ontologies for advanced driver assistance systems
TRANSCRIPT
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
