an ontology-based intelligent speed adaptation system for autonomous cars
DESCRIPTION
Intelligent Speed Adaptation (ISA) is one of the key tech- nologies for Advanced Driver Assistance Systems (ADAS), which aims to reduce car accidents by supporting drivers to comply with the speed limit. Context awareness is indispensable for autonomous cars to perceive driving environment, where the information should be represented in a machine-understandable format. Ontologies can represent knowledge in a format that machines can understand and perform human-like reason- ing. In this paper, we present an ontology-based ISA system that can detect overspeed situations by accessing to the ontology-based Knowl- edge Base (KB). We conducted experiments on a car simulator as well as on real-world data collected with an intelligent car. Sensor data are converted into RDF stream data and we construct SPARQL queries and a C-SPARQL query to access to the Knowledge Base. Experimental re- sults show that the ISA system can promptly detect overspeed situations by accessing to the ontology-based Knowledge Base.TRANSCRIPT
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AN ONTOLOGY-BASED
INTELLIGENT SPEED
ADAPTATION SYSTEM FOR
AUTONOMOUS CARS
Presentation by Lihua ZhaoJIST2014
Lihua Zhao, Toyota Technological Institute, Japan
Ryutaro Ichise, National Institute of Informatics, Japan
Seiichi Mita, Toyota Technological Institute, Japan
Yutaka Sasaki, Toyota Technological Institute, Japan
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Outline
Motivation
Related Work
Approach
Experiment
Conclusion & Future Work
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Avoid overspeed to reduce car accidents.
Advanced Driver Assistance Systems (ADAS)
Intelligent Speed Adaptation (ISA): one of the most cost-efficient
way to improve roadway safety.
Enable autonomous cars to perceive driving
environment.
Ontology-based Knowledge Base
Advanced Digital Map: road information, speed limits, etc.
Traffic Regulations
Motivation3
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Use ontology and 14 SWRL rules to enable the vehicle to
understand the context information when it approaches road
intersections. [Armand, 2014]
Automation level ontology and situation assessment ontology
are designed for co-driving. [Pollard, 2013]
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
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System Flowchart
Knowledge Base Ontologies
Instances
Rules
Query SPARQL Query
C-SPARQL Query
Ontology-Based Intelligent Speed Adaptation
System5
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Input
Sensor Data
PreScan driving simulator
GPS-IMU sensor
Knowledge Base
Ontology-based data
Output
Overspeed warning
System Flowchart6
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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
Ontologies7
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Enable autonomous cars
to perceive driving
environment
to make safe driving decisions.
Knowledge Base
Components
Ontologies
Instances
Rules
Knowledge Base8
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Describe road, intersection, lane, and speed limit. (78)
ObjectProperty (18)
map:isLaneOf
map:isRoadSegmentOf
DatatypeProperty (31)
map:speedMax
map:boundPOS
map:osm_ref
Map Ontology9
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Describe the path of autonomous cars. (34)
ObjectProperty (15)
control:nextPathSegment: intersection or lane
DataProperty (2)
control:pathSegmentID
Control Ontology10
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Concepts of vehicles and devices such as sensors.
(33)
ObjectProperty (3)
car:usedSensor
DataProperty (15)
car:car_length
car:car_ID
Car Ontology11
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Instances are also known as individuals that
model abstract or concrete objects based on the
ontologies.
Tempaku Map Instance
Path Instance
Car Instance
Instances12
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Map instances include
roads, road segments,
intersections, lanes,
schools, etc.
speed limits
enter & exit of lanes
connection of road
segments
Tempaku Map Instance13
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Constructed based on the Tempaku map and control
ontology.
next path segment
Path Instance14
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Describe a car and devices installed on the car.
Car Instance15
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Semantic Web Rule Language (SWRL) is used to
express rules.
Pellet reasoner is used for ontology reasoning.
E.g.: If a car is running on a road near a kindergarten. The speed
limit should be 30km/h near the kindergarten, even though the
default speed limit is 40km/h on the road.<tempaku:Takasaka_Kindergarten, map:nearTo,
tempaku:Hisakata2RS2>
Rules:
Rules16
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SPARQL Query
A powerful RDF query language.
Access to the ontology-based Knowledge Base.
C-SPARQL Query
Access to the RDF stream data.
Format: <subject, property, object, timestamp>
Queries for ISA system17
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Retrieve the next path segment based on
current path segment. (pathSegmentID: 0, 1,
2, …, n)
SPARQL Query I18
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Retrieve the speed limit of current path
segment.
SPARQL Query II19
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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 Query20
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Experiment Settings
Knowledge Base
PreScan Simulator Experiment
Real-World Data Experiment
Experiment21
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Computer Specification
PreScan driving simulator car and a smart
vehicle
Experiment Settings I22
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Trajectory for the experiment (near TTI
campus)
Experiment Settings II
PreScan Map (OpenStreetMap) Google Map
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Knowledge Base for
Experiments24
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Speed: 8 ~ 18 m/s
Smooth acceleration,
deceleration, constant
speed.
SPARQL: 11ms (2 ~
23ms)
Reasoner: 242ms
PreScan Simulator Experiment
Kindergarten
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Drive the smart vehicle
GPS-IMU sensor
SPARQL: 11ms (3 ~
23ms)
Reasoner: 177ms
Real-World Data Experiment
Kindergarten
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Advantages Evaluate the ISA system with the PreScan driving
simulator and a smart vehicle.
Retrieve knowledge from Knowledge Base at real-time.
Effectively detect overspeed situations.
Problems Shifts of GPS positions on PreScan driving simulator.
Delays of data transmission with GPS-IMU sensor.
Discussion27
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Intelligent Speed Adaptation System
Ontology-Based Knowledge Base
SWRL rules for ontology reasoning
SPARQL and C-SPARQL for knowledge
retrieval
ISA system evaluation with PreScan driving
simulator and real-world GPS-IMU sensor
data.
Conclusion28
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Add more knowledge
Traffic light, traffic regulations.
Improve driving safety.
Add links to external resources
Discover hidden knowledge from interlinked
instances.
Future Work29