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

martin.winkelbauer@kfv.at

www.kfv.at

NR is not

easy, but

worth it