Development of Autonomous Vehicle

Overview of Autonomous Driving Demonstration in ITS World Congress 2013

Naoki Suganuma

and Yutaro Hayashi

Institute of Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa, Japan

Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa, Japan

Keywords: Autonomous Vehicle, ITS World Congress 2013.

Abstract: Recently, fully automated autonomous vehicles have been developed, and field examinations in public road

have also been conducted, especially in United States. In this paper, preparation of our laboratory toward

field examination of the autonomous vehicle is reported. Additionally, overview of demonstration in the ITS

world Congress 2013 (ITSWC2013) is reported.

1 INTRODUCTION

Recently, there are many researches related to

autonomous vehicle in all over the world

(Montemerlo, 2008), (Broggi, 2006). Especially, in

the United State and EU, some tests on public road

have been conducted and researches related to this

area become very popular.

Unfortunately, in Japan, autonomous vehicle test

in public road have not been permitted, however

some vehicles are demonstrated in some events in

recent years. For example, in Tokyo Motor show

2011, Toyota Motors exhibits autonomous vehicle

named A.V.O.S. (Advanced Vehicle Operation

System). Moreover, in last year, ITS World

Congress, which is one of the biggest conferences in

this field, is conducted at Tokyo Japan, and some

autonomous vehicles were demonstrated.

In this paper, system overview of autonomous

vehicle developed by Kanazawa University is

introduced, and overview of demonstration in the

ITS World Congress 2013 is shown.

2 ONBOARD EQUIPMENT

Figure 1 shows our autonomous vehicle. In this

vehicle, various sensors and actuators for

autonomous navigation are equipped. Therefore, to

drive such equipment, additional sub-battery is

installed in trunk room, and the sub-battery has a

mechanism to be charged by an alternator, which is

exchanged to large power from original one, via

isolator. Moreover, a 100V 1500W inverter system is

also equipped.

(a) Sensors for obstacle detection

(b) Sensors for localization

Figure1: Onboard perception sensors.

GNSS antenna

(Applanix)

GNSS antenna

(Applanix)

LIDAR(SICK)

LIDAR

(SICK)

LIDAR(Velodyne)

RADAR

(Fujitsuten)

LIDAR

(Ibeo)

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Suganuma N. and Hayashi Y..

Development of Autonomous Vehicle - Overview of Autonomous Driving Demonstration in ITS World Congress 2013.

DOI: 10.5220/0005101105450549

In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2014), pages 545-549

ISBN: 978-989-758-040-6

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

In this vehicle, in order to percept under complex

environment like urban road, some sensors are

equipped. In front of the vehicle, a multi-layered

LIDAR (SICK LD-MRS) and millimeter-

wavelength RADAR (Fujitsu-ten) are installed.

These sensors are used to detect distant obstacles.

Moreover, three LIDARs are equipped on roof of the

vehicle. The one of three LIDAR is a high definition

LIDAR (Velodyne HDL64E-S2), and this is used to

detect middle range obstacles. In this LIDAR, 64

laser transmitters are embedded, by rotating 360

degrees around vertical axis, three-dimensional

position of all direction can be given at 10 times per

second. From the LIDAR, since dense three-

dimensional information of 1.3 million point per

second can be given, three-dimensional position of

almost all vehicles, sidewalls, road surface, and so

on can be given up to about 60 meters as shown in

figure 2(a). The rest of two LIDARs(SICK LMS

291-S05) are used to detect the lane markers. They

are positioned in each side of vehicle looking at the

road near the vehicle.

(a) Raw laser point cloud

(b) Example of typical scene percepted by Environment

Perceptor.

Figure 2: Overview of Environment Perceptor.

In addition, to obtain precise vehicle trajectory

for always, GNSS/INS (Applanix POS LV220) is

used. This system consists of Distance Measurement

Unit (DMI), Inertial Measurement Unit (IMU), and

two GNSS receivers, which includes a

GPS/GLONASS azimuth heading measurement

subsystem, and thereby it is possible to obtain

vehicle position at 100Hz by Kalman filtering for

tightly coupled GNSS/INS integration. Therefore, it

is possible to measure vehicle position with accuracy

of 3cm in position, 0.05 degree in attitude when

GPS/GLONASSS signal can be observed, and RTK

correction signal can be given. Additionally, this

system has an accuracy of less than 0.7m after 1km

or 1minute of travel without GPS/GLONASS

signals.

In this vehicle, actuators are installed on steering,

gas pedal, brake, shift and parking brake to realize

autonomous driving. Moreover, to realize natural

driving, horn switch, turn signal and hazard switch

can also be controlled by computer. These actuators

are controlled via motor controller EPOS2

manufactured by MAXSON motor, real-time control

can be achieved via CAN-bus network.

3 SYSTEM OVERVIEW

Figure 3 shows a system overview of our

autonomous vehicle. The software architecture used

in our vehicle is designed as a data driven pipeline,

which individual modules process information

asynchronously. Each module communicates with

other modules via an anonymous publish/subscribe

message passing protocol.

As shown in figure 3, our autonomous vehicle

system is roughly composed of three modules of

Perception, Path planning and Controller modules.

In perception modules, there are three modules of

Lane marker detection, Map matching and

Environment perception. The lane marker module

extracts lane marker on both side of the vehicle by

using side looking LIDARs. From these LIDARs,

distance and reflectivity can be obtained, and lane

marker positions, curvature of the lane marker are

estimated from these measurement. The map

matching module refine pose estimate given from

the GNSS/INS system (Suganuma, 2011), since

typically GNSS/INS system has significant drift

error under urban environment. The rest of

perception module is Environment Perception,

which is main process of these modules and is used

to generate important information to safety driving.

The Environment Perception extracts static obstacles

and estimate motion of dynamic object around the

Ego-vehicle trajectory

Dynamic object and

predicted trajectory

Drivable area (OGM)

Static obstacle (OGM)

Ego-vehicle

Pedestrian

Vehicle

Bicycle

Curb stone

Occlusion

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Figure 3: Overview of system architecture of the autonomous vehicle.

vehicle based on onboard sensors. The main sensor

used in this module is a LIDAR mounted on roof of

the vehicle. Figure 2(a) is an example of point cloud

given from this LIDAR. As mentioned above, dense

three-dimensional information of 1.3 million points

per second can be given. However, even such dense

point cloud can be given; there still exist occluded

region as shown in figure 3(a). Therefore, in our

system, Occupancy Grid Maps (OGM) (Elfes,

1989), which is a method to estimate posterior

probability of existence of obstacle, is adopted.

Since the OGM is a method to extract static

obstacle, we can also extract measurement given

from dynamic obstacle by extracting inconsistency

of OGM. Then extracted measurement belongs to

dynamic obstacle is clustered, and the clustered

objects are tracked using Interacting Multiple

Models (IMM) Filter to estimate its motion (Bar-

Shalom). Figure 2(b) is an example of typical scene

perceived by Environment Perception.



Globalpath

PreviewpointsE

o‐vehicle

(a) Overview of trajectory candidate generation

Globalpath

Obstacle



Obstacle



Oldtrajectory

Newtrajectory

Ego‐vehicle

(b) Connection between new and old trajectory

Figure 4: Overview of local path planning.

The Path planning modules generate feasible

trajectory which the vehicle can be safely navigated

based on information comes from Perception

modules. In Path planning modules, there are two

main modules. The High Level Path Planner module

consists of High Level planer and Middle Level

Planner. The High Level Planner selects optimal

path from digital map like car navigation system.

Middle Level Planner decides driving action like

forward driving, lane change, stop, and so on

depending on the situation based on Finite State

Machine. The Low Level Path Planner module

generates trajectory, which the autonomous vehicle

finally follow. In our implementation, as shown in

figure 4(a), multiple path candidates are generated

simultaneously, the best trajectory is chosen based

on multiple evaluation value such as safety, lateral

offset from global path, and so on. Each trajectory of

these candidates is generated to become minimum

lateral jerk and to be smoothly connected to old

trajectory as shown in figure 4(b).

The Controller modules play an important role to

follow trajectory generated by Path Planning

modules. In these modules, steering, gas pedal,

brake commands are computed. Additionally, horn,

turn signal, hazard, parking brake and gear shifting

command are also generated. Then these commands

are directly sent to the actuator via CAN.

4 DEMONSTRATION IN THE ITS

WORLD CONGRESS 2013

4.1 Implemented Functions

For the realization of intelligent autonomous driving,

it is necessary to implement many functions to path

planner modules. Following are basic functions

implemented before ITS World Congress 2013.

DevelopmentofAutonomousVehicle-OverviewofAutonomousDrivingDemonstrationinITSWorldCongress2013

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 Automatic Route Selection

A function to select optimal route like car navigation

system was implemented in High Level Path Planner

module. The main difference from car navigation

systems is to consider multiple lanes, and optimal

lane is selected in case of multiple lane roads.

 Velocity Keeping

In case of no preceding vehicle conditions, a

function to keep pre-set velocity was implemented in

Low Level Path Planner Module. However, user pre-

set velocity is overwritten in case of some situations,

and more slow velocity is adopted to keep safety.

First situation to overwrite the pre-set velocity is a

case where user pre-set velocity is larger than limit

speed described in digital map. It is clear that we

must to keep limit speed by law. The second

situation is the case where curvature or curvature

rate of the course is significantly large. If the vehicle

was drove at high speed, large lateral acceleration

will be occurred to passenger in case of large

curvature road, and steering wheel will be rotated at

high speed in case of large curvature rate road. To

avoid such dangerous situations, pre-set velocity is

changed to smaller one in such situations. By these

considerations, slowdown before curve is achieved.

 Distance Keeping

In case where a preceding vehicle exists, a function

to keep its inter-vehicle distance was implemented in

Low Level Path Planner module. In case where

preceding vehicle exist on far position, autonomous

vehicle is decelerated, and diminish its inter-vehicle

distance until the distance reaches optimal value.

 Stop at Designated Position

In case when the autonomous vehicle reaches

destination, stop line and so on, a function to stop at

designated position was implemented in Low Level

Path Planner module.

 Obstacle Avoidance

In case where obstacles exist within own lane, a

function to avoid the obstacles is implemented in

Low Level Path Planner module. However, there are

many situations on obstacle avoidance. Therefore

minimum jerk trajectory is selected within drivable

trajectory depending on its situation to become

comfortable driving.

 Overtaking

In case where obviously slow preceding vehicle

exists and there is a passing lane, a function to

overtake slow preceding vehicle was implemented.

This overtaking action is implemented on a Finite

State Machine on Middle Level Path Planner,

achieved by changing optimal lane, suggested by

high level path planner, to passing lane.

(a) Step 1 and 2

(b) Step 3 and 4

(d) Step 6 and 7

Figure 5: Overview of demonstration in the ITS World

Congress 2013 (ITSWC2013).

These basic functions were adequately tested on

a test course, and it was confirmed that it works

reliably up to about 40km/h.

4.2 Demonstration in the ITS World

Congress 2014

The autonomous driving demonstration in ITS

World Congress 2013 was held in Tokyo. As shown

in Figure 5, the demonstration site is a double lane

course, and round trip autonomous driving was

conducted in the course.

The scenario of the demonstration was as

follows:

 Step 1. At first, preceding vehicle was driven at

about 20km/h manually and decelerated to about

10km/h.

 Step 2. The autonomous vehicle keeps inter-

vehicle distance while preceding vehicle was

constant velocity of 20km/h. After detection of

preceding vehicle deceleration, the autonomous

vehicle automatically changes its lane and

Goa

6. Return to original lane

5.Deceleration

6.Overtaking

Preceding vehicle

3. Reacceleration

4.Cancellation of

Start

Preceding vehicle

1. Deceleration

2.Overtaking

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(a) Re-acceleration of preceding vehicle while overtaking

(b) Overtaking

Figure 6: Results of demonstration in the ITS world congress 2013.

conducts an overtaking action.

 Step 3. Preceding vehicle was re-accelerated up

to about 40km/h manually and overtaking action

of autonomous vehicle was blocked.

 Step 4. After reacceleration of preceding vehicle,

autonomous vehicle cancel overtaking action and

return to original lane.

 Step 5. After U-turn of the farthest position of

demonstration course, preceding vehicle was

decelerated to about 10km/h manually.

 Step 6. The autonomous overtake preceding

vehicle and drive in front of the manually

decelerated vehicle.

 Step 7. The autonomous vehicle stops

automatically at manually set destination.

Figure 6 shows some portions of above

demonstration scenario. Through the demonstration,

about 250 passengers including not only specialists

from universities, companies and government, but

also some medias and journalists experienced our

autonomous demonstration. Some comments were

received from participants but there were no

negative opinions, and there were almost good

impressions.

5 CONCLUSIONS

In this paper, system overview of autonomous

vehicle developed by Kanazawa University is

introduced, and overview of demonstration in the

ITS World Congress 2013 is shown. In the future,

we will extend our path planner modules to be able

to use in more complicated situations, and improve

robustness to be able to use in bad weather

conditions for the realization of public road test in

Japan.

ACKNOWLEDGEMENTS

This work was partially supported by Grant-in-Aid

for Scientific Research 24560288.

REFERENCES

M. Montemerlo, et al. 2008. Junior: The Stanford Entry in

the Urban Challenge, In Journal of Field Robotics,

Vol.25, No.9, pp.569-597.

A. Broggi, C.Caraffi, P.P.Prota, P.Zani 2006, The Single

Frame Stereo Vision System for Reliable Obstacle

Detection used during the 2005 DARPA Grand

Challenge on TerraMax, In Proceedings of the 2006

IEEE Intelligent Transportation Systems Conference,

pp.745-752.

N. Suganuma, Uozumi, 2011, Precise Position Estimation

of Autonomous Vehicle Based on Map-Matching, In

Proceedings of the 2011 IEEE Intelligent Vehicles

Symposium.

A. Elfes 1989. Occupancy grids: a probabilistic

framework for robot perception and navigation, In

PhD thesis, Carnegie-Mellon University.

Cox, L. J., and Hingorani, S. L. 1996, An efficient

implementation of Reid's multiple hypotheses tracking

algorithm and its evaluation for the purposes of visual

tracking. In IEEE Transactions on Pattern Analysis

and Machine Intelligence, vol.18, no.2 pp.138-150.

Y. Bar-Shalom, W. D. Blair, Multitarget-Multisensor

Tracking: Applications and Advances, Artech House

Publishers.

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