sp2 progress meeting – data fusion 24 - 25 september 2007, paris 1 infrastructure side data fusion...

1SP2 Progress Meeting – Data Fusion24 - 25 September 2007, Paris

Infrastructure Side Data FusionInfrastructure Side Data Fusion

Tobias Schendzielorz

Technische Universität München -TUM (Germany)

[email protected]

SAFESPOTSAFESPOTSAFESPOTSAFESPOT


Agenda

New Data Fusion Scheme

Data Fusion Components

Further Steps (proposal)

Overview on the Urban Area Activities



Sensors / other Sources

Signals Raw Measurements Refined Objects Refined Situations and Events

Vehicles

CCTV_1

WSN Magnetometer

CCTV_2

Traffic Centre

Thermal Imaging

Road RFID

VANET (V2I)

Images

Magnetic Field Variation

Images

Depending on test site

Thermal Frames

Radio Waves

Extended Veh.Data

Vehicle Position

Vehicle Presence

Vehicle Presence

Vehicle Type

Vehicle Speed

Vehicle Presence

Vehicle Presence

Event Information

Vehicle Passage

Vehicle Passage

Vehicle Passage

Vehicle Passage

Vehicle Position

Vehicle Speed

Vehicle Speed

Laser Scanner LS Raw Data

Vehicle Position

PTW position

Pedestrian Position

Vehicle Type

PTW Speed

Vehicle Speed

Vehicle Acceleration

Refined Veh. Speed

Refined Veh. Position / Lane Assigment

Refined Vehicle Type

Dyn. Black Spot

Obstacle Position

Section Avgerage Speed

Section Traffic Density

Section Traffic Flow

Section occupancy

Vehicle Direction

Wrong Direction

Safety Center

Traffic Lights Controller

Predicted Manoeuvre

Signalling Data

Visibility Range

Ice on the Road

Rain Presence

Fog Presence

Rain Intensity Class

Visiblity Intensity Class

Scene Lighting

Comgestion / Incident

Polarisation Direction

Object Thermal Profil

People on the Road

Animal on the Road

WSN Pyrolectric Temperature

Variation

Vehicle Presence

Vehicle Passage

Vehicle Speed

Vehicle Speed

Vehicle Direction

Vehicle Direction


Displays not all the data; missing e.g. detailed vehicle data on events, data from the traffic and the safety centre;

But provides a good overview of the complexity of the SP2 fusion and detection processes.

Definition of Terms (proposal):

- Vehicle Presence: ??

- Vehicle Passage: Vehicle passed a certain point at certain time

- Vehicle Direction: Vehicle going into a certain direction

- Vehicle Count: Number of vehicles passed during a predefined period of time

- Vehicle Position: Absolute position of the vehicle in WGS84 coordinates

There should be a common glossary for Part A, B and C.


Sensors / other Sources

Signals Raw Measurements Refined Objects Refined Situations and Events

Vehicles

CCTV_1

WSN Magnetometer

CCTV_2

Traffic Centre

Thermal Imaging

Road RFID

VANET (V2I)

Images

Magnetic Field Variation

Images

Depending on test site

Thermal Frames

Radio Waves

Extended Veh.Data

Vehicle Position

Vehicle Presence

Vehicle Presence

Vehicle Type

Vehicle Speed

Vehicle Presence

Vehicle Presence

Event Information

Vehicle Passage

Vehicle Passage

Vehicle Passage

Vehicle Passage

Vehicle Position

Vehicle Speed

Vehicle Speed

Laser Scanner LS Raw Data

Vehicle Position

PTW position

Pedestrian Position

Vehicle Type

PTW Speed

Vehicle Speed

Vehicle Acceleration

Refined Veh. Speed

Refined Veh. Position / Lane Assigment

Refined Vehicle Type

Dyn. Black Spot

Obstacle Position

Section Avgerage Speed

Section Traffic Density

Section Traffic Flow

Section occupancy

Vehicle Direction

Wrong Direction

Safety Center

Traffic Lights Controller

Predicted Manoeuvre

Signalling Data

Visibility Range

Ice on the Road

Rain Presence

Fog Presence

Rain Intensity Class

Visiblity Intensity Class

Scene Lighting

Comgestion / Incident

Polarisation Direction

Object Thermal Profil

People on the Road

Animal on the Road

WSN Pyrolectric Temperature

Variation

Vehicle Presence

Vehicle Passage

Vehicle Speed

Vehicle Speed

Vehicle Direction

Vehicle Direction



cmp Processing and Data Fusion with in SP2

Processing and Data Fusion within SP2

Object Refinement

Sensor Level Fusion

Situation Refinement

Data Receiv er

Message Router (VANET in)

LDM

Information Prov ider

Infrastructure Sensor 1 ... N

Laserscanner 1 Laserscanner N

Safety Centre

Traffic Centre

Urban Traffic Lights Controller

Manoeuv re Estimatior

SP5 Applications

Central Level Fusion

Object Matcher

Object Consolidator

Env ironmental Ev ent Recognition

Ev ent Consolidator

Traffic Data Calculator

Dynamic Black Spot Recognition

Env ironmental Consolidator

ECAID

Static Map

Time AlignmentMap Matcher

Cooperativ e Pre-Data Fusion

Results of Data Fusion

IBEO

PTV

CSSTTUM

SODIT

TUM

MIZARMIZAR

PTV

TUM

TUM

LCPC



The Data ReceiverData Receiver receives and distributions the input from the different data sources:

The Object RefinementObject Refinement focuses on the fusion of the object related attributes like the position and speed of a vehicle. This component is divided into a sensor levelsensor level partpart including the Co-operative Pre-Data Fusion and a central level partcentral level part including object and map matching as well as object consolidation.

The fusion and interpretation of environmental data like weather information or aggregated traffic data like traffic density is done within the Situation RefinementSituation Refinement.

The fused and consolidated data and information is fed via the Information ProviderInformation Provider into the LDM.

General ComponentsGeneral Components



The Cooperative Pre-Data Fusion (CPDF)Cooperative Pre-Data Fusion (CPDF) is an environment perception sub-system, providing information about objects in vicinity of the Laserscanner system.

The Time AlignmentTime Alignment is responsible for sorting accounting to the time stamp and provided the data in predefined periods to the Central Level Components. It is assumed that the SAFESPOT system uses the same reference time.

Object Refinement at Sensor LevelObject Refinement at Sensor Level



At the central level the Object MatchingObject Matching establishes whether data from different sources refers to the same object.

The Map MatcherMap Matcher assigns the object to the entities of the static layer of the Local Dynamic Map, e.g. a lane or road segment. In practice, the map matching process consists in comparing the vehicle position and travel direction to the surrounding map network. Road sections are eliminated if they are situated too far from the position of the vehicle or if their heading is too different from the observed travel direction of the vehicle. After the elimination of inappropriate road segments, the map-matching module generally produces a set of positioning candidates.

The Object ConsolidationObject Consolidation is responsible for performing complementary and competitive fusion. The value which is considered most reliable or of the highest quality “wins”. The result of this process is the consolidation of the attributes of a single object.

Object Refinement at Central LevelObject Refinement at Central Level



The Manoeuvre EstimatorManoeuvre Estimator predicts the manoeuvres of approaching vehicles to urban intersections based on the current lane of the vehicle is driving on, the use of the indicator and information coming from the navigation system.

The Traffic Data CalculatorTraffic Data Calculator uses data from infrastructure sensors as well as from probe vehicles in order to compute aggregated traffic data like flow and density on motorways.

Based on data/information from the SAFESPOT vehicles (e.g. windscreen wiper status) it is planed to derive the current environmental situation (e.g. rain) by the Environmental Event Environmental Event RecognitionRecognition.

Situation Refinement (i)Situation Refinement (i)



The Dynamic Black Spot RecognitionDynamic Black Spot Recognition merges and interprets a high amount of static and dynamic data/information (e.g. current weather situation, bad road surface) in order to estimate the current risk level on the road network and thereby to accompany the driver on his way with the best possible safety information.

The Enhanced Cooperative Automatic Incident Detection (ECAID)Enhanced Cooperative Automatic Incident Detection (ECAID) consists of algorithms based on traffic measurements in order to detect a sudden alteration in the traffic flow data; This information can be use within SAFESPOT to flagged an alarm when there is symptomatic evidence of an abrupt traffic disruption, with the aim of preventing from (further) collisions.

Situation Refinement (ii)Situation Refinement (ii)



The TrafficTraffic Event Consolidator Event Consolidator fuses the information on traffic events (e.g. congestion, roadwork) providing consistent list of traffic events.

Situation Refinement (iii)Situation Refinement (iii)

Question: Traffic Event Management ComponentQuestion: Traffic Event Management Component

What is the component’s aim and output?

How does the component fit into the current scheme?

Answer: Is the Traffic Event Consolidator. Calculates in addition the impact on the traffic flow of an event like road works.. Coordination between Incident Detection and Traffic Calculation needed. Responsible SODIT.


Further Steps (proposal)

Development of three kinds of RSUs:For the rural area, motorways and urban area, depending on the respective characteristics.

Identification of common components e.g. map matching

Development of the corresponding architectures

Close cooperation with the partners in the other SPs linked to the corresponding RSU.

Overview on the output of the fusion processes to the LDM.

Overview on the Urban Area ActivitiesOverview on the Urban Area Activities


CICPS – Activities on Intersection Safety

Safe signalized intersection (red light violation) – two phases

Safe signalized intersection (right turning)

Safe signalized intersection (left turning)

Emergency vehicle approaching a controlled intersection

RSU


CICPS – Road Side Architecture

class Domain Objects

Exsiting Intersection Equipment Intersection Roadside Unit

Gateway

CICPS

Message Component

Local Dynamic Map

Data Fusion

Roadside Alert System

Equipped SAFESPOT

v ehicles

Special Sensors (positioning)

Urban Traffic Lights Controller

Existing Roadside Sensors

Traffic Lights

Intersection Road Side UnitExisting Intersection Equipment


CICPS – Sensors helpful

Highly accurate positioning of the vehicles

Additional vehicle data such as speed, acceleration, indicator status,…

Detection of vulnerable road users like cyclist and pedestrians.

Sensor Parameter Cooperative Laserscanner

time stamp

number of objects

vehicle ID

Object type

Object latitude

Object longitude

Object longitudinal speed

Object lateral speed

Object heading (course angle)

Object width

Object length

Object type quality

CCTV – Image_1

Number of moving objects in scene

Position of moving objects (x-coordinate)

Position of moving objects (y-coordinate)

Speed of moving objects (x-coordinate)

Speed of moving objects (y-coordinate)

Background image

Laserscanner

CCTV Camera (road side)


Detailed description of the static and dynamic objects and attributes of the intersection within the Local Dynamic MapLocal Dynamic Map.

CICPS – Intersection LDM


In-Vehicle DisplayIn-Vehicle Display

In-Vehicle Human Machine InterfaceHuman Machine Interface

Road side Influence of the Control Status of the Urban Traffic LightsInfluence of the Control Status of the Urban Traffic Lights

Road Side HMI Road Side HMI

CICPS – Alert Systems helpful


CICPS – Test Site

City of DortmundCity of Dortmund

374

214

60

76

24

61

complex signalized intersections in the very centre of the city of Dortmund have been selected

comprising multiple independently signalized lanes in the approaches and signalized pedestrian or cyclists crossings.

traffic volumes use to be high at the rush hours

http://maps.google.de/


CICPS – Test Site

City of DortmundCity of Dortmund

374

214

60

76

24

61

http://maps.google.de/

sp2 progress meeting – data fusion 24 - 25 september 2007, paris 1 infrastructure side data fusion...

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