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...
Naturalistic Research
on
Powered Two-Wheelers
Martin Winkelbauer (KFV)
Martin Donabauer (KFV)
Alexander Pommer (KFV)
Reinier Jansen (SWOV)
UDRIVE Webinar2017 03 07
Two worlds…
… two populations
2017 03 07 UDRIVE Webinar2
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
Camera
positions
Forward cameras
Feet camera
Face camera
Driver’s action camera
Passengercompertmentcamera
Right blind spot camera
Rear View
Camera Position PTW
2017 03 08UDRIVE Webinar5
Forward cameras
Face camera
Side cameras
Top case
78°
78°
90° 90°
78°
DAS overview
UDRIVE Webinar
Cars TrucksPTW
Cars
Cars TrucksPTW
Cars Trucks
PTW
Cars Trucks
Cars Trucks
Cars TrucksPTW
UDRIVE PTW
Piaggio Liberty 125
2017 03 07 UDRIVE Webinar8
2017 03 08 UDRIVE Webinar9
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
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)
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
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
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?
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
2017 03 07 UDRIVE Webinar15
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
17
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
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
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
)
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
)
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
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
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
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
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
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