SP2 Progress Meeting – Data Fusion September 2007, Paris 1 Infrastructure Side Data Fusion Tobias Schendzielorz Technische Universität München -TUM (Germany) SAFESPOTSAFESPOT

SP2 Progress Meeting – Data Fusion September 2007, Paris 2 Agenda  New Data Fusion Scheme  Data Fusion Components  Further Steps (proposal)  Overview on the Urban Area Activities

SP2 Progress Meeting – Data Fusion September 2007, Paris 3 New Data Fusion Scheme

SP2 Progress Meeting – Data Fusion September 2007, Paris 4  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. New Data Fusion Scheme

SP2 Progress Meeting – Data Fusion September 2007, Paris 5 Data Fusion Components IBEO PTV CSST TUM SODIT TUM MIZAR PTV TUM LCPC

SP2 Progress Meeting – Data Fusion September 2007, Paris 6 Data Fusion Components Data Receiver  The Data Receiver receives and distributions the input from the different data sources: Object Refinement sensor levelpart central level part  The Object 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 level part including the Co-operative Pre-Data Fusion and a central level part including object and map matching as well as object consolidation. Situation Refinement  The fusion and interpretation of environmental data like weather information or aggregated traffic data like traffic density is done within the Situation Refinement. Information Provider  The fused and consolidated data and information is fed via the Information Provider into the LDM. General Components

SP2 Progress Meeting – Data Fusion September 2007, Paris 7 Data Fusion Components Cooperative Pre-Data Fusion (CPDF)  The Cooperative Pre-Data Fusion (CPDF) is an environment perception sub-system, providing information about objects in vicinity of the Laserscanner system. Time Alignment  The Time 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 Level

SP2 Progress Meeting – Data Fusion September 2007, Paris 8 Data Fusion Components Object Matching  At the central level the Object Matching establishes whether data from different sources refers to the same object. Map Matcher  The Map 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. Object Consolidation  The Object 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 Level

SP2 Progress Meeting – Data Fusion September 2007, Paris 9 Data Fusion Components Manoeuvre Estimator  The Manoeuvre 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. Traffic Data Calculator  The Traffic 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. Environmental Event Recognition  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 Recognition. Situation Refinement (i)

SP2 Progress Meeting – Data Fusion September 2007, Paris 10 Data Fusion Components Dynamic Black Spot Recognition  The Dynamic 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. Enhanced Cooperative Automatic Incident Detection (ECAID)  The 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)

SP2 Progress Meeting – Data Fusion September 2007, Paris 11 Data Fusion Components Traffic Event Consolidator  The Traffic Event Consolidator fuses the information on traffic events (e.g. congestion, roadwork) providing consistent list of traffic events. Situation Refinement (iii) Question: 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.

SP2 Progress Meeting – Data Fusion September 2007, Paris 12 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 Activities

SP2 Progress Meeting – Data Fusion September 2007, Paris 13 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

SP2 Progress Meeting – Data Fusion September 2007, Paris 14 CICPS – Road Side Architecture Intersection Road Side Unit Existing Intersection Equipment

SP2 Progress Meeting – Data Fusion September 2007, Paris 15 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. Laserscanner CCTV Camera (road side)

SP2 Progress Meeting – Data Fusion September 2007, Paris 16 Local Dynamic Map  Detailed description of the static and dynamic objects and attributes of the intersection within the Local Dynamic Map. CICPS – Intersection LDM

SP2 Progress Meeting – Data Fusion September 2007, Paris 17 In-Vehicle Display Human Machine Interface  In-Vehicle Human Machine Interface Influence of the Control Status of the Urban Traffic Lights  Road side Influence of the Control Status of the Urban Traffic Lights Road Side HMI CICPS – Alert Systems helpful

SP2 Progress Meeting – Data Fusion September 2007, Paris 18 CICPS – Test Site City of Dortmund  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

SP2 Progress Meeting – Data Fusion September 2007, Paris 19 CICPS – Test Site City of Dortmund