ontologies for advanced driver assistance systems

ONTOLOGIES FOR ADVANCED

DRIVER ASSISTANCE

SYSTEMS

Presentation by Lihua ZhaoSWO2015

Lihua Zhao, Toyota Technological Institute

Ryutaro Ichise, National Institute of

Informatics

Seiichi Mita, Toyota Technological Institute

Yutaka Sasaki, Toyota Technological Institute

SIG-SWO-035-03

Outline

Motivation

Related Work

Ontology-Based Knowledge Base

Advanced Driver Assistance ADAS Systems (ADAS)

Experiment

Conclusion & Future Work

2

Advanced Driver Assistance Systems (ADAS)

Perceive driving environment by processing sensor data.

Make driving decisions in different traffic situations.

Machine Understandable Ontology-based Knowledge Base

Advanced Digital Map

Road information, speed limits, etc.

Traffic Regulations

Right-of-Way Rules

Motivation3

Automation level ontology and situation assessment ontology are

designed for co-driving. [Pollard, 2013]

Use ontology and 14 SWRL rules to enable the vehicle to understand the

context information when it approaches road intersections. [Armand, 2014]

A complex intersection ontology (car, crossing, road connection, and sign

at crossing) is introduced for fast reasoning. [Hulsen, 2011]

An ontology-based traffic model that can represent typical traffic

scenarios such as intersections, multi-lane roads, opposing traffic, and bi-

directional lanes is introduced. [Regele,2008]

Related Work4

Ontology

Instances

SWRL Rules

SPARQL Queries

C-SPARQL Query

Ontology-Based Knowledge

Base5

Ontology: Machine-understandable knowledge representation

Classes: called as Concepts, defined by owl:Class.

Properties: owl:ObjectProperty and owl:DatatypeProperty.

Instances: individuals of a domain, defined by owl:Thing.

Rules: describe logical inferences, with if-then sentence.

Ontology Editor

Protégé ontology editor

Ontologies6

Describe road, intersection, lane, and speed limit. (78 Classes)

ObjectProperty (18)

map:isLaneOf

map:isRoadSegmentOf

map:turnLeftTo

map:goSraightTo

DatatypeProperty (18)

map:speedMax

map:boundPOS

map:osm_ref (OpenStreetMap Ref)

Map Ontology7

Describe the path of autonomous cars. (34 Classes)

ObjectProperty (15)

control:nextPathSegment(intersection or lane)

control:giveWay

control:collisionWarningWith

control:approachTo

DataProperty (2)

control:pathSegmentID

control:nodePos

Control Ontology8

Concepts of vehicles and devices such as sensors. (33 Classes)

ObjectProperty (3) car:usedSensor

car:isRunningOn

car:currentPath

DataProperty (15) car:car_length

car:car_ID

car:velocity

Car Ontology9

Instances are also known as individuals that model

abstract or concrete objects based on the ontologies.

Tempaku Map Instance

Path Instance

Car Instance

Instances10

Tempaku Map Instance11

Constructed based on the Tempaku map and control ontology.

Path: E -> A -> G

Path Instance12

Describe a car and devices installed on

the car.

Car Instance13

Semantic Web Rule Language (SWRL) is used to express rules.

Pellet reasoner is used for ontology reasoning.

SWRL Rules14

At an intersection, the

car turning right should

give way to the other

car which is going

straight.

Identify driving direction.

Retrieve the next path segment based on current path

segment. (pathSegmentID: 0, 1, 2, …, n)

SPARQL Query I15

Retrieve the speed limit of current path segment.

SPARQL Query II16

If a car’s average velocity in the past 500ms exceeds its

own speed limit. (i.e. maxSpeed:120km/h)

RANGE: duration to receive sensor stream data

STEP: frequency of a sensor receiver.

C-SPARQL Query17

Intelligent Speed Adaptation (ISA) System

Detect overspeed situations.

Intelligent Decision Making System

Make driving decisions at uncontrolled

intersections.

ADAS Systems18

Input

Sensor Data

GPS-IMU sensor

Knowledge Base

Ontology-based data

Output

Overspeed warning

Intelligent Speed Adaptation

System19

Intelligent Decision Making

System20

1. Send sensor data to SPARQL Query

Engine & SWRL Rule Reasoner.

2. Retrieve current lane, next lane, and

driving direction, etc.

3. SWRL rule reasoner adds some

additional information such as

collision warning and the other vehicle's

position, velocity, and driving direction .

Intelligent Decision Making

System21

4. Ontology reasoning on the updated

Knowledge Base.

5. The SPARQL query engine retrieves

the commands and the vehicles that

our vehicle should give way to.

6. The decision signals are sent to the

path planning system to update driving

path or driving behavior.

7. Newly added inferred knowledge is

removed from the ontology-based

Knowledge Base.

Data Format

Evaluation of ISA System

Evaluation of Decision Making System

Experiment22

Data Format23

Sensor data is transmitted through User

Datagram Protocol (UDP) at real time.

Evaluation of ISA System24

●SPARQL Query: 11ms

(3 ~ 23ms)

●Rule Reasoning: 177ms

Overspeed detected near

Takasaka kindergarten.

(speed > 30kmh)

40kmh

Evaluation of Decision Making

System25

Execution time: 99ms (79ms ~ 312ms)

Ontology-Based Knowledge Base

Advanced Driver Assistance Systems (ADAS)

Intelligent Speed Adaptation System

Intelligent Decision Making System

Experiment with real sensor data.

Conclusion26

Speed up execution time

Use part of Knowledge Base for reasoning.

Add more rules to cover other situations

Driving on a corner or on private roads.

Future Work27

Lihua Zhao: lihua@toyota-ti.ac.jp

Thank you !