
Scenario Speed (km/h) – distribution, %above limit Acceleration (g) – distribution
Pre-stop Pre-man Man Post-man Pre-stop Pre-man Man Post-man
Flow
Left X X X X X X X
Right X X X X X X X
Straight X X X X X X X
Full stop
Left X X X X X X X X
Right X X X X X X X X
Straight X X X X X X X X
Everyday riding: Expected results
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Scenario Speed (km/h) – mean,min,max, %above limit Acceleration (g) – mean,min,max
Full stop Pre-stop Pre-man Man Post-man Pre-stop Pre-man Man Post-man
GenderM X X X X X X X X
F X X X X X X X X
AgeYoung X X X X X X X X
Old X X X X X X X X
ExperienceNovice X X X X X X X X
Exp. X X X X X X X X
Personality
Cat 1 X X X X X X X X
Cat 2 X X X X X X X X
Cat 3 X X X X X X X X
Cat 4 X X X X X X X X
Everyday riding: Expected results
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SCEs: What we're looking for ...
• Recording – Vehicle manoeuvres: e.g. speed, acceleration/deceleration, direction,
high jerk
– Driver/rider behaviour: e.g. eye, head and hand manoeuvres
– External conditions: e.g. road, traffic and weather characteristics
2017 03 07 UDRIVE Webinar18
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Preliminary results: SCEs
• 19 / 40 scooters
• 500 hours of riding data
• Acceleration x / y / x
• Rotation speed x / y / z
• y … longitudinal
• x … lateral
• z … vertical
• corrected by average
• filtered 30 to 2 Hz
• cut off at 55km/h
2017 03 07 UDRIVE Webinar19
map-matched GPS speedoriginal y accelerationfiltered y acceleration
time (s)
acce
lera
tio
n(g
)
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Preliminary results: SCEs
• 19 / 40 scooters
• 500 hours of riding data
• Acceleration x / y / x
• Rotation speed x / y / z
• y … longitudinal
• x … lateral
• z … vertical
• corrected by average
• filtered 30 to 2 Hz
• cut off at 55km/h
2017 03 07 UDRIVE Webinar20
map-matched GPS speedoriginal y accelerationfiltered y acceleration
time (s)
acce
lera
tio
n(g
)
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Distribution of longitudinal acceleration
2017 03 07 UDRIVE Webinar21
-0,9
4
-0,8
6
-0,7
9
-0,7
2
-0,6
7
-0,6
3
-0,5
8
-0,5
3
-0,4
9
-0,4
5
-0,4
1
-0,3
7
-0,3
3
-0,2
9
-0,2
5
-0,2
1
-0,1
7
-0,1
3
-0,0
9
-0,0
5
-0,0
1
0,0
3
0,0
7
0,1
1
0,1
5
0,1
9
0,2
3
0,2
7
0,3
1
0,3
5
0,3
9
0,4
3
0,4
7
0,5
1
0,5
5
0,5
9
0,6
3
0,6
7
0,7
2
0,7
7
0,8
1
0,8
6
0,9
3 1
0
5000
10000
15000
20000
25000
30000
N
Trigger = 0,5 g
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Distribution of lateral acceleration
2017 03 07 UDRIVE Webinar22
-1,7
2-1
,44
-1,3
9-1
,27
-1,1
9-1
,13
-1,0
6-1
,01
-0,9
5-0
,9-0
,85
-0,7
7-0
,71
-0,6
2-0
,55
-0,4
9-0
,42
-0,3
7-0
,32
-0,2
7-0
,22
-0,1
7-0
,12
-0,0
7-0
,02
0,0
30
,08
0,1
30
,18
0,2
30
,28
0,3
30
,38
0,4
30
,48
0,5
40
,60
,65
0,7
10
,77
0,8
20
,89
0,9
61
,01
1,0
61
,15
1,2
31
,31
,42
1,4
9
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
N
Trigger = 0,25 g
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Distribution of vertical acceleration
2017 03 07 UDRIVE Webinar23
-4 -3 -2 -1 0 1 2 3 4
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
N
Trigger = 0,25 g
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Observations
at outliers …
…but nothing
dangerous
obs subjective assessment
87 no reason recognisable
47 in garage
32 curve
29 brake
16 start from traffic light
10 strong brake at zebra (no ped.)
6 gravel road
5 lane change
5 brake for pedestrian on zebra
4 speed hump
4 probably curve (unsecure detection)
4 rough road
3 start (other)
3 strong braking at traffic light
2 start from parking
2 swerve
2 turn
1 start from traffic light and change lane
1 enter parking lot
1 accelerate
1 non-critical interaction with pedestrian on zebra
1 strong braking in congestion
1 brakíng, curve
1 strong braking behind other PTW
1 overtaking bicycle
1 strong braking for parking space
1 strong braking
271 Total2017 03 0724
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Distributions of rotation speed
• 109 cases
• as acceleration ….
• and a lot of roundabouts
• and some u-turns
2017 03 07 UDRIVE Webinar25
x y
z
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3/7/201726
TRA Conference Paris 2014
Martin Winkelbauer, KFV
Phone: +43 5 77077 1214
www.kfv.at
NR is not
easy, but
worth it